Service Life Prediction and Life Cycle Costs of Light Weight Partitions

Abstract

This study investigates the life expectancy (LE) and life cycle costs (LCC) of three alternatives of interior partitions in residential units: gypsum board, autoclaved concrete block, and hollow concrete block partitions. The aim is to examine the sustainability and cost-effectiveness of these partitions in various service and occupancy conditions. Three different service conditions were analyzed: Standard (constructed without faults), Inherent Defect Conditions (with initial, non-progressing defects), and Failure Conditions (developing defects over time). To analyze the impact of occupancy conditions, six ‘negative occupancy factors’ were identified that accelerate partition deterioration, including non-ownership, poor maintenance, high residential density, the presence of young children, the presence of domestic animals, and the density of furniture. These factors define four occupancy condition categories: light, moderate, standard, and intensive. The research found that hollow concrete block partitions are the most durable, exceeding 100 years in light or moderate conditions. Gypsum board partitions, while cost-effective, have a lower life expectancy, needing replacement in 11–27 years in intensive conditions. Autoclaved concrete blocks offer moderate durability, with similar costs to hollow blocks in normal conditions. Overall, the study highlights the influence of service and occupancy on the lifespan of interior building components, and provides recommendations for partition type selection that are based on specific conditions. These recommendations are a pivotal outcome, highlighting the study’s significant contribution to the understanding of the long-term performance and sustainability of building materials in residential construction.

1. Introduction

Partition walls, which are non-load-bearing interior structures, are essential for dividing spaces within a building’s interior space. Partitions play a critical role in the overall impact of a building’s life cycle. Serving as separate rooms or compartments in residential and commercial settings, these walls enhance space efficiency, privacy, and safety, and are particularly valued for their ability to offer sound insulation and, in certain instances, fire resistance [1]. In recent years, there has been a significant expansion in the use of lightweight partitions in residential construction, as they have numerous advantages over other heavier alternatives, such as hollow concrete blocks or concrete erected on a concrete slab [2,3]: these benefits include speed of installation, timing in the later stages of the building construction process, lower labor (compared to traditional heavy construction methods), flexibility and ease of post-occupancy modification, a reduction in on-site wet processes, and a decrease in waste materials during construction.

It is essential to consider and address the challenges related to the use of gypsum light partitions [4], which include:

• Instability of Mounted Objects: There was a recurrent issue of light objects and accessories hung on gypsum boards falling off, indicating inadequate preparation for load bearing on gypsum ceiling finishes.
• Crack Development: A significant problem observed was the emergence of cracks at the points where partitions meet core building elements, such as ceilings, exterior walls, and around door frames.
• Gaps in Finishes: Noticeable gaps were frequently found in ceiling finishes, compromising aesthetics and structural integrity.
• Electrical Fixture Issues: Loose electrical fixtures were a common problem within the gypsum partitions, posing potential safety hazards.
• Skirting Board Incompatibility: Skirting boards often had shapes not conducive to proper adhesion to gypsum boards, leading to structural weaknesses.
• Moisture Penetration: A significant concern was the wetting of gypsum finishes due to water seeping through the building’s exterior envelope, particularly under windowsills.
• Inadequate Sealants: The sealants used at the junctions between sanitary fixtures and partitions were often not resistant to mold growth, undermining partition longevity and indoor air quality.

Functional defects caused by faulty design and/or construction, such as cracking, water damage, and loose electrical fixtures, were usually discovered early after apartment occupancy and did not progress over time. However, attempts to repair these defects were not always successful. Defects arising from living in the apartment and others related to the service life of building systems (such as disintegration due to plumbing leaks) increased with the duration of residence.

Given the significant role of partition walls in the life cycle of a building and the prevalent issues identified in their use, it is imperative to adopt a systematic approach to improve their design, installation, and maintenance. The challenges encountered highlighted the need for a robust methodology that would extend the life expectancy and enhance the overall performance of building interior partitions.

This paper presents research that aims to predict the service life of interior partition walls and seeks to address common failure mechanisms by undertaking a systematic review and inferential statistical analysis. The presented research focuses on implementing a methodology to estimate the life expectancy of building components, particularly interior residential partitions, and predict their ongoing performance on the basis of a systematic review of their physical and visual condition and failure patterns at a given age. The fundamental component of this methodology is deterioration patterns that reflect a building component’s typical degradation patterns, both standard wear conditions and under the influence of failure mechanisms, with the aim of enabling statistical analysis of the data to determine the component’s predicted service life.

The methodology was developed by comparing common lightweight partitions in residential construction (gypsum board partitions and autoclaved aerated concrete partitions) to conventional hollow concrete block partitions. The results and findings illustrate the methodology’s advantages (and disadvantages), highlighting its potential as an effective tool for maintenance policy management.

The research contributes a statistical estimation of the service life of conventional and lightweight partitions in residential buildings, providing a basis for planning the life cycle of these components, and providing a foundation for ascertaining the life cycle costs of manufacturing processes in residential building construction. The findings also offer estimates for the life cycle planning of interior partitions. This analysis enables an examination of the economic aspect of lightweight partitions in buildings that takes into account the service life of the building, and enables the for quantification of costs that result from typical component failures.

2. Background

The service life expectancy of building components plays a crucial role in ensuring the longevity, sustainability, and cost-effectiveness of constructed facilities. Predicting and understanding the factors influencing component service life facilitates informed design, construction, and maintenance decisions, leading to optimized building performance. Several studies have conducted research in this area and have focused on various aspects of building component durability and deterioration patterns.

Shohet and Paciuk conducted comprehensive research of the service life prediction of exterior cladding components in standard conditions and failure conditions [5], which included monitoring the physical and visual performance of building components. A recent study by Wasserman et al. examines the deterioration patterns and life expectancy of exterior stone claddings, comparing dry-fixed and wet-fixed methods in both standard and marine environments. This study incorporated laboratory tests, field surveys, and probabilistic service life prediction methods, leading to statistical prediction of exterior stone cladding service life [6]. Furthermore, a study by [7] found that different mineral admixtures significantly influence the concrete’s durability and can increase its life cycle expectancy.

Moreover, research by Petersen et al. delved into the impact of maintenance on the service life of painted renderings, providing insight into the effectiveness of different maintenance strategies [8,9]. Additionally, a study by Ferrández-García et al. evaluated ten interior partition wall solutions in Spain by undertaking a comprehensive life cycle and eco-efficiency analysis [10]. Amiri Fard et al. (2021) provided a multi-criteria analysis of insulated concrete wall technologies and wood-frame walls in residential buildings, focusing on optimizing building envelope-based life cycle cost and decreasing environmental impacts [11].

Furthermore, studies have, in engaging with building design and materials, developed critical aspects of life cycle assessment (LCA) and life cycle cost (LCC). Kumar et al. (2020) address the optimization of insulation thickness from a life-cycle cost perspective, focusing on the balance between energy efficiency and cost-effectiveness in building materials [12]. Islam et al. (2014) conduct a thorough life cycle assessment and life cycle cost analysis of a typical Australian house, providing a detailed economic and environmental impact analysis to guide sustainable building design [13]. Stazi et al. (2012) combine life cycle assessment with energy simulation and optimization analysis to reduce building envelope environmental impact and life cycle energy demand [14]. Plebankiewicz et al. (2016) propose a comprehensive life cycle cost model that incorporates risk factors, enhancing the understanding and management of costs throughout the building life cycle [15]. Fathoni et al. (2013) add to this by exploring the interaction between building materials and environmental factors, identifying various environmental zones in Malaysia that can be used to enhance design and maintenance strategies that will improve the durability and service life prediction of building components [16]. These studies emphasize the importance of LCA and LCC, providing valuable insights to sustainable and cost-effective building design and maintenance practices.

The application of AI algorithms, such as Artificial Neural Networks (ANN) and Machine Learning (ML), has been increasingly recognized to enhance understanding and prediction in various engineering domains [17,18,19], and contribute to the prediction of service life expectancy and life cycle assessment costs. Despite this, few studies have integrated AI algorithms with the aim of achieving a more accurate projection of the service life and deterioration of building components, along with their service life of building components. Matos et al. explore the use of deep learning and regression algorithms to automate anomaly detection and predict building degradation, demonstrating the potential of AI to optimize the building of life cycle management [20]. Aisyah et al. discuss combining ANN with ISO 15686 [21] to achieve more reliable forecasting of building deterioration, and focus on the relationship between factors affecting the service life of buildings, the value of building conditions, and the level of degradation. They seek to provide a more accurate prediction of building component age and degradation level, with the aim of contributing to better service life planning and maintenance strategies [22]. Dias et al. introduce a mathematical model, using ANN to predict the service life of painted exterior surfaces by drawing on data from 160 buildings in Lisbon, Portugal. The model, informed by various degradation agents, estimates the mean service life of these surfaces by incorporating detailed factors that account for variations in degradation, and then offers values consistent with existing perceptions of the durability of painted coatings. This model aims to enhance economic and environmental performance and maintenance plans, contributing to more informed decisions in the design and construction stages [23]. And Almeida and De Freitas provide a case study of school buildings that demonstrates how artificial neural networks can be used in conjunction with life cycle cost analysis to optimize building retrofitting strategies [24].

Further extending this discussion, Building Information Modeling (BIM) has emerged as a powerful tool in this realm, whose ability to model structural and non-structural components provides a comprehensive life cycle assessment and cost analysis platform. Its capacity to model material properties and incorporate relevant data presents opportunities to employ it in life cycle assessment and life cycle cost analysis [25,26,27,28,29]. For instance, Santos et al. (2020) use it to explore how LCA and LCC integration can be enhanced, proposing an automatic analysis within a BIM environment and demonstrating its utility through a case study conducted in the Netherlands, with the aim of advancing automatic sustainability simulations and predictive analyses in construction [30]. A study by Rad et al. (2021) focuses on integrating LCC into BIM to optimize the life-cycle costs and resilience of building design, demonstrating that investing in resilience can significantly reduce life-cycle costs and improve building durability, particularly in earthquake-prone areas [31].

In the context of climate change, optimizing the life cycle management of buildings is critical to ensure sustainability and resilience. Loli et al. (2020) discuss the importance of service life prediction for building component design and maintenance, with reference both to the importance of adjusting SLP to climate change, and using the life cycle assessment to analyze the embodied energy in buildings [32]; their findings highlight the need to not only consider the superstructure but also the floor structures in multistorey buildings, for the reason that the latter significantly influences the embodied impact [32]. Further, Broun and Menzies (2011) employ life cycle assessment techniques to evaluate the embodied energy and environmental impacts of clay brick, hollow concrete block, and timber frame partition walls, finding timber stud walls to be the most environmentally friendly, leading them to advocate the reduction of embodied energy in clay brick walls to improve sustainability [33].

The life cycle of building components significantly impacts the performance of critical infrastructures, particularly in extreme events, and can affect design and maintenance strategies. Proper maintenance plays a crucial role in reducing risks and enhancing the resilience and reliability of these structures, both under normal conditions and in extreme events [34]. Several studies have delved into the life cycle management of infrastructure in extreme conditions, including Padgett et al., who emphasize the importance of a risk-based seismic life-cycle cost-benefit analysis for bridge retrofit assessment, which integrates probabilistic seismic hazard models and fragility assessments to optimize retrofit strategies [35]. Alipour et al. and Akiyama et al. researched the life-cycle performance, cost, and reliability of reinforced concrete highway bridges in high seismic areas [36,37]. Welsh-Huggins et al. investigate the life-cycle seismic and environmental performances of buildings by using alternative concretes, linking seismic resilience with sustainability in building materials [38]. Li et al. propose a framework for assessing the life cycle resilience of RC structures in multi-hazards, and consider the effects of corrosion on seismic risks and resilience [39]. These studies underscore the critical need to integrate maintenance, material selection, and hazard assessment into infrastructure life cycle management, with the aim of ensuring resilience and sustainability in the face of extreme events.

This section has provided a thorough overview of various research studies that focus on the service life expectancy and life cycle costs of building components, and has emphasized their importance in helping to ensure sustainability, longevity, and cost-effectiveness in construction. It has covered various topics, ranging from the deterioration and maintenance of exterior claddings to the life cycle assessment of different building materials. The discussion of the integration of advanced technologies like AI and Building Information Modeling (BIM) into these studies has highlighted the significant progress that has been made in predicting service life and managing life cycle costs more efficiently. In addition, the impact of climate change on building materials and methods also emerged as a recurring theme, illustrating the need for the construction need to adopt adaptable and sustainable practices. All of these studies underscore the importance of developing comprehensive approaches to building design and maintenance when seeking to meet the challenges of a changing environment.

3. Methodology

The research methodology assesses the performance of interior residential building components by using a standardized performance scale, identifying failure mechanisms that impact performance and developing deterioration pattern curves to predict the life cycle expectancy in standard and various failure conditions. This approach is based on the methodology presented by [5,6,40], which estimates building component life expectancy, and predicts ongoing performance based on physical and visual conditions while providing a systematic survey of failures at a given age. Deterioration patterns are central to this methodology, as they reflect typical degradation mechanisms, enabling statistical data analysis to determine each component’s predicted life cycle expectancy.

The research focuses on characterizing the wear patterns of common lightweight partitions in residential construction in order to compare gypsum board partitions, gypsum block partitions, autoclaved aerated concrete partitions, and conventional hollow concrete block partitions.

The main stages of the methodology are:

• Systematic review of the physical and visual condition of components at different ages.
• Determination of the actual performance level of the components and the failure mechanisms that appear in them.
• Development of wear patterns for the statistical analysis of various deterioration paths.
• Prediction of component service life expectancy and performance level in standard service conditions.
• Quantification of the impact of failures on the component’s predicted life cycle.

3.1. Review of the Physical and Visual Condition of Components at Different Ages

Determining the deterioration pattern of a building component traditionally involves monitoring the component throughout the building’s lifespan, which is often impractical because of the extended time this required. An alternative method is to observe similar components at different ages, and therefore at varying stages of failure and deterioration. This approach makes it possible to establish typical wear patterns for a specific type of component in defined service conditions.

The research involved conducting systematic field surveys to assess the physical and visual conditions of a large sample of similar partitions in various residential apartments. These surveys employed specialized forms to document both characteristics and any observed defects in each partition (

Table 1. Rating criteria for standard interior apartment partitions.

Phenomenon	5 (100%)	4 (80%)	3 (60%)	2 (40%)	1 (20%)
Deviations from flatness or undulations on the partition surface	No deviation from planarity in all directions.	Deviation from planarity up to 2 mm along 2 m at each measuring point on the surface.	Deviation from planarity up to 8 mm along 2 m at each measuring point on the surface.	Deviation from planarity over 8 mm along 2 m at least one measuring point on the surface.	Deviation from planarity over 8 mm along 2 m at multiple measuring points on the surface.
Deviations from vertical or horizontal alignment	No deviation from vertical or horizontal.	Deviation from vertical up to 1 cm for each wall height and/or horizontal deviation up to 1 cm.	Deviation from vertical up to 1.5 cm for each wall height and/or horizontal deviation up to 2 cm.	Deviation from vertical over 1.5 cm for each wall height and/or horizontal deviation over 2 cm at one measuring point.	Deviation from vertical over 1.5 cm for each wall height and/or horizontal deviation over 2 cm at multiple measuring points.
Surface spalling or flaking	The entire structure is intact, no deterioration on the surface.	Initial local deterioration is only visible in isolated areas.	Deterioration exists in isolated areas on the surface.	Deterioration covers up to 5% of the wall surface.	Deterioration covers one-third or more of the wall surface.
Peeling of plaster or paint	No peeling of plaster or paint.	Initial peeling of plaster/paint is visible in isolated areas on the surface.	Peeling of plaster/paint in isolated areas on the surface.	Peeling of plaster/paint covers up to 5% of the wall surface.	Peeling of plaster/paint covers one-third or more of the wall surface.
Localized cracks.	The entire structure is intact, no local breakage on the surface.	The entire structure is intact, except for local breakage.	The structure is broken in an area of less than 5% of the surface.	The structure is broken in an area of up to one-third of the surface.	The structure is broken in more than one-third of the surface.
presence of Scratches, Holes, or Crevices	The entire structure is intact, and there are no scratches, holes, or crevices on the surface.	The entire structure is intact, except for scratches, holes, or crevices in isolated areas.	There are scratches, holes, or crevices covering less than 5% of the wall surface.	There are scratches, holes, or crevices, covering up to one-third of the wall surface.	There are scratches, holes, or crevices, covering more than one-third of the wall surface.
Stains on the partition surface	No stains on the surface.	Stains are visible on the surface, localized phenomenon.	Stains exist, covering less than 5% of the wall surface.	Stains exist, covering up to one-third of the wall surface.	Stains exist, covering more than one-third of the wall surface.
Noticeable or Visible Irregularities	No noticeable level differences between panels or construction joints.	Level differences between panels or construction joints are noticeable or visible. Localized phenomenon.	Level differences between panels or construction joints are noticeable or visible, covering less than 5% of the wall surface.	Level differences between panels or construction joints are noticeable or visible, covering up to one-third of the wall surface.	Level differences between panels or construction joints are noticeable or visible on more than one-third of the wall surface.
Corrosion signs on the surface.	No signs of corrosion on the partition surface.	Localized signs of corrosion are visible on the partition surface (localized).	Signs of corrosion cover less than 5% of the partition surface (< 5% Area).	Signs of corrosion cover up to 1/3 of the partition surface (up to 1/3 of Area).	Corrosion signs on more than 33% of the surface
Cracks on the surface.	No cracks on the partition surface.	Localized minor cracks are visible on the partition surface (localized minor).	Cracks cover less than 5% of the partition surface (<5% Area).	Cracks cover up to 1/3 of the partition surface (up to 1/3 of Area).	Cracks cover more than 1/3 of the partition surface (More than 1/3 of Area).
Quality of Wall-to-Wall Joint Construction	Very good construction quality.	Good construction quality.	Construction quality leaves something to be desired.	Very poor construction quality.	Very poor construction quality.
Exterior Wall-to-Wall Joint (Esthetics)	The connection is continuous, full, and intact. No aesthetic defects or minor deviations from verticality.	The connection is continuous, full, and intact. Some aesthetic defects or slight deviations from verticality.	Construction quality leaves something to be desired. Many aesthetic defects or deviations from verticality up to 1.5 cm.	Very poor construction quality. The connection is not continuous, not full, and not intact. Noticeable deviations from verticality at all joint heights.	Very poor construction quality. The connection is not continuous, not full, and not intact. Noticeable deviations from verticality throughout the entire joint.
Exterior Wall-to-Ceiling Joint (Esthetics)	The connection is continuous, full, and intact. No aesthetic defects or minor deviations from horizontality.	The connection is continuous, full, and intact. Some aesthetic defects or slight deviations from horizontality.	Construction quality leaves something to be desired. Many aesthetic defects or deviations from horizontality up to 2 cm.	Very poor construction quality. The connection is not continuous, not full, and not intact. Noticeable deviations from horizontality exceeding 2 cm.	Very poor construction quality. The connection is not continuous, not full, and not intact. Noticeable deviations from horizontality throughout the entire joint.
Cracks around Exterior Wall-to-Wall Joint	No cracks around the exterior wall-to-wall joint.	Local minor vertical cracks, no wider than 0.1 mm.	Visible cracks around the exterior wall-to-wall joint, up to 0.2 mm wide and up to 10% of the wall height.	Prominent cracks around the exterior wall-to-wall joint, up to 0.2 mm wide and covering more than one-third of the wall height.	Prominent cracks around the exterior wall-to-wall joint, wider than 0.2 mm or covering more than one-third of the wall height.
Cracks around Exterior Wall-to-Ceiling Joint	No cracks around the exterior wall-to-ceiling joint.	Local minor vertical cracks, no wider than 0.1 mm.	Visible cracks around the exterior wall-to-ceiling joint, up to 0.2 mm wide and up to 10% of the ceiling width.	Prominent cracks around the exterior wall-to-ceiling joint, up to 0.2 mm wide and covering more than one-third of the ceiling width.	Prominent cracks around the exterior wall-to-ceiling joint, wider than 0.2 mm or covering more than one-third of the ceiling width.
Electrical Installation Stability	All electrical installations are stable.	Electrical installations are loose but not displaced. Minor repairs are needed.	Electrical installations are loose and displaced. Less than one-third of the installations need repair.	Electrical installations are loose and displaced. One-third or more of the installations need repair.	Electrical installations are loose and displaced. More than half of the installations need repair.
Accumulation of Dirt in Exterior Wall Gaps	Exterior wall gaps are suitable for the type of wall.	Unsuitable exterior wall gaps. No visible dirt accumulation in wall-to-wall joints.	Unsuitable exterior wall gaps. Visible dirt accumulation is observed in isolated areas along the wall-to-wall joint.	Unsuitable exterior wall gaps. Prominent dirt accumulation is observed in multiple areas along the wall-to-wall joint.	Unsuitable exterior wall gaps. Prominent dirt accumulation is observed along the entire length of the wall-to-wall joint.

). Data collected included general information, such as construction type, apartment location, building age, detailed descriptions of the partition, and other building components, such as exterior walls, ceiling, flooring, and skirting. Moreover, the forms captured data on maintenance activities (painting, repairs, modifications) and additional occupancy details, including ownership status, apartment size, resident demographics, the presence of pets, and the density of objects within the living space.

The performance concept implies the translation of basic user requirements into flexible and technically non-prescriptive performance requirements and criteria. The performance level of interior partitions is thus defined as the ability to meet functional and durability requirements, rather than technical specifications that extend over the expected service life, and encompasses factors such as structural integrity, resistance to wear and tear, maintenance needs, and visual appearance. The performance level of each partition is assessed using a five-grade Component Performance (CP) scale, which is categorized into five levels. A top grade of 5 (100%) indicates optimal performance, signifying a component without any signs of wear and a perfect performance condition. Conversely, a bottom grade of 1 (20%) signifies complete dysfunction or severe deterioration. Each grade on the scale is accompanied by descriptive definitions, facilitating consistent and accurate assessments across different surveyors. These definitions provide a detailed verbal explanation for each level, as shown in Table 1, ensuring uniformity and clarity in the rating process. This refined grading system effectively captures the range of partition conditions, extending from excellent to poor, allowing for a comprehensive evaluation of component deterioration and performance level.

3.2. Determination of the Performance Level and the Failure Mechanisms

The phenomena detailed in Table 1 allow for the determination of the actual component performance (CP) of each interior partition surveyed during field surveys in three areas: initial component performance, standard condition performance (SCP), and failure condition performance.

3.2.1. Initial Component Performance (ICP)

The initial component performance (ICP) describes the quality of a component’s construction and the performance of vertical and horizontal seams at the junctions of the partition with the exterior wall and ceiling, respectively. The ICP value is determined based on the equal weighting of several phenomena that characterize each partition after construction, which are inherent in construction defects and do not progress over time. The ICP for partitions is determined by a formula, as expressed in Equation (1).

$$ICP=\frac{\left(\sum _{i=1}^{n}C{P}_i\right)}{n}$$

(1)

The $C{P}_i$ and $n=5$ are the ratings for the inherent construction deviations, which includes evaluating deviations and imperfections that are inherent in their construction and do not evolve over time. These include:

• Deviations from flatness or undulations on the partition surface.
• Deviations from vertical or horizontal alignment.
• Noticeable or visible seams and joints.
• Quality of the intersection between the partition and exterior walls.
• Quality of the intersection between the partition and the ceiling.

This formulation provides a standardized assessment of the initial quality and performance of partitions.

3.2.2. Standard Condition Performance (SCP)

Standard condition performance (SCP) represents the deterioration in the performance of a partition under normal wear conditions without the impact of failure conditions (such as poor planning and construction, use of inappropriate or low-quality materials, etc.). Improper maintenance or exceptional occupancy conditions can accelerate the deterioration rate of the partition function. The SCP value is determined based on an equal weighting of all the phenomena listed above. The SCP of each partition is determined on the basis of the following general expression (2):

$$SCP=\sum _{i=1}^{n}C{P}_i/n$$

(2)

In this equation, n equals 6, and CP_i represents the ratings for each criterion that characterizes the partition’s deterioration under standard wear conditions. The SCP assesses the partition’s decline in performance by referring to various indicators of wear and tear, including:

• Surface spalling or flaking.
• Peeling of plaster or paint.
• Localized breakages.
• Scratches, holes, or indentations.
• Stains on the partition surface.
• Accumulation of dirt on the skirting, especially when it significantly protrudes from the partition surface.

3.2.3. Failure Condition Performance (FCP)

Failure condition performance (FCP) expresses the partial impact of failure factors on the component deterioration processes. It distinguishes the performance level of a partition in standard conditions from the level of the same type of partition in failure conditions, in which each failure has an exclusive deterioration pattern. The failure mechanisms likely to affect the overall performance level of various types of partitions are characterized in advance, based on professional judgment or data from relevant literature. Factors influencing the occurrence of failures are typically rooted in faulty construction and/or design, although a minority are caused by poor material quality. Failures are characterized by surface cracking, cracking at the junctions of partition-exterior wall and partition-ceiling, cracking around door frames, signs of corrosion on the surface, and instability of electrical fixtures within the partition.

3.3. Occupancy Conditions

In the research, six “negative occupancy factors” are identified as conditions within the living environment that have been found to negatively contribute to the deterioration and wear of partitions, thereby reducing their lifespan and performance. These factors can be more prevalent or impactful in specific residential settings, and include:

• Non-Ownership of the Dwelling: Renters or temporary occupants might not have the same incentive as owners to maintain or protect the property, potentially leading to quicker degradation.
• Poor Maintenance (no maintenance activities for over three years): Lack of regular maintenance activities can deteriorate materials and fixtures.
• High Residential Density (less than 30 m² per occupant): More people living in a smaller area can increase usage and physical impacts on partitions, and also the faster accumulation of dirt or damage.
• Presence of Young Children: Young children (under 12 years-of-age) often engage in activities that can lead to more stains, impacts, or other forms of damage.
• Domestic Animals: Pets can scratch, stain, or otherwise damage partitions, especially if they are not trained or their activities are not monitored.
• High Object Density: Many objects within a living space can lead to more accidental impacts; mounting of fixtures; or modifications that can affect the partitions’ integrity.

Each observation was classified into one of four categories based on the number of negative factors. The number of Negative Occupancy Factors (µ) was used to define the service conditions as follows:

$\mathrm{\mu }=0$ Light service conditions

$2>\mathrm{\mu }\ge 1$ Moderate service conditions

$4>\mathrm{\mu }\ge 2$ Standard service conditions

$\mathrm{\mu }\ge 4$ Intensive service conditions

In the study, statistical analysis of each service condition category was performed individually, and a comprehensive expression was also developed to reflect the combined influence of partition age and various negative occupancy factors on the partition wear patterns. This method helped to provide a detailed understanding of how different factors collectively impact the degradation of residential partitions.

3.4. Typical Deterioration Patterns

The development of deterioration pattern curves enables statistical analysis to explore different deterioration pathways. Graphically recording partition survey data over time showcases the real-time component performance (CP) and allows these curves to be formulated for each partition type. The curves evaluate partition deterioration under standard conditions and in response to specific failure mechanisms. This methodology thoroughly assesses partition durability and performance across various service conditions.

Through statistical analysis based on the general linear model, it is possible to determine, for deterioration pattern curves that allow it, a typical deterioration path (TDP) that graphically and quantitatively expresses the expected average decline in component performance over time. Additionally, it is possible to establish a prediction interval that will contain, with a given statistical probability, future observations of similar components at different stages of wear. The results of this analysis will make it possible to predict the continued deterioration pattern of partitions according to the wear conditions they are exposed to, and estimate the period when their performance level will fall below the required minimal level.

In cases where a specific failure mechanism influences the deterioration pattern, the reduction in component performance may not follow a defined path, and instead display a dispersed array of points over time. This irregular pattern often does not arise from the component’s age but rather from inherited flaws rooted in defective construction or design. The impact of the failure factor varies in severity, is closely tied to the quality of construction, and can be quantified by referring to the average decrease in performance it causes. By incorporating this failure influence into the component’s inherent deterioration pattern, we can delineate a typical deterioration pathway and predict service life in such failure conditions.

3.5. Service Life Expectancy and Performance Level

Predicting service life expectancy and performance in standard conditions involves defining a building component’s service life as the age when it fails to meet performance criteria. Life expectancy is, in standard deterioration, the service duration until refurbishment becomes impractical or replacement is necessary.

By utilizing the typical deterioration path (TDP) for given wear conditions, it is possible to forecast the continued decline in component performance (CP) over time (see Figure 1), and predict the service life expectancy (LE), up to the point where rehabilitation or replacement is needed, which is established by referring to the point where the typical deterioration path (TDP) and the minimum required component performance (MRCP) line intersect. An upper and lower limit for the predicted service life interval (PSLI) of building components at a given performance level can be determined by referring to the intersection points of the prediction interval (PI) boundaries, in which the line represents the MRCP. This defined range of service years represents a given statistical significance. Conversely, the intersection points of the prediction interval (PI) boundaries, in which the line corresponds to a given component’s age, support the predicted component performance interval (PCPI) at that point in time.

3.6. Impact of Failure Mechanisms on Predicted Service Life

The predicted service life is the age at which the component reaches a state where it is no longer feasible or economical to refurbish and must be replaced. It is typically calculated by multiplying the standard life expectancy (SLE) in standard deterioration conditions by coefficients that reflect the impact of various failures observed in the building. The partial impact of a given failure mechanism on a building component’s overall service life is expressed through a life expectancy limiting coefficient (LELC), as follows (3):

$$LELC=1-\left(\frac{SLE-LEFC}{SLE}\right)\times IC$$

(3)

LELC-Life Expectancy Limiting Coefficient

SLE-Standard Life Expectancy

LEFC-Life Expectancy under Failure Conditions

IC-Impact Coefficient

The impact coefficient reflects the extent of influence a particular failure factor has on the component’s overall deterioration mechanism. Each failure factor affects the component’s overall life expectancy differently, and accordingly its impact coefficient is determined. The coefficient value ranges from 0 (failures that do not affect the component’s service life) to 1 (failures that have maximal effect on the service life.

Calculating the LELC of a component in specific failure is based on the predicted service life using typical deterioration patterns for the component (in both standard deterioration conditions and failure conditions). The LELC decreases as the impact coefficient (IC) increases and as the life expectancy of the component in failure conditions (LEFC) decreases. In other words, the more severe and impactful the failure, the lower the LELC value, increasing its effect on the component’s overall lifespan.

It is important to note that this model considers the impact of individual failure mechanisms on the component’s service life expectancy separately, and does not account for the combined effects of various failure mechanisms and interactions between them (whether independent, mitigated, or synergistic).

3.7. Summary

The method presented in this chapter was developed to predict the deterioration pattern and service life of interior residential building components (in this case, partitions). The method is based on collecting observations through field surveys, systematically rating them according to predefined performance scales, and using statistical means to analyze findings and predict parameters necessary for determining the component’s service life expectancy in standard service and failure conditions.

The method offers notable advantages due to its systematic approach, enabling observation of the deterioration rate of partitions in residential buildings and the prediction of service life expectancy. It also effectively supports life cycle planning and the calculation of life cycle costs for residential construction processes. However, the method requires substantial investments of time and effort into conducting field surveys, gathering data, and establishing an appropriate sample database for each partition type, which is a significant limitation.

4. Field Survey Results and Discussion

The study’s sample encompassed 133 residential interior partitions, with ages ranging from 1 to 26 years, that were categorized into three types: gypsum board, aerated concrete block, and hollow concrete block partitions. The site selection criteria aimed to guarantee a representative sample of residential buildings by considering geographic location, building type (low-rise up to four floors and high-rise above four floors), construction method (concrete frame buildings with conventional or industrialized ceilings and cladding components), and occupancy date. Including a site in the sample depended on the availability of detailed prior information about the implementation of partitions. The list of sites and the distribution of the number of apartments, made in accordance with the sample classification are provided in

Table 2. Geographic distribution of field surveyed partitions by type.

Geographical Location	Gypsum Board Partitions	Gypsum Block Partitions	Aerated Concrete Block Partitions	Hollow Concrete Block Partitions
Ashdod		3
Be’er Sheva			3	5
Giv’atayim	4
Gedera			2
Hadera	4
Haifa		3		1
Carmiel	9		3	6
Modi’in	6
Nesher		3		7
Kiryat Bialik				3
Kiryat Gat	7
Kiryat Tiv’on				7
Kiryat Yam	2		7	10
Rishon LeZion		11	2
Rehovot	1	6	13
Shoham				3
Tel Aviv	2
Total	35	26	30	42

, which offers a detailed overview of the survey’s scope, presenting a comprehensive breakdown of the apartments surveyed in each sample category.

The apartment selection within each building was random, not pre-determined, and apartments at various levels, including on top, middle, and bottom floors, were surveyed. The investigation process for each apartment included interviewing residents with a questionnaire and visual inspection of the partitions that rated them on the basis of their physical and visual condition through marking, on a scale from 0 to 100%, that reflected the partition’s performance in various aspects.

The age range of most observations in the gypsum block partition sample was 1–4 years, and an additional three observations were eight years old. This age distribution did not allow for reliable conclusions to be drawn from statistical analysis, and service life expectancy prediction or life cycle cost calculation were therefore not performed for this partition type, and it was not included in the research conclusions. The main findings for the remaining three partition types are detailed later in the study.

4.1. Initial Component Performance (ICP)

The initial component performance (ICP) for partitions, which reflects their baseline performance, is derived by using Equation (1).

Figure 2, Figure 3 and Figure 4 present the ICP values gathered from sample observations, highlighting any substantial deviations from the average ICP value. These deviations are critical for identifying and excluding outliers in the data set, ensuring a more accurate and representative analysis of the partition’s initial quality and expected performance.

Average ICP levels, which indicate a partition’s initial quality performance, show variability across partition types. Gypsum board partitions have an average ICP of 82.7%, reflecting common issues such as surface waviness and visible seams that can impact their functional longevity. In contrast, autoclaved and hollow concrete block partitions exhibit higher average ICP levels, at 88.82% and 91.0%, respectively. These types often demonstrate joint workmanship challenges between partitions and adjoining structural elements like walls and ceilings, although this can be mitigated by improved workmanship and stringent quality control measures.

Table 3. Initial Partition Performance Level.

	n	Average	S.D.	Min	Max
Gypsum board
Deviation from flatness	33	80	11.99	50	100
Deviation from Verticality/Horizontal	33	82.42	12.51	60	100
Tangible Interfaces	33	81.52	16.98	40	100
Intersection between the partition and exterior walls	32	86.88	9.98	70	100
Intersection between the partition and the ceiling	33	82.73	11.8	50	100
ICP [%]	33	82.7	5.41	74	94
Autoclaved Concrete Blocks Partitions
Deviation from flatness	30	90.33	10.98	70	100
Deviation from Verticality/Horizontal	30	87.67	10.73	70	100
Tangible Interfaces	30	94	11.02	60	100
Intersection between the partition and exterior walls	29	86.9	11.68	70	100
Intersection between the partition and ceiling	30	85.33	11.37	60	100
ICP [%]	30	88.82	6.92	78	98
Hollow Concrete Blocks partitions
Deviation from flatness	41	87.8	12.94	60	100
Deviation from Verticality/Horizontal	41	89.63	10.51	70	100
Tangible Interfaces	41	100	0	100	100
Intersection between the partition and exterior walls	41	86.95	11.06	60	100
Intersection between the partition and ceiling	41	90.61	8.67	70	100
ICP [%]	41	91.00	6.34	74	100

summarizes the statistical data for each partition type, highlighting the distinct performance characteristics that can influence construction and maintenance decision-making.

4.2. Partition Performance under Standard Deterioration Conditions

In this chapter, the deterioration patterns of partition in standard conditions are analyzed. Figure 5, Figure 6 and Figure 7 present the SCP (standard condition performance) value for each type of partition of observational survey data. The SCP is essential in assessing the performance and durability of partitions, thereby informing their quality and structural integrity over time. A further observation was made by classifying the data by occupancy conditions, according to the number of negative occupancy factors. A comparative regression analysis, augmented by an R-squared test, was employed to elucidate the deterioration patterns for each partition type under varied occupancy conditions. A specific regression equation correlates deterioration to partition age for each type and condition. The SCP, modeled as a linear function of age, is given by Equation (4).

$$SCP=\alpha *Age+\beta$$

(4)

In this context, α represents the slope coefficient, indicating the rate of deterioration over time, and β denotes the intercept coefficient, reflecting the partition’s initial performance level. Together, these parameters provide a mathematical model for forecasting the deterioration pattern of partitions in standard conditions. A steeper slope indicates a quicker degradation rate, reflecting the speed at which partition performance decreases over time. This slope is instrumental in understanding durability and projecting how various partition types will fare in standard service conditions as they age. Analyzing slope magnitude is essential for gauging the durability of different partition types and planning maintenance and replacement schedules accordingly.

The regression lines illustrate each sample’s typical deterioration path (TDP). The deterioration paths are linear, indicating the consistent and sustained influence of the deterioration mechanisms across the examined partitions. As expected, it can be concluded that, in all samples, the deterioration process in standard service conditions intensifies with partition age. However, the deterioration rate differs for each sample. It should be noted that it was impossible to fit a regression line to the autoclaved aerated concrete block partition in light occupancy conditions due to a lack of observations (Figure 6).

The deterioration rate of partitions made from hollow concrete blocks is found to be the most moderate rate, which reflects the material’s durability and experience in constructing such partitions. In contrast, the degradation rate of gypsum board partitions is observed to be significantly higher, surpassing autoclaved aerated concrete block partitions in this respect. Additionally, clear differences can be discerned between the deterioration path characteristic of various levels of negative occupancy factors (µ) within each sample; here, as expected, the deterioration rate of the path associated with intensive conditions (µ > 4) is the most accelerated, reaching up to 2.96 performance points per year in the gypsum board partition. These differences in deterioration rates impact the component’s predicted service life expectancy.

It is noteworthy that the dispersion of observations around regression lines tends to increase as the number of negative occupancy factors rises. Consequently, the resulting prediction interval is quite broad, which, from a statistical standpoint, indicates a less robust fit of the predictive model to sample observations. This variability suggests that the presence of more negative factors initiates greater uncertainty in the deterioration predictions.

4.3. Service Life Expectancy and Performance Level under Standard Conditions

A statistical analysis was conducted of each sample, which included fitting a regression line and formulas, determining the prediction interval, and calculating the predicted service life expectancy in standard deterioration conditions. Additionally, prediction interval boundaries for the expected behavior of the partition were established with a statistical probability of p = 0.80, as required by ISO 15686 [21].

To calculate the predicted life expectancy of the components, a minimum required component performance level (MRCP) of 60% was determined (MRCP_60%), reflecting the component’s advanced deterioration and necessitating its complete rehabilitation. The partition’s predicted service life expectancy (SLE) is determined by where the MRCP_60% line meets the TDP regression line, given specific occupancy conditions (µ). The range of years for expected performance (PSLI) is set by the prediction interval (PI) with an 80% statistical probability. The PI intersections provide the probable upper and lower performance levels (PCPI) for a specific component age. It should be noted that because the age range of the survey components does not exceed 30 years, most intersections occur outside the range examined in the field survey.

Table 4. Service Life Predictions at a Minimum Required Component Performance Level of 60% (MRCP_60%); Component Performance Intervals for Various Partitions in Different Occupancy Conditions.

Occupancy Conditions	Light	Moderate	Standard	Intensive
	µ = 0	1 ≤ µ < 2	2 ≤ µ < 4	µ ≥ 4
Gypsum board
Service life prediction MRCP60% [years]	95	47	27	11
Expected service life interval MRCP60% [years]	101–89	51–43	29–24	16–6
Component performance interval expected at 20 years	94–90	86–79	72–65	47–18
Component performance interval expected at 25 years	92–88	81–75	66–59	33–3
Autoclaved Concrete Blocks
Service life prediction MRCP60% [years]	n/a	72	32	18
Expected service life interval MRCP60% [years]	n/a	78–66	35–29	23–13
Component performance interval expected at 20 years	n/a	88–84	76–70	65–47
Component performance interval expected at 25 years	n/a	86–81	71–64	57–38
Hollow Concrete Blocks
Service life prediction MRCP60% [years]	164	118	86	38
Expected service life interval MRCP60% [years]	174–156	125–111	95–77	45–31
Component performance interval expected at 20 years	96–95	93–89	90–84	82–70
Component performance interval expected at 25 years	94–93	91–87	88–82	78–65

summarizes the analysis results of each partition type.

In referring to the prediction interval boundaries established for gypsum board partitions, we see the partitions are only expected to exceed the range of 50 years (average service life expectancy of 95 years) in light service conditions (µ = 0). As the number of negative occupancy factors increases, the average service life expectancy decreases to 47, 27, and 11 years, respectively. At levels of µ greater than 2, it can be expected that, for a component lifespan of about 25 years, the functional range will fall below the desired minimum performance level of MRCP_60%. This last finding means that partitions of this type are not suitable for situations of occupancy conditions with µ greater than 2; that is, gypsum board partitions are not suitable for harsher than average occupancy conditions.

For autoclaved blocks, the SLE is projected at 72 years in moderate occupancy conditions, and reduces to 18 years in more intensive scenarios. Within a 25-year span, it is anticipated that the performance level will decline below the required MRCP_60% threshold, indicating significant deterioration. Consequently, this type is generally not recommended for intensive occupancy conditions (µ > 4), due to its reduced durability and functional lifespan in such conditions.

In the case of hollow concrete blocks, the projected service life expectancy (SLE) only falls below 50 years in intensive occupancy conditions (µ > 4). Additionally, for a span of 25 years, these partitions maintain performance above the MRCP_60% threshold, demonstrating resilience and a slower rate of deterioration. Thus, it can be concluded that hollow concrete block partitions exhibit higher durability and are generally more suitable across the range of conditions observed in this study.

Figure 8 and Figure 9 compare service life expectancy (SLE) across partition types and occupancy conditions, finding that hollow concrete block partitions have the longest service life cycle of all examined partitions. Within the category of lightweight partitions, aerated concrete block partitions exhibit a marginal advantage in durability and sustained performance over time, when compared to gypsum board counterparts.

4.4. Predicting Durability and Performance with Inherent Construction Defects

Construction failures due to flawed construction significantly reduce the initial performance level of partitions. This foundational data is incorporated into the typical deterioration path of a partition in standard conditions by using Equation (5):

$$\mathrm{S}\mathrm{W}\mathrm{C}\mathrm{P}=-\mathsf{\alpha }\times \mathrm{a}\mathrm{g}\mathrm{e}+\mathsf{\beta }-\left[\frac{100-\mathrm{I}\mathrm{C}\mathrm{P}}{\mathrm{n}}\right]$$

(5)

SWCP represents the standard weighted condition performance, combining the initial state and typical deterioration. α and β are parameters in the regression formula, depicting the typical deterioration path in standard conditions. ICP denotes the average initial component performance. And n is the count of indicators of component deterioration in standard conditions. This formula is employed to predict the weighted service life expectancy of partitions in various samples, and its results are detailed in

Table 5. Service Life Predictions at a Minimum Required Component Performance level of 60% (MRCP_60%) expectancy; Component Performance Intervals for Various Partitions in Different Occupancy Conditions.

Occupancy Conditions	Light	Moderate	Standard	Intensive
	µ = 0	1 ≤ µ < 2	2 ≤ µ < 4	µ ≥ 4
Gypsum board
Service life prediction MRCP60% [years]	89	44	24	10
Expected service life interval MRCP60% [years]	94–83	48–40	27–22	15–5
Component performance interval expected at 20 years	92–87	83–76	69–62	45–15
Component performance interval expected at 25 years	89–85	79–72	63–56	30–0
Autoclaved Concrete Blocks
Service life prediction MRCP60% [years]	n/a	68	30	16
Expected service life interval MRCP60% [years]	n/a	74–62	34–27	12–22
Component performance interval expected at 20 years	n/a	86–82	74–68	63–45
Component performance interval expected at 25 years	n/a	84–79	69–63	56–36
Hollow Concrete Blocks
Service life prediction MRCP60% [years]	158	113	82	36
Expected service life interval MRCP60% [years]	167–151	120–106	91–74	44–29
Component performance interval expected at 20 years	94–93	91–87	89–82	80–68
Component performance interval expected at 25 years	93–92	90–86	87–80	76–64

.

As can be seen, considering the initial state of the partitions reduces the expected lifespan and functional range in various samples, as implied in practice by the survey results. The service life expectancy of gypsum board partitions with a performance level of 60% MRCP reaches 89–94% of the value of a well-constructed partition in similar service conditions but does not show functional defects. The reduced service life expectancy of partitions made of autoclaved aerated concrete blocks, when compared to a well-executed component, amounts to 6–11%; hollow concrete blocks are more moderate, and only amount to 3.7–5.26%.

4.5. Deterioration Patterns in Failure Conditions

This section presents the deterioration patterns observed in failure conditions, and examines the partition performance of collected data, specifically focusing on indicators of failure, as shown in Figure 10, Figure 11 and Figure 12. The observed deterioration patterns indicate that the decrease in performance due to various defects is not directly related to component age but rather to different failure mechanisms that manifest themselves with varying degrees of severity, subject to the influence of design and construction flaws and other factors, including the frequency and quality of repairs during the partition life cycle. The impact of failure factors in each sample can be characterized by referring to their occurrence rate and the resultant average decrease in component performance. Calculating this effect of the different failure factors on the standard deterioration patterns will enable service life expectancy and performance level range of partitions to be determined in the case of failure conditions.

This data enables an analysis of the occurrence and average performance impact of various defects across the three different partition types (hollow concrete, gypsum, and autoclaved).

Table 6. Summary of the Impact of Failure Conditions on Partition Performance. This table provides the count of observed samples (n), the proportion of samples exhibiting defects in failure conditions (%), and the average performance score (mean) across different partition typ.

	Hollow Concrete			Gypsum			Autoclaved
Total Number of Samples	41.00			33			30
	n	%	mean	n	%	mean	n	%	mean
Cracks on the Surface	6.00	15%	83.33	11	27%	76.00	12	29%	73.33
Crack at Partition-Wall Junction	15.00	37%	80.00	13	32%	74.62	18	44%	75.88
Crack at Partition-Ceiling Junction	10.00	24%	70.00	22	54%	67.73	12	29%	71.82
Crack Around Door Frame	12.00	29%	78.18	21	51%	73.16	10	24%	68.89
Stability of Electrical Installations	10.00	24%	82.00	17	41%	72.94	11	27%	81.82

summarizes results obtained for the impact of failure conditions on partition performance. Defects studied include cracks on the surface, cracks at the partition-wall junction, cracks at the partition-ceiling junction, cracks around the door frame, and the stability of electrical installations.

In hollow concrete partitions, the most prevalent defect is cracks at the partition-wall junction, which is observed in 37% of the samples, with an average performance score of 80.00. The least common defect is cracks on the surface, noted in 15% of samples with an 83.33 performance score. In gypsum partitions, the most common defects are cracks around the door frame and at the partition-ceiling junction, observed in 51% and 54% of the samples, respectively, which significantly impact performance, with average scores of 73.16 and 67.73, respectively. The stability of electrical installations is also a notable issue, affecting 41% of the gypsum partitions. In autoclaved partitions, the most frequent defect is cracks at the partition-wall junction, seen in 44% of samples, with an average performance impact of 75.88. Cracks around the door frame and at the partition-ceiling junction are also significant issues, affecting 24% and 29% of samples, respectively.

The comparative analysis of partition performance (Figure 13) reveals that hollow concrete typically exhibits higher performance averages across different failure types than gypsum and autoclaved partitions. This information is critical in assessing each material’s durability and resilience to common building failure. Gypsum partitions are particularly vulnerable to cracking, especially around door frames and ceiling junctions, which markedly diminishes their performance. In contrast, hollow concrete partitions demonstrate relative robustness, showing fewer surface cracks and achieving better performance metrics in critical areas. Autoclaved partitions tend to show moderate susceptibility and impact, positioning them between the other types for both defect frequency and performance. This analysis highlights the need for precise construction methods to be developed and interventions that are specific to each partition type to be implemented, as this will help to mitigate prevalent defects and enhance durability. Accordingly, the selection of partition materials should consider these typical failure patterns, with specific attention to the impact that their deterioration patterns have on maintenance and life cycle performance in building design.

4.6. Service Life Expectancy and Performance Level under Failure Conditions

Assuming that each failure factor operates independently from others, the cumulative and partial influence on a partition’s weighted standard deterioration path (initial state and standard failure) can be quantified by using the following expression:

$$TWCP=-\alpha *age+\beta -\left[\frac{100-ICP}{n}\right]-{\sum }_1^m\left[\frac{100-FC{P}_m}{n}\right]$$

(6)

where:

• $TWCP$-Weighted performance under failure conditions (initial state + standard failure + specific failure).
• $\alpha ,\beta$-Regression parameters of the typical deterioration path in standard conditions.
• $ICP$-Average Initial Component Performance.
• $FC{P}_m$-Average component performance of a specific failure mechanism.
• $n$-Number of component deterioration characteristics in standard conditions
• $m$-Number of independent failure mechanisms

This expression, which is used to predict the comprehensive service life expectancy of partitions in various scenarios, considers each failure factor, both independently and in combination (Figure 14). The summarized results are presented in

Table 7. Service Life Predictions at a Minimum Required Component Performance level of 60% (MRCP_60%); Component Performance Intervals for Gypsum Board in Different Occupancy Conditions.

Gypsum Board
Occupancy Conditions	Light	Moderate	Standard	Intensive
	µ = 0	1 ≤ µ < 2	2 ≤ µ < 4	µ ≥ 4
Failure mechanism	Crack at Partition-Ceiling Junction
Service life prediction MRCP60% [years]	76	37	20	8
Expected service life interval MRCP60% [years]	70–82	33–41	18–23	3–13
Component performance interval expected at 20 years	82–86	70–77	57–64	10–39
Component performance interval expected at 25 years	80–84	66–73	50–57	0–25
Failure mechanism	Crack at Partition-Wall Junction
Service life prediction MRCP60% [years]	79	39	21	8
Expected service life interval MRCP60% [years]	73–85	35–43	19–24	4–13
Component performance interval expected at 20 years	83–87	71–79	58–65	11–40
Component performance interval expected at 25 years	81–85	68–74	52–59	0–26
Failure mechanism	Crack Around Door Frame
Service life prediction MRCP60% [years]	78	38	21	8
Expected service life interval MRCP60% [years]	73–84	34–42	19–24	3–13
Component performance interval expected at 20 years	83–87	71–78	58–65	11–40
Component performance interval expected at 25 years	81–85	67–74	51–58	0–26
Failure mechanism	Cracks on the Surface
Service life prediction MRCP60% [years]	79	39	21	8
Expected service life interval MRCP60% [years]	73–85	35–43	19–24	4–13
Component performance interval expected at 20 years	83–87	71–79	58–65	11–40
Component performance interval expected at 25 years	81–85	68–74	52–59	0–26
Failure mechanism	Stability of Electrical Installations
Service life prediction MRCP60% [years]	78	38	21	8
Expected service life interval MRCP60% [years]	72–84	34–42	18–24	3–13
Component performance interval expected at 20 years	83–87	71–78	58–65	10–40
Component performance interval expected at 25 years	80–85	67–74	51–58	0–25

,

Table 8. Service Life Predictions at a Minimum Required Component Performance level of 60% (MRCP_60%); Component Performance Intervals for Autoclaved Concrete Blocks in Different Occupancy Conditions.

Autoclaved Concrete Blocks
Occupancy Conditions	Light	Moderate	Standard	Intensive
	µ = 0	1 ≤ µ < 2	2 ≤ µ < 4	µ ≥ 4
Failure mechanism	Crack at Partition-Ceiling Junction
Service life prediction MRCP60% [years]	n/a	59	26	14
Expected service life interval MRCP60% [years]	n/a	65–53	29–23	9–19
Component performance interval expected at 20 years	n/a	82–77	70–63	58–41
Component performance interval expected at 25 years	n/a	79–75	64–58	51–32
Failure mechanism	Crack at Partition-Wall Junction
Service life prediction MRCP60% [years]	n/a	60	27	14
Expected service life interval MRCP60% [years]	n/a	66–54	30–23	9-19
Component performance interval expected at 20 years	n/a	82–78	70–64	59–41
Component performance interval expected at 25 years	n/a	80–75	65–58	51–32
Failure mechanism	Crack Around Door Frame
Service life prediction MRCP60% [years]	n/a	58	26	13
Expected service life interval MRCP60% [years]	n/a	64–53	29–23	9–19
Component performance interval expected at 20 years	n/a	81–77	69–63	58–40
Component performance interval expected at 25 years	n/a	79–74	64–58	51–31
Failure mechanism	Cracks on the Surface
Service life prediction MRCP60% [years]	n/a	59	26	14
Expected service life interval MRCP60% [years]	n/a	65–54	29–23	9–19
Component performance interval expected at 20 years	n/a	82–77	70–63	58–41
Component performance interval expected at 25 years	n/a	80–75	65–58	51–32
Failure mechanism	Stability of Electrical Installations
Service life prediction MRCP60% [years]	n/a	62	28	15
Expected service life interval MRCP60% [years]	n/a	68–57	31–25	10–20
Component performance interval expected at 20 years	n/a	83–79	71–65	60–42
Component performance interval expected at 25 years	n/a	81–76	66–60	53–33

and

Table 9. Service Life Predictions at a Minimum Required Component Performance level of 60% (MRCP_60%); Component Performance Intervals for Hollow Concrete Blocks in Different Occupancy Conditions.

Hollow Concrete Blocks
Occupancy Conditions	Light	Moderate	Standard	Intensive
	µ = 0	1 ≤ µ<2	2 ≤ µ<4	µ ≥ 4
Failure mechanism-Crack at Partition-Ceiling Junction
Service life prediction MRCP60% [years]	141	100	72	32
Expected service life interval MRCP60% [years]	149–135	107–93	83–64	39–24
Component performance interval expected at 20 years	90–89	87–83	85–78	76–64
Component performance interval expected at 25 years	89–88	85–82	83–76	72–60
Failure mechanism	Crack at Partition-Wall Junction
Service life prediction MRCP60% [years]	146	104	75	33
Expected service life interval MRCP60% [years]	154–139	111–97	85–66	40–26
Component performance interval expected at 20 years	91–90	88–84	86–79	78–65
Component performance interval expected at 25 years	90–89	87–83	84–77	73–61
Failure mechanism	Crack Around Door Frame
Service life prediction MRCP60% [years]	141	100	72	32
Expected service life interval MRCP60% [years]	149–135	107–93	83–64	39–24
Component performance interval expected at 20 years	90–89	87–83	85–78	76–64
Component performance interval expected at 25 years	89–88	85–82	83–76	72–60
Failure mechanism	Cracks on the Surface
Service life prediction MRCP60% [years]	145	103	74	33
Expected service life interval MRCP60% [years]	153–139	110–96	85–66	40–26
Component performance interval expected at 20 years	91–90	88–84	86–79	77–65
Component performance interval expected at 25 years	90–89	86–83	84–77	73–61
Failure mechanism	Stability of Electrical Installations
Service life prediction MRCP60% [years]	146	104	75	33
Expected service life interval MRCP60% [years]	154–139	111–97	85–66	40–26
Component performance interval expected at 20 years	91–90	88–84	86–79	78–65
Component performance interval expected at 25 years	90–89	87–83	84–77	73–61

.

The average total decrease in performance level as a result of the combined influence of failure mechanisms reaches 23 performance points in the gypsum board partition and 21 performance points in the autoclaved aerated concrete block partition sample.

Table 10. Service Life Predictions and Component Performance Intervals in Cumulative Failure Conditions.

Occupancy Conditions	Light	Moderate	Standard	Intensive
	µ = 0	1 ≤ µ < 2	2 ≤ µ < 4	µ ≥ 4
Gypsum board
Service life prediction MRCP60% [years]	36	16	7	2
Expected service life interval MRCP60% [years]	41–31	20–11	10–5	7–0
Component performance interval expected at 20 years	69–65	60–53	47–40	22–0
Component performance interval expected at 25 years	67–62	56–49	40–33	7–0
Autoclaved Concrete Blocks
Service life prediction MRCP60% [years]	n/a	26	10	3
Expected service life interval MRCP60% [years]	n/a	30–21	13–7	7–0
Component performance interval expected at 20 years	n/a	65–60	53–47	42–24
Component performance interval expected at 25 years	n/a	63–58	48–41	34–15
Hollow Concrete Blocks
Service life prediction MRCP60% [years]	87	58	40	17
Expected service life interval MRCP60% [years]	90–84	64–51	49–31	9–24
Component performance interval expected at 20 years	90–89	87–83	85–78	76–64
Component performance interval expected at 25 years	89–88	86–82	83–76	72–60

presents the service life prediction in cumulative failure conditions. In this case, light-weight partitions are expected to reach a 60% performance level in less than 50 years. The overall average decrease in the performance level of hollow concrete block partitions, as a result of cumulative failure mechanisms, totals 18 performance points. In this condition, these partitions would be expected to reach a 60% performance level in less than 50 years, but only if their standard deterioration processes are subject to the influence of negative occupancy factors at the 2 ≥ μ level. It can also be expected that, for a component lifespan of about 25 years, the performance range of this partition type will not drop below the minimum desired performance level of MRCP60%.

This analysis emphasizes the importance of considering the combined effect of failure mechanisms when predicting the service life expectancy and performance range of partitions in failure conditions, and stresses that this issue is particularly important in a building design and maintenance context.

4.7. Impact of Failures Mechanisms on Predicted Service Life Expectancy of Partitions

Table 11. Summary of Life Expectancy Limiting Coefficients (LELC) of Various Failure Mechanisms: Cumulative Impact at Different Negative Occupancy Factor Levels (µ).

Occupancy Conditions	Light	Moderate	Standard	Intensive
	µ = 0	1 ≤ µ < 2	2 ≤ µ < 4	µ ≥ 4
Gypsum board
Crack at Partition-Ceiling Junction	0.85	0.84	0.83	0.80
Crack at Partition-Wall Junction	0.89	0.89	0.88	0.80
Crack around the Door Frame	0.88	0.86	0.88	0.80
Cracks on the Surface	0.89	0.89	0.88	0.80
Stability of Electrical Installations	0.88	0.86	0.88	0.80
Cumulative Impact of all failure mechanisms	0.40	0.36	0.29	0.20
Autoclaved Concrete Blocks
Crack at Partition-Ceiling Junction	n/a	0.87	0.87	0.88
Crack at Partition-Wall Junction	n/a	0.88	0.9	0.88
Crack around the Door Frame	n/a	0.85	0.87	0.81
Cracks on the Surface	n/a	0.87	0.87	0.88
Stability of Electrical Installations	n/a	0.91	0.93	0.94
Cumulative Impact of all failure mechanisms	n/a	0.38	0.33	0.19
Hollow Concrete Blocks
Crack at Partition-Ceiling Junction	0.89	0.88	0.88	0.89
Crack at Partition-Wall Junction	0.92	0.92	0.91	0.92
Crack around the Door Frame	0.89	0.88	0.88	0.89
Cracks on the Surface	0.92	0.91	0.9	0.92
Stability of Electrical Installations	0.92	0.92	0.91	0.92
Cumulative Impact of all failure mechanisms	0.55	0.51	0.49	0.47

provides a summary of the predicted life expectancy limiting coefficients (LELC) for gypsum board partitions, hollow concrete block partitions, and hollow concrete block partitions, that considers examined failure mechanisms. All calculations were conducted for an impact coefficient (IC) value of 1. The life expectancy shortening coefficient is computed to quantify the impact of a specific failure mechanism on the component service life expectancy. The coefficient value ranges from 0 to 1, and a smaller value indicates the failure mechanism had a greater impact.

It can be observed that each failure mechanism has a slightly different effect on the overall life expectancy of components in each sample. However, the differences between the failure mechanisms are small. Additionally, this influence varies with the number of negative occupancy factors, a high-impact factor in standard deterioration patterns.

Of the three types of partitions examined, gypsum board partitions exhibit the greatest sensitivity to the influence of failure mechanisms, which emphasizes the critical importance of rigorous quality assurance (implemented by the building contractor) and quality control (supervised by the construction manager) during the construction of such partitions.

Conversely, the service life expectancy of hollow concrete block partitions is significantly impacted by the quality of their design and construction. In contrast, the service life expectancy of hollow concrete block partitions is less susceptible to failure mechanisms that stem from design or implementation errors.

To summarize, hollow concrete block partitions, known for their relatively extended service life expectancy in standard conditions, experience less pronounced effects from failure mechanisms associated with design or application inaccuracies. Moreover, the cumulative impact of all failures on component service life expectancy becomes increasingly noticeable as the presence of negative occupancy factors intensifies across all three partition types.

4.8. Summary and Discussion

This section presented a comprehensive analysis of alternative partition types in residential buildings, including gypsum boards, autoclaved aerated concrete blocks, and hollow concrete blocks. The field survey data consists of 133 samples taken from across different sites in Israel. This section presents several key findings:

• Initial quality varies by partition type, with hollow concrete blocks showing the highest initial component performance (ICP) and gypsum boards the lowest. This indicates that construction quality control is critical, especially for more vulnerable materials like gypsum.
• The deterioration rate in standard conditions is highest for gypsum boards and lowest for hollow concrete blocks.
• Considering failure mechanisms, gypsum boards again show the shortest service life projections, as hollow concrete blocks the longest.
• The cumulative impact of failures further reduces service life expectancy across all partitions.

Predicting the service life of building components is a subject that has aroused considerable interest in the fields of construction and civil engineering. Previous studies have explored different materials, environmental conditions, and maintenance strategies to estimate the longevity of building components [6,39,41], clearly contrasting with this research, which considers initial conditions, failure mechanisms, and cumulative impact. Moreover, this research recognizes that occupant behaviors can significantly influence partition durability, and also defines and presents the impacts of various occupancy conditions.

The comprehensive analysis provides a quantitative basis for selecting appropriate partition types on the basis of expected occupancy conditions, quality considerations, and desired life cycle performance.

5. Life Cycle Cost Analysis

In this section, we will focus on the economic implications of findings related to the deterioration patterns of various partitions in standard service conditions and failure conditions, as defined in this study. The analysis utilizes a life cycle cost approach that considers the costs of construction, maintenance, replacement, and operating the building components. This approach aims to maximize benefits, as defined by building users.

5.1. Lifecycle Cost Analysis in Standard Service Conditions

The analysis is based on calculating life cycle costs and the predicted service life of the three types of partitions in different service conditions, as in the Section 4 analysis.

$$NPV=C+R\times sr(i,lc)\times rp(i,k\times lc)+(M+O+T+A)rp(i,LC)-S\times sp(i,LC)$$

(7)

where,

• $C$—Initial Investment: The upfront cost required to purchase and install the partition or building component.
• $R$—Replacement Cost: The cost of replacing the component at the end of its service life.
• $M$ Annual Maintenance Cost: The yearly cost incurred to maintain the partition or building component.
• $O$—Annual Operating Costs: Yearly costs associated with the operation of the component or system.
• $T$—Taxes: Any tax-related expenses associated with the component over its lifecycle.
• $A$—Ancillary Costs: Additional costs that might not fall directly under other categories.
• $S$—Salvage Value: The residual value of the component at the end of its service life.
• $LC$—Planned Lifecycle of the Building: The overall expected duration the building is planned to last.
• $lc$—Life cycle of Building Component: The expected or average service life of the building component.
• $i$—Annual Interest Rate: The rate of interest used to discount future costs and benefits.
• $k$—Number of Lifecycle Repeats for Component: The number of times the component goes through its lifecycle.
• $rp(i,n)$—Present Value Coefficient of an Annuity: A factor used to calculate the present value of a series of payments.
• $sr(i,n)$—Sinking Fund Factor: A factor used to convert a future value sum into a series of equal present value payments.
• $sp(i,n)$—Single Present Value Factor: A factor used to determine the present value of a single future amount.

The lifecycle cost analysis was divided into four categories of negative occupancy factors (µ), as previously described. Several assumptions were also made:

• LC (Planned Lifecycle of the Building): The building is planned to last for 50 years.
• Annual Interest Rate (i): 5% per annum.
• Operating costs, taxes, ancillary costs, and salvage value: Assumed to be zero.
• Initial Construction Costs:

  ○

  Hollow cncrete block partition: 154.8 ILS/m²

  ○

  Autoclaved aerated concrete block partition: 131.3 ILS/m²

  ○

  Gypsum board partition: 94.2 ILS/m²

• Replacement costs:

  ○

  Hollow concrete block: 185.8 ILS/m²

  ○

  Autoclaved aerated concrete block: 157.6 ILS/m²

  ○

  Gypsum board: 152.7 ILS/m² (including disassembly of the old partition)

• Annual maintenance costs (M): Established as customary at 0.5% of the asset value for all types of partitions examined in the study.
• Partition lifecycle (lc): Based on the service life expectancy and performance range predicted in standard deterioration conditions, as determined.
• Component replacement: A component is only replaced if its residual life is more than half of its standard predicted lifecycle in given conditions (for instance, if the standard service life of a component is 40 years and the planned lifespan of the building is 50 years, the component will not be replaced in year 40 because its residual life of 10 years is not greater than half of its standard predicted service life).

Table 12. Summary of Partition Lifecycle Cost Analysis: Predicted Service Life Expectancy in Different Service Conditions.

Partition Type	Gypsum Board Partitions	Autoclaved Aerated Concrete Blocks	Hollow Concrete Blocks
Initial Cost (ILS /m²)	94.2	131.3	154.8
Annual Maintenance Cost (ILS /m²)	0.5	0.7	0.8
Replacement Cost (ILS /m²)	103.6	157.6	185.8
Life Expectancy [years]
Light Conditions (µ = 0)	>50	>50	>50
Moderate Conditions (1 ≥ µ > 2p)	47	>50	>50
Standard Conditions (2 ≥ µ > 4)	27	32	>50
Intensive Conditions (µ ≥ 4)	11	18	38
Lifecycle Cost [$/m²]
Light Conditions (µ = 0)	23.4	32.6	38.4
Moderate Conditions (1 ≥ µ > 2)	23.4	32.6	38.4
Standard Conditions (2 ≥ µ > 4)	29.7	40.1	38.4
Intensive Conditions (µ ≥ 4)	52.6	53.6	38.4

and Figure 15 provide a breakdown of the lifecycle costs and expected service life of partitions (made of gypsum board, autoclaved aerated concrete blocks, and hollow concrete blocks) in various service conditions.

On the basis of the analysis, the following key findings and conclusions (related to the lifecycle costs of partitions in varying occupancy conditions) have been established:

• Gypsum board partitions exhibit the lowest initial cost, while hollow concrete block partitions demand the highest investment. Comparison of the implementation of aerated concrete block partitions and hollow concrete block partitions shows the former costs approximately 15% less.
• The lifecycle costs for gypsum board partitions are the lowest across the first three levels of service conditions, ranging from light to standard. It is vital to recognize the heightened sensitivity of gypsum board partitions to an increasing number of negative occupancy factors. The service life expectancy significantly diminishes with each additional factor, and this is particularly notable in the case of standard service conditions (2 ≤ µ < 4), where it is necessary to add a replacement every 27 years, a figure that dramatically reduces to 11 years in intensive conditions. Although derived from a linear deterioration model, this indicates a rapid decline in partition integrity in harsher conditions, while highlighting the unsuitability of the gypsum board partition in such environments.
• Research findings suggest that hollow concrete block partitions are the most stable in various service conditions. The predicted service life of this type of partition exceeds 50 years in regular, moderate, or light service conditions. It is only in intensive service conditions that the partition service life drops below 50 years; however, even here the residual service life (service life after replacement) is 12 years (until the end of the building’s lifecycle), and therefore does not justify the replacement of the partition but rather its repair.
• Of the block partitions examined, hollow concrete block partitions, whose lifecycle costs are the highest in light or moderate service conditions, require the highest initial investment. whose l In standard service conditions, the lifecycle costs of concrete block partitions are similar to those of aerated concrete block partitions. In intensive service conditions, hollow concrete block partitions are the most economical choice.

On the basis of the above paragraphs, the following partition choice recommendations are made:

• Gypsum board partitions are the most economical choice in moderate or regular service conditions (µ < 4). However, their performance level in regular service conditions is significantly lower than concrete block partitions and aerated concrete block partitions. Therefore, even at a regular service level, this partition is not a good solution if the user’s planning horizon is more than 25 years.
• Block partitions, aerated concrete block partitions and hollow concrete block partitions are the partitions best-suited to functioning in intensive service conditions (accelerated wear due to a large number of negative occupancy factors). Of the block partitions examined in this study, hollow concrete blocks have a better performance in intensive service conditions (predicted lifespan of 38 years compared to 18 for aerated concrete block partitions). Although this partition has higher initial costs (154.8 vs. 131.3), the better performance of hollow concrete block partitions means their lifecycle costs are lower than aerated concrete block partitions (169 vs. 236 ILS/m²). Therefore, hollow concrete block partitions are economically preferable in these service conditions (µ ≥ 4).

5.2. Lifecycle Cost Analysis in Failure Conditions

This sub-section discusses the lifecycle costs in two failure scenarios:

• Life cycle costs impacted by each of the failure mechanisms identified in the field surveys.
• The impact of inherent construction failures, originating in flawed construction or design, on creating non-evolving defects.

5.2.1. Lifecycle Costs Influenced by Inherent Construction Defects

The findings of the analysis are presented in

Table 13. Comparative Analysis of Predicted Life Expectancy and Lifecycle Costs in Inherent Construction Failures.

Partition Type	Gypsum Board Partitions	Autoclaved Aerated Concrete Blocks	Hollow Concrete Blocks
Life Expectancy [years]
Light Conditions (µ = 0)	50<	50<	50<
Moderate Conditions (1 ≥ µ > 2)	44	50<	50<
Standard Conditions (2 ≥ µ > 4)	24	30	50<
Intensive Conditions (µ ≥ 4)	10	16	36
Lifecycle Cost [$/m²]
Light Conditions (µ = 0)	23.4	32.6	38.4
Moderate Conditions (1 ≥ µ > 2)	23.4	32.6	38.4
Standard Conditions (2 ≥ µ > 4)	30.7	40.9	38.4
Intensive Conditions (µ ≥ 4)	55.5	56.5	38.4

and Figure 16. As mentioned, inherent construction defects cause a decrease in the predicted service life of partitions. The lifecycle cost analysis of these conditions shows that gypsum board partitions are the most economical in terms of lifecycle costs, even in conditions with inherent construction defects (i.e.,when the number of negative occupancy factors is less than four). Hollow concrete block partitions have an advantage in lifecycle costs over gypsum board partitions and aerated concrete block partitions in intensive occupancy conditions (µ ≥ 4).

Comparing these findings to the lifecycle cost analysis of standard service conditions reveals that inherent construction defects significantly affect the lifecycle costs of gypsum board partitions and aerated concrete block partitions (an increase of about 6% in lifecycle costs), which clearly contrasts with their negligible impact on hollow concrete block partitions. It is evident that the lifecycle costs of hollow concrete block partitions subjected to the influence of inherent construction defects are identical to the lifecycle costs of counterparts without inherent construction defects (refer to Table 12 above). In contrast, the lifecycle costs of aerated concrete block partitions and gypsum board partitions are found to be 2–5.5% higher, when compared to equivalent service conditions where µ > 2. This finding shows that hollow concrete block partitions reduce the economic risk to the user that results from inherent construction defects in a lifecycle planning perspective.

5.2.2. Life cycle Cost Impacts on Various Failure Mechanisms

In this part of the discussion, we focus on the economic implications of findings related to the deterioration patterns of different partitions in failure conditions.

Table 14. Partition Life cycle Cost Analysis [$ per m²] Based on Predicted Service Life Expectancy in Various Failure Mechanisms and Occupancy Conditions.

Occupancy Conditions	Light	Moderate	Standard	Intensive
	µ = 0	1 ≤ µ < 2	2 ≤ µ < 4	µ ≥ 4
Gypsum board
Crack at Partition-Ceiling Junction	23.40	23.40	32.20	65.70
Crack at Partition-Wall Junction	23.40	23.40	31.80	65.70
Crack around Door Frame	23.40	23.40	31.80	65.70
Cracks on the Surface	23.40	23.40	31.80	65.70
Stability of Electrical Installations	23.40	23.40	31.80	65.70
Autoclaved Concrete Blocks
Crack at Partition-Ceiling Junction	32.60	32.60	42.60	64.40
Crack at Partition-Wall Junction	32.60	32.60	42.20	64.40
Crack around Door Frame	32.60	32.60	42.60	67.00
Cracks on the Surface	32.60	32.60	42.60	64.40
Stability of Electrical Installations	32.60	32.60	41.70	58.10
Hollow Concrete Blocks
Crack at Partition-Ceiling Junction	38.00	38.00	38.00	46.80
Crack at Partition-Wall Junction	38.00	38.00	38.00	46.40
Crack around Door Frame	38.00	38.00	38.00	46.80
Cracks on the Surface	38.00	38.00	38.00	46.40
Stability of Electrical Installations	38.00	38.00	38.00	46.40

presents a summary of the partition lifecycle cost analysis [$ per m²], which is based on the predicted service life expectancy of various failure mechanisms and occupancy conditions. Figure 17 presents a comparison of the impact of the failure mechanism on partitions in various occupancy conditions.

This analysis provides several insights:

• Hollow concrete block partitions emerge as the most stable, in terms of lifecycle costs, under failure conditions. The impact of failure mechanisms only becomes significantly noticeable in intensive conditions.
• In failure conditions and when the number of negative occupancy factors is less than four, gypsum board partitions show the lowest lifecycle costs. However, it is important to note that when the number of negative occupancy factors exceeds one, all failure mechanisms lead to at least one replacement of these partitions over a 50-year lifecycle. This factor affects the comfort of use in the dwelling, necessitating at least one refurbishment during the building’s lifecycle.
• In intensive service conditions (µ > 4), hollow concrete block partitions have the lowest lifecycle costs, which is attributable to their relatively good performance in failure conditions, compared to other partition types.

6. Conclusions

The study conducted a comparative analytical examination of three residential building partition alternatives (gypsum board partitions, aerated concrete block partitions, and hollow concrete block partitions) that evaluated these technological alternatives by referring to four main aspects:

• In standard service conditions, the predicted lifecycle entails the partitions being perfectly constructed and fully functional.
• In predicted service life, “inherent construction defects” includes the impact of flawed construction or design that leads to non-progressive defects.
• Predicted lifecycle under failure mechanisms includes scenarios where one or more failure mechanisms are identified.
• Lifecycle cost analysis of partitions in compound conditions includes standard deterioration and failure mechanisms.

In the research, four “negative occupancy factors” were recognized to affect the wear rate of partitions, and four categories of occupancy conditions (light, moderate, standard, and intensive service conditions) were subsequently identified.

Standard service conditions are the ideal scenario in which the partition is flawlessly constructed without any inherent defect or expected failure mechanism. The findings reflect the standard lifecycle of partitions for planning purposes, and point out the following:

• Typical Deterioration Paths of Partitions: All partitions show linear wear patterns with a constant deterioration rate that increases with the number of negative occupancy factors, leading to a gradual decrease in the partition performance.
• Longest Predicted Standard Lifecycle (SLE): Hollow concrete block partitions have the longest predicted SLE over 50 years in all service conditions, except when the number of negative occupancy factors is four or more. The performance and maintainability of these partitions are the most stable.
• High Service Life in Moderate or Light Conditions: In moderate or light service conditions (µ < 2), the predicted service life of hollow concrete block partitions is exceptionally high (over 100 years)—aerated concrete blocks over 72 years, and gypsum board partitions are the only ones to fall below 50 years (47 years). However, if we assume a building’s planned service life is 50 years, these differences have minimal implications for life cycle costs.
• Best Performance in Standard Service Conditions (2 ≤ µ < 4): Hollow concrete block partitions require no replacement due to their durable service life performance, compared to one replacement for aerated concrete and gypsum board partitions.
• Variability in Intensive Service Conditions (4 ≤ µ): Hollow concrete block partitions stand out with a stable predicted service life of 38 years, while others drop significantly, indicating that they are not well suited to intensive service conditions.
• Service and occupancy conditions, as defined in this research, may be adapted as design criteria for interior partitions in residential buildings. Performance criteria, as proposed here, may enhance the sustainability of residential buildings interiors.

The research analyzed inherent construction defects and evolving failure mechanisms, two categories of failure, observing a 4–10% reduction in the predicted service life due to inherent construction defects, with hollow concrete blocks showing less sensitivity than other materials. While Gypsum board partitions emerge as the most cost-effective in standard and minor failure conditions, their high sensitivity to increasing negative occupancy factors diminishes their suitability in more demanding or intensive service environments.

The life cycle cost results of the partition alternatives in different service conditions show:

• Gypsum Board Partitions as Economical Choice: In standard deterioration conditions and moderate or standard service conditions (µ < 4), gypsum board partitions are the most cost-effective choice. However, they are sensitive to the number of negative occupancy factors, especially in intensive service conditions.
• These partitions demonstrate stability across different service conditions and offer the most economical lifecycle costs in intensive service conditions.
• Built-in construction defects significantly affect the lifecycle costs of gypsum board and aerated concrete block partitions more than hollow concrete block partitions, reducing economic risks for users planning long building lifecycles.
• Gypsum board partitions are, in failure Conditions, are the most economical in terms of lifecycle costs, even in various failure conditions, although this only applies when the number of negative occupancy factors is less than four.

This research explored the lifecycle by using a methodology based on monitoring the performance of building components, producing findings that highlight the significant impact of service conditions on partition performance and deterioration patterns. The method tested here (for the first time) on building finishing components shows that their service conditions have a strong effect on predicted lifespan. This emphasizes the importance of selecting building components that are suited to the expected service conditions that will be encountered over the building’s planned life cycle, which take the user’s planning horizon into account.