Application of Smart Modelling Framework for Traditional Wooden Architecture

Abstract

Preserving ancient buildings can be improved using Building Information Modelling (BIM) models created from high-quality point-cloud data. The problems arise from the need for automatic extraction of the characteristics required to meet various security criteria from a high-fidelity point cloud. BIM for Traditional Wooden Architecture (TWA) constructions requires collaboration across various research fields. Two crucial concerns are needed to overcome the current gap and enhance the use of BIM: an automated model for the major components that smartly combines historical information and a Smart Modelling Framework (SMF) to represent these components. First, a parametric model for the usual components, highlighting similarities and properties, was created using a TWA structure as the basis. The next step is creating an automated modelling approach to determine the component type and hidden dimensions automatically. Conservation initiatives for traditional wooden structures will benefit greatly from this research results. The experimental results demonstrate that the suggested technique accomplishes better efficiency, reliability, and effectiveness than other existing technologies.

1. Introduction

For many years, the established building approach has been on-site construction, which entails bringing all necessary supplies, tradespeople, and workers to the site where the project will be built [1]. However, off-site construction has emerged as a viable alternative to the traditional on-site method as the sector has undergone varied methodologies used in building along with industrialisation [2]. The term “smart buildings” refers to structures that can be remotely operated and controlled to maximise comfort, efficiency, cost savings, and environmental friendliness throughout the building’s entire lifespan [3]. Smart buildings aim to deliver services that are secure, cost-effective, environmentally friendly, and easy to use [4]. BIM advanced machine learning are all examples of information technology that might help make a building “smart” [5].

BIM is a method for quickly conveying Information about a building’s design, construction, and maintenance to all participants using computer-generated, virtual three-dimensional (3D) models [5]. BIM has many advantages, such as reducing building time and expenses without sacrificing quality [6]. It is now common practice around the world to use cutting-edge electronics for the upkeep of historic structures.

Application of BIM methodology, BIM, in the field of architectural heritage in recent years has generated a significant catalogue of experiences classified under the title Heritage Building Information modelling, HBIM [7]. HBIM is one example of a technique that employs photo survey data with scanning using lasers to create 3D models of buildings, thereby providing the documentation required for research [8]. With photogrammetry, precise records of the present condition of timber constructions may be created. This is necessary for keeping the intricacies intact regarding historic structures. Creating high-resolution 3D models using photogrammetry allows us to examine structures in great detail and pinpoint damaged or decaying regions. The repair activities may be carried out precisely using accurate models, guaranteeing that the wooden constructions’ architectural integrity will be preserved. Light Detection and Ranging, the LiDAR, is a laser scanning method that uses light beams to measure the distance to an object’s surface. Converting the dense point cloud into precise 3D models is possible since this approach creates it from the scanned surface. By taking exact measurements, laser scanning can precisely capture the intricate geometries of wooden constructions. It can evaluate the structural integrity and track any changes or deformations using the data we obtain. Learning more about building methods and any past alterations done to the buildings using laser scanning is possible. The goal is effective and precise rebuilding and upkeep. Using drones and scanners, HBIM captures digital versions of historic sites [9]. Careful administration and preservation become possible. Therefore, developing smart buildings requires an awareness of how BIM software (Revit 2022) is employed in the construction industry [10]. These digital resources have been used successfully in archaeology and architecture to enhance management and distribution procedures with the help of technologies like virtual reality [11]. When it comes to representing and visualising data, virtual reconstruction has taken centre stage, opening up exciting new avenues of inquiry [12]. As such, BIM is a development of the conventional approach based on orthogonal projections and viewpoints incorporating additional information elements [13]. The preservation process for a cultural asset involves a wide range of disciplines and methods of inquiry, from geometric and mechanical analysis to chemistry and physics and even documentary research [14].

Today, most reports on historic structures are standalone documents, resulting from separate initiatives utilising datasets compiled by different technical groups [15]. Historic buildings can undergo various treatments, including preservation, reconstruction, reuse for adaptive purposes, preventative maintenance, analysis, research, and information management [16]. Using the BIM technique as it is here can help with architectural management, document management, and data with semantics storage [17]. Historic Building Information modelling is a cutting-edge approach to preserving and managing historic buildings [18].

The following are the paper’s primary contributions.

BIM-TWA is a computerised model of the essential components incorporating historical data.

A parametric model of the standard parts is built on top-of-a-TWA framework and emphasises shared features and characteristics. To proceed, an automated modelling strategy that can identify the component type and hidden dimensions automatically is developed by a smart modelling framework.

The experimental evidence demonstrates that the proposed method outperforms current efficiency, dependability, and efficacy technologies.

The paper is organised as follows: Section 1 provides an overview of BIM for wooden structures, while Section 2 discusses related environmental research. In Section 3, we see the Smart Modelling Framework’s proposed automated model for the framework’s primary components, which cleverly mixes the historical data. This article’s experimental investigation and conclusion are presented in Section 4 and Section 5.

2. Literature Review

The percentage of wooden buildings among cultural properties is relatively high. Studies indicate that Japan has preserved many wooden buildings that are similar to those of ancient China. In the heritage of public buildings, the coverage rate has reached 13.1% [19]. These nations are putting in extra work on creative documentation projects to assist them in managing and using their historic wooden buildings. The modelling procedure and member procedures for composing the wooden structure have been studied, and the joining of these members has been analysed concerning the traits of traditional East Asian architecture.

This paper follows a sequence proposed by Moyano J. et al. [20], which begins with data collection via remote sensing techniques [DC-RST] and continues with the incorporation of Information stored in a point cloud into a BIM environment, the application of ontologies to HBIM (Historic BIM), data categorisation, and the development of the concept of the Digital Twin (DT). In addition, case research involving building a column foundation for the church is analyzed in this research. This groundbreaking and cohesive effort offers all the necessary components for data gathering, analysis of complex geometries, constructing the parametric model with semantic enhancement, and exchanging among all essential experts in open-source IFC formats.

In the work of Darko, A. et al. [21], modular integrated construction risk management (MiCRM) has aided using BIM and related digital technologies. The primary concept of integrating BIM and STTs for MiCRM is still in its early stages. Along these lines, BIM-RFID integration has been extensively studied, while BIM-GIS integration has been mostly ignored. Using a critical lens, this research will examine BIM-based MiCRM [BIM-MiCRM] and suggest filling in knowledge gaps and pointing researchers in the right direction. This was achieved by the study team’s methodical identification and review of pertinent papers from four angles. (1) MiCRM via BIM alone; (2) MiCRM via BIM in conjunction with monitoring and detection systems; (3) MiCRM via BIM in conjunction with devices for creating and comparing 3D models (3D-MCCTs); and (4) other applications.

Youn, H. C. et al. established mesh modelling [MM] [22] is undertaken utilising data from a point cloud of the complete Seoikheon edifice of Jeonju Pungpajigwan, a national cultural property of Korea. [MM] is related to 3D building modelling. A detailed Rhino 3D model, complete with a set of bracket joints, was made possible by deconstructing the construction, scanning members, and comparing data stored in the cloud. Therefore, classic wooden buildings can have a 3D model created in Revit that accurately represents their distinctive forms and qualities.

Use relational databases and time characteristics to establish a BIM-related 4D-[HBIMM] model for deterioration detection and data calculation settlement. Bruno, S. et al. [23] presented a proposed operating approach for managing the knowledge system about historical buildings. Parameters in a BIM describe how old the structure is, what kind of construction it used, and how bad it is regarding fractures and decay. These details are relevant to those who research large groups of buildings for patterns in cracking and decay and those who research the dynamics between individual structures.

There are three dimensions to the paradigm that Yang, A. et al. [24] presented and proposed for the intersection of BIM and smart buildings [IBIM-SB]. These dimensions are the characteristics of BIM, the phases of a project, and smart properties. Based on these three dimensions, this article delves into three main points: (1) the benefits of BIM for achieving different levels of smartness; (2) the many smart building stages in which BIM is applied; and (3) the intelligent building functions that may be executed using BIM. This research discusses the synthesis of BIM and utilises a cross-analysis of research on smart buildings in three dimensions. To ensure that building data are accessible and useful by non-digital expert operators via user-friendly services, Angelo Massafra et al. [25] proposed decision support systems (DSS). Using a simulation-based application on the legacy case study of the Faculty of Engineering in Bologna, Italy, we describe the strategy used to construct the digital DSS and illustrate the process. Using a data visualisation method, the investigation delves into the building’s energy performance at the space and hour scale, investigating its link with the expected occupancy. Future expansions to additional technologies and data, such as live sensor readings, occupant input, and forecasting algorithms, may be built upon the foundations of conceptualising the DSS inside a digital twin vision.

Kang et al. [26] proposed the parametric modelling technology for applying HBIM to Korean traditional wooden architecture. The primary objective of this research is to construct a single structure from the provided blueprints using a maximum of parametric models, with the help of scanning models for more complicated shapes that cannot be accurately modelled using parametric methods. Afterwards, different kinds of joints were categorised using pictures and literature, and the specifications of each member were constructed and organised so they could be used in the construction model. Unlike Scan-to-BIM, this study’s script-based technique can create low-capacity models and visualise different restoration strategies for non-existent architectural heritage and virtual reconstruction of traditional timber buildings. In addition, it may be used as a technique for procedural modelling to construct structures in historic areas, taking into account the terrain around them. It can also be applied mechanically to curved surfaces over time.

Problems with the current model are worsened when tried-and-true methods like DC-RST, BIM-MiCRM, MM, HBIMM, and IBIM-SB are applied. Hence, to optimise efficiency, reliability, and effectiveness compared to other existing technologies and criteria, the BIM-TWA has been proposed as a safe and efficient option. The proposed model will be briefly discussed in this article.

3. Building Information Modelling for Traditional Wooden Architecture (BIM-TWA)

3.1. An Overview of Smart Buildings and Building Information Modeling (BIM)

Currently, neither BIM nor smart buildings fully address the unique challenges of preserving traditional wooden structures. The proposed framework BIM-TWA fills the current gap by integrating high-fidelity point-cloud data with BIM to create detailed and accurate models. Two crucial concerns are needed to overcome the current gap and enhance the use of BIM: an automated model for the major components that smartly combines historical information and a Smart Modelling Framework (SMF) to represent these building components. This section presents the research’s definitions of smart buildings and BIM, which examines these concepts’ ideas as other academics put forth. According to the Intelligent Building Institute, smart buildings can combine different systems to manage resources to maximise flexibility cooperatively, cost savings on investments and operations, and technical performance. The concept makes several practical assumptions regarding the future uses of smart buildings. Smart building definitions are expanded upon based on the definition put forth by the Smart Building Institute (IBI).

To enhance our understanding of smart buildings within the context of this article, it is essential to delineate their capabilities from the perspective of various smart functions. The term “smart buildings” here refers to future structures designed to deliver sustained value and meet occupant needs through the integration of numerous advanced technologies while still conforming to established smart building standards. BIM plays a crucial role in this by providing a framework for intelligent building capabilities, which are reflected in its attributes. Previous research has identified key functionalities of BIM, including the integration of information across systems, visualisation of models, facilitation of collaborative efforts, and other practical applications. These elements collectively enhance the operational efficiency and adaptability of smart buildings, ensuring they remain responsive to both current needs and future developments.

The goal of smart building with BIM is depicted in Figure 1. Among smart buildings is smart construction. By utilising a 3D geometric model and abundant data, BIM can accomplish smart construction goals by visualising and simulating the building process. Studies have already demonstrated BIM technology’s practical and financial benefits in building, opening up new avenues for more sophisticated and intelligent construction. During the construction process, time, budget, quality, security, and environment are among the goals of smart buildings. The five objectives of smart construction, based on BIM and related literature, are displayed in Figure 1. The literature frequently analyses cost and time goals in tandem since they are closely related. The most fundamental objective throughout the building stage is safety. Objectives related to quality and the environment are equally crucial for smart buildings. The method’s main objective is to find and gather construction-related data systematically. BIM, a technique with three submodules, is used for bridges. The initial module is the approximation estimation module. Multiplying the element count from the BIM by the unit price is what the module does. The second module deals with detailed estimation. This module considers labour and equipment rates, various material costs, productivity estimates, and the quantity of elements extracted by BIM. The effectiveness measurement module is the third module. Combining planned and actual values, the fourth module finds the earned value, determining the budget and timetable condition. Generally speaking, there are two benefits to employing BIM throughout the life cycle of smart buildings. On the one hand, BIM facilitates information sharing and interchange by offering 3D visual representations. As a result, BIM can help stakeholders collaborate and communicate data amongst technologies used in other disciplines.

3.2. Application of BIM in Traditional Wooden Architecture

The choice to employ digital technologies for accurately depicting the behaviour of traditional wooden structures has been driven by a growing recognition of their relevance and maturity in recent times. Particularly in the fields of archaeology and architectural heritage, numerous studies and applications focus on modelling complex shapes, surfaces, and spaces, as evidenced in references [27], and extending to educational dissemination [28]. Traditional wooden structures, as significant cultural heritage assets, face unique challenges in their preservation and management. The introduction of Building Information Modeling (BIM) technology offers new possibilities for addressing these challenges. When applied to traditional wooden structures, this practice is referred to as Historical Building Information Modeling (HBIM). HBIM leverages laser scanning and photogrammetric data to create 3D models of buildings, capturing essential information for analysis as cited in [29]. Predominantly developed in the West, the foundational 3D scanning and photogrammetry technologies underpinning HBIM have been robustly applied in the structural analysis of wooden trusses, HBIM workflows [30], and lifecycle data management of existing wooden structures [31]. Additionally, research on 3D modelling of wooden structures incorporating various data types [32] often relates more to architectural spaces than to detailed cataloguing and joint methods. A major direction in the use of HBIM for traditional wooden structures is not only to utilise precise measurements from 3D scans but also to consolidate and manage previously dispersed information relevant to cultural heritage, such as maintenance histories, into a unified 3D model. Given wood’s susceptibility to deform and deteriorate over time, more meticulous records are necessary. Consequently, alongside computer-aided design software, 3D laser scanners and computer programs are now employed for geometrical measurements to accurately record the current appearance of wooden architectural elements [33].

3.3. The BIM-TWA Framework: Enhancing Traditional Wooden Architecture

Figure 2 depicts a BIM-based smart building system. Therefore, to fulfil people’s desire for intelligent work and living, more sophisticated technology must be adopted immediately to actualise the smart features added to buildings in recent years. As a result, smart building features are always being developed and enhanced. Four factors are the main forces behind the ongoing development of smart buildings: lower energy consumption, higher economic efficiency, enhanced user well-being, and integration with other technology. BIM has received much attention for offering smart building solutions and further encouraging advancements in smart building technology. This section examines the latest BIM applications in the smart building field from the abovementioned angles.

$$SMB=k_{1}LEC+k_{2}HEE+k_{3}QOL+k_4 IOT$$

(1)

The above Equation (1) shows that $SMB$ denotes the smart building development attributes where $k_1, k_2, k_3, k_4$ are the constants for each attribute, as shown above. $\mathrm{L}\mathrm{E}\mathrm{C}$ denotes lower energy consumption, $HEE$ denotes higher economic efficiency, $QOL$ denotes the quality of life, and $IOT$ denotes the integration with other technology.

$$E_y^n=k_1{P}_y^n+K_2^n\left(R_y^{max}-{Eff}_y^n\right)+K_3^n\left(R_y^{max}-{Eff}_y^n\right)$$

(2)

$P_y^n$ is the notation for the detection of precision vectors while $R_y^{max}$ is the notation for the reliability and ${Eff}_y^n$ is the effectiveness and $E_y^n$ is the efficiency in Equation (2). $k_1$, $k_2$, $K_3^n$ denotes the constants for each building taken into consideration. Efficiency is achieved with the above Equation.

$${OPE}_N=\frac{\frac{1}{n}\sum _{x=0}^n{\left({DL}_{n_n-n_r}\right)}^2+\mathrm{l}\mathrm{o}\mathrm{g} {\left({IL}_{n_n-n_r}\right)}^2}{S_N}$$

(3)

In Equation (3), the quantity represented by ${OE}_N$ is the operational efficiency which can be considered a function used to assess the proposed modification. The number n represented the buildings and corners of the proponent. The notation for expressing direct link (DL) and indirect link (IL) respectively. $S_N$ be the sensor nodes if the cloud is used for its construction.

A MiC project’s risk management process, or MiCRM, entails planning, identifying, analyzing, responding, implementing, and monitoring risks. MiC initiatives are one-of-a-kind endeavours with several distinctive characteristics, like off-site production. MiC is riskier than traditional buildings because of the tremendous hazards that come with such intricacy. Early risk assessment and identification are essential for effective risk management when risks cannot be completely removed. An integrated design, production, and construction system for MiC projects is created using a smart modelling framework and a BIM, as shown in Figure 3. Recycling/landfill, deconstruction, operation and maintenance, and logistics phases were not integrated; this research helps optimise the MiC supply chain. Risks can be categorised into quantitative and qualitative categories, and there are several methods for recognising and analyzing them. Included in the former are risk indices, environmental risk assessments, etc. The latter uses spreadsheets, SWOT analysis, and other tools. These methods have been used in both conventional construction and risk management. Traditional construction is fundamentally different from MiC. There is a clear distinction among the parties engaged. There are more parties involved in MiC; manufacturers are one example of these parties. It has more phases as well. It offers a MiCRM architecture that promotes stakeholder cooperation to manage risks systematically.

$$IoT=C+\frac{\sum _{k=1}^N{ IS}_{ij}({[f-R_j]}^{2/})}{\sum _{k=1}^N ({[f-R_j]}^{2/})}-n k=1, 2, \dots \dots \dots n \mathrm{and} j=1, 2\dots \dots \dots n$$

(4)

$IoT$ indicates the automation technology C and n indicate the total number of buildings and links between each, respectively, from Equation (4) above, which help in robustness in dynamic environments. ${IS}_{ij}$ is the infrastructure component, where $f$ is the frequency of each part, and $R$ denotes the recycling of the deconstruction site. Reliability is achieved from the above Equation.

The final piece of Information uses a comprehensive strategy for BIM-based MiCRM. As Figure 4 illustrates, six dimensions, from 3D to 8D, are present in BIM. Research on BIM-based MiCRM focuses mostly on the first three Ds: scheduling, cost prediction, and visualisation; management of facilities, sustainability, and safety hazards receive less attention. Project design and planning is the phase that involves scheduling, cost estimation, and visualisation. This phase receives much attention, as noted. Few studies use BIM to control hazards throughout the MiC project’s facilities management phase. This could be explained by the belief that design, manufacturing, logistics, and on-site construction are the areas where MiC has had the biggest impact; as a result, attention may have been slanted in other directions.

$$F_M=(\left(1-{SP}_B\right)\times \left(1-{SF}_T\right)\times \left(1-{SP}_D\right))$$

(5)

From the above Equation (5), $F_M$ is the probability of successful facilities management over the site, ${SP}_B$ is the probability of a site being blocked, ${SF}_T$ is the probability of a failed transmission over the site, and ${SP}_D$ is the probability of a site being discarded after infinite retries. Accuracy is enhanced with the above Equation.

$${SF}_T= {SP}_B\sum _{x=0}^N{e}^{{-\left(F_M-S-Sus+oper\right)}^2}$$

(6)

The above Equation (6) shows that ${SF}_T$ indicates the probability of a failed transmission, which may be due to reasons such as $F_M$ being the facilities management issues, $S$ being the safety, $Sus$ being the site’s sustainability, and $oper$ being the operational efficiency. Security is achieved in the above Equation.

Hence, to improve the sustainability and safety of MiC projects, more research is required on the use of BIM. One suggestion is to analyze how BIM can monitor worker safety while large, heavy modules are installed. MiC is widely recognised as a sustainable technique since it lessens the building’s effect on the environment by reducing waste, noise, and dust. This knowledge could play a role in the incomplete attention to sustainability risks. It is important to remember that sustainability encompasses more than lowering waste, noise, and dust. It addresses more general topics like CO₂ emissions. To greatly increase the likelihood of a MiC project’s success, a comprehensive BIM-based MiCRM approach is required to address all potential risks.

Organising the suggested systematic strategy to create an HBIM model for this architectural restoration project (Section 1) and the historical framework regarding the chosen (Section 2) were helpful. Figure 5 illustrates how to structure a digital environment that meets the goals of each phase and can streamline the knowledge and intervention processes. The foundations for an effective process considering this methodology have established the building’s preservation, upkeep, and historical data access. Making precise replicas is impossible since restoration interventions depend on the decisions taken by the designers and engineers for each building.

$$PE=AP+PL+CS \log DC$$

(7)

Equation (7) shows the $PE$ as the project environment, $AP$ as the analysis phase, $PL$ as the level of the project, $CS$ as the construction stage, and $DC$ as the data collection at the given input level.

Two primary parallel contexts comprise the overall approach:

(1)

The project environment, which includes the analysis of the architectural heritage asset’s current state as well as earlier interventions through the phases of analysis, project level, and construction;

(2)

The modelling environment organises the data gathered and specified in the project environment into a current HBIM model using 3D reality-data capture. The phases of analysis, evaluation, historical summary, and data processing outputs comprise the project environment.

$$ME=\frac{DG+DS+3\varnothing \times \ln dc }{B+M+ 3D\varnothing -SI}$$

(8)

$\mathrm{M}\mathrm{E}$ denotes the modelling environment, where it combines $DG$ data gathering, $DS$ is the specification of data, $3\varnothing$ is the 3D reality, and $dc$ is the data capture. $B$ be the building, $M$ be the modelling, $3D\varnothing$ be the 3D model, and $SI$ be the semantic Information as per Equation (8). Performance is achieved greatly through this equation.

Semantically classifying and BIM the point cloud are the tasks of the data processing phase. This prevented a lack of data during the collection process by covering the full inquiry region. Specifically, the TLS device made it possible to acquire geometry with accuracy, calibrated and scaled using control points captured with devices (i) and (ii); an included high-resolution camera made it possible to map colour onto the point clouds using HDR pictures. Semantically classifying the heritage components into architectural typologies corresponding to particular historical periods is a concurrent undertaking. The structure is found to create the topological linkages between the objects, the representation of each separated object’s geometries, and the semantics that divide the objects into elements.

For wooden beams, the bending stress $\rho$ can be computed using the following Equation:

$$\rho =\frac{N·x}{J}$$

(9)

As shown in Equation (9), where $N$ denotes the bending moment, $x$ indicates the distance from the neutral axis to the outermost fibre, $J$ represents the inertia of the beam cross-section.

The maximum deflection ${\delta }_{max}$ of a simply supported wooden beam under a central load can be computed using:

$${\delta }_{max}=\frac{Q·L^3}{48·E·J}$$

(10)

As inferred from Equation (10), where $Q$ represents the Applied load, $L$ denotes the Length of the beam, $E$ indicates the Modulus of elasticity of wood, $J$ symbolises the inertia of the beam cross-section.

The proposed framework provides a multi-dimensional assessment methodology. From smart building attributes (Equation (1)) to specific structural performance (Equations (9) and (10)), it covers a wide range of technical, managerial and engineering dimensions to ensure the comprehensiveness of BIM-TWA. Equations (5) and (6) pay special attention to risk management aspects, which are crucial for the conservation and maintenance of traditional timber frame buildings. They can help identify potential problems and develop preventive measures. This system of formulas reflects BIM-TWA’s efforts to find a balance between preserving the historical value of traditional timber structures (e.g., structural analysis through Equations (9) and (10)) and introducing modern smart technologies (e.g., smart building and IoT concepts in Equations (1) and (4)). By regularly applying these formulas for evaluation, the BIM-TWA system can establish a mechanism for continuous improvement. For example, the results of the efficiency assessment in Equation (2) can be used to continuously optimise the project environment in Equation (7).

3.4. The Implementation Process of BIM-TWA

The implementation process of BIM-TWA is an integrated approach that encompasses several key steps, each crucial for preserving the architectural integrity and heritage value of traditional wooden structures. Figure 6 shows the flowchart of the proposed BIM-TWA model. The construction information modelling for Heritage is the focus of the following phase. This stage prepares the modelling environment for the next one by producing a 3D (point cloud and mesh) model that will be used to create an accurate DT for the architectural element and a parametric BIM later on. A worldwide point cloud is generated from the TLS data, which is then divided into morphological units following the heritage components’ semantic classification into elements using an expert-defined spontaneously constructed tree of classes based on geometry, morphology, substance, and past interventions. The modelling procedure may occasionally be laborious due to the unique objects and shapes. Because of this, the suggested BIM workflow uses a complicated mesh created from the point cloud using modelling programs like Rhino or MeshLab. The mesh’s transformability into a normative element and compatibility with the BIM platform makes this possible. The scientific community faces a hurdle in accurately parameterising CH structural components with complicated geometrical elements, such as ornamental flowers, organic shapes, and mythologically-inspired sculptures that are part of the building’s heritage.

Smart point clouds represent a novel data platform that addresses the underutilisation of extensive discrete spatial information inherent in active remote sensing technologies, which has traditionally hindered effective data mining [34]. Point cloud models excel in capturing the intricate geometric details of cultural artefacts. They are primarily composed of numerous spatial points (x, y, z) and serve as the fundamental source for constructing complex three-dimensional models in the domain of cultural heritage [35]. The application of three-dimensional point cloud data has been extensive, encompassing the digitalisation of archives [36], high-fidelity physical replication of artworks [37], geometric computation and analysis [38], virtual restoration [39], and cultural heritage monitoring [40]. Consequently, point clouds are an essential raw data type for supporting the management and preservation of cultural heritage. Over the past two decades, three-dimensional laser scanning technology has provided a technical means for the high-precision digitisation of artefacts. This technology detects complex geometric shapes with high accuracy, offering more detailed geometric information and surpassing the capabilities of other traditional techniques [41]. This enhanced precision and comprehensive data capture make three-dimensional point clouds indispensable for advanced analyses and interventions in cultural heritage conservation.

Three-dimensional (3D) digital technologies have become crucial for the preservation of TWA, regardless of whether the structures are well-preserved or have experienced some degree of degradation [42]. This significance arises from the technology’s ability to capture high-fidelity information. The 3D digital models created using this technology provide an intuitive representation of distinctive architectural techniques and historical cultural values, making them indispensable in the documentation and study of architectural heritage.

4. Results

Participants in the renovation can see a dynamic display of the building’s life cycle, from the years of original construction to the correlation between the years of changes made to each depicted constructive unit because of the simulation. The simulation supports diagnostic procedures to identify the root causes of degradation and settling and provides an overview of the present conservation status and constructive evolution. The data are from the point cloud segmentation kaggle dataset [43]. The Semantic3D project provided this labelled point-cloud dataset. There are seven categories assigned to the dataset’s billions of XYZ-RGB points. The data are presented in raw ASCII files with seven columns: X, Y, Z, Intensity, R, G, and B. The labels indicate several types of landscape: artificial terrain, natural terrain, high vegetation, low vegetation, buildings, hardscape, scanning artefacts, automobiles, and an eighth class that is unlabeled. Figure 7 shows the Case Research for Experimental Analysis.

4.1. Comparative Analysis of Proposed and Existing Methods

To evaluate the efficacy of BIM-TWA, we conducted a comparative analysis between it and other existing methodologies. A comparison between the suggested strategy and the current methods for the various datasets under consideration is displayed in

Table 1. The comparison between the suggested strategy and the current methods.

Number of Samples	DC-RST [20]	BIM-MiCRM [21]	MM [22]	HBIMM [23]	IBIM-SB [24]	BIM-TWA
10	12.9	41.3	50.4	55.9	57.2	60.1
20	15.4	43.9	52	59	59.3	62.2
30	19.1	45.2	53.5	63.5	63	64
40	21.7	45.7	55.1	67.3	66.1	68.9
50	24.4	48.5	56.1	69.1	69.5	74.4
60	25.8	48.6	58.7	70.9	72.2	76.6
70	29.2	50.4	59.9	71	74.3	77.9
80	30.1	55.6	63.7	74.3	76.7	79.6
90	33.7	57.8	67.4	78.9	77.9	80.2
100	36.6	59	69.1	79.5	80.7	83.3

. The research presented here illustrates how collaboration across multiple areas is necessary for BIM for TWA builds using an upgraded BIM model and security analysis. The output ratio is examined, and the efficiency, reliability, security, performance, and accuracy are simulated against other methods using the SMF. The table compares the performance of six different Information Modeling methodologies, including the suggested BIM-TWA strategy, based on their efficiency and effectiveness in handling various samples. The values represent the performance scores for each methodology, with higher values indicating better performance. The BIM-TWA approach consistently scores the highest across all sample sizes, demonstrating its effectiveness in accurately modelling and preserving traditional wooden structures. This performance is attributed to its specialised focus on traditional architecture, detailed material data integration, and comprehensive historical context.

4.2. Accuracy Analysis

Figure 8 shows the protections against accuracy analysis ratios for samples. Compared to other existing methods, accuracy is higher. It is imperative that the building design, which is the data, be transferred via a network to stop damage brought on by infiltration. More data accuracy is obtained than with state-of-the-art techniques. Accuracy is enhanced with Equation (5).

4.3. Security Analysis

Figure 9 illustrates how useful Security analyses are taken into account. A range of samples is displayed on the x-axis against the effective analysis ratios depicted on the y-axis. Therefore, we collected samples at different ratios to test each hypothesis. With BIM-TWA, this model performs better than current methods regarding Security and higher standards. Security is achieved in Equation (6).

4.4. Efficiency Analysis

After the samples were gathered, the data were examined for effectiveness. Figure 10 shows the efficiency analysis’s findings. The efficiency analysis ratio is displayed against the sample size on a graph. This could allow for more efficient data design than in the past. Forecasts of the results can be generated by analyzing a content strategy for effective data analysis related to advancement. This model outperforms existing approaches regarding efficacy and higher standards because of BIM-TWA implementation. Efficiency is achieved with the Equation (2).

4.5. Reliability Analysis

Following sample collection, the data are examined for dependability. The reliability analysis’s findings are presented in Figure 11. A graph is created by plotting the reliability analysis ratio versus sample size. With this, building more dependable data than ever might be possible. It is possible to produce outcome projections by analyzing a content strategy for dependable data analysis related to advancement. This model achieves higher standards and greater reliability than existing approaches using BIM-TWA. Reliability is achieved with the help of Equation (4).

4.6. Performance Analysis

After sample collection, the performance of the data is analysed. Figure 12 presents the results of the performance analysis. The performance analysis ratio is plotted against sample size to create a graph. This could allow for the construction of more reliable data than before. Analysing a content strategy for reliable examination of advancement-related data might yield outcome estimates. With BIM-TWA, this model outperforms previous methods in terms of performance and standards. Performance is achieved greatly through Equation (8).

The experimental results show that the suggested BIM-TWA model increases the accuracy ratio by 98.7%, the efficiency ratio by 97.5%, the reliability ratio by 96.4%, and the performance analysis by 95.6% compared to other existing models. The BIM-TWA performs better than the existing DC-RST, BIM-MiCRM, MM, HBIMM, and IBIM-SB models. The paper states that this BIM-TWA is being built to increase data security and efficiency in light of the previously mentioned rivalry.

5. Conclusions and Discussion

This paper presents the BIM-TWA using essential components incorporating historical data. With the rise of BIM, new possibilities have opened for creating smart buildings. This research examines relevant journal papers using BIM in smart buildings and pertinent BIM reports and standards. This research identifies and examines the relationship between BIM and smart buildings from three angles: project phases, smart qualities, and BIM attributes. Finding the nexus dimensions shows several crucial and connected features of the smart building using Building Information modelling. Improved BIM capabilities in areas such as intelligent technology integration, life cycle management, and comprehensive function design will be a direct outcome of this study. The methodology shows promise in combining data and information from surveys, archival and bibliographic sources, recollections, and customs; however, further development is needed to enable BIM’s information management and retrieval capabilities; this knowledge may be automatically analyzed to help identify the actual reasons. This paper introduces the system, a fresh method for managing historical building knowledge by assembling an interchangeable workflow of commercially available BIM tools. The advantage of information management is its assistance in determining primary causes through database queries and proposing suitable solutions. The goal of this case research is semantic enrichment and effective non-graphical information storage and analysis rather than precise geometric model development. Therefore, the modelling process can be improved to create a strict HBIM model.

The experimental results show that the suggested BIM-TWA model increases the accuracy ratio by 98.7%, the efficiency ratio by 97.5%, the reliability ratio by 96.4%, and the performance analysis by 95.6% compared to other existing models. However, staying true to the primary goal of the research, the connection between the model with parameters and the three-dimensional model created by photography (mesh with textures) makes it possible to comprehend three-dimensional geometry and surfaces with accuracy. In the future, a database about previous implementation methods for each architectural element will be integrated; this is an additional factor that may contribute to settling and decay. Moreover, more investigation may reveal a technique for gathering and logically connecting data and information, enabling an automated process that identifies problems and suggests solutions.

The BIM-TWA evaluation framework proposed in this study constitutes a comprehensive framework for evaluating and optimising BIM-TWA systems through a series of interrelated formulas. This framework demonstrates its multi-dimensional, scalable and comprehensive characteristics, covering multiple dimensions from smart building attributes to specific structural performance, and has good scalability to adapt to future technological developments. Through the combined application of these formulas, comprehensive information support can be provided to decision-makers, especially playing an important role in risk management and performance optimisation. Together, these formulas provide decision-makers with comprehensive information to identify potential risks and perform performance optimisation. Notably, the framework seeks to strike a balance between preserving the historical value of traditional timber structures and introducing modern smart technologies, reflecting the core philosophy of BIM-TWA. This system of formulas reflects BIM-TWA’s efforts to find a balance between preserving the historic value of traditional wood construction and introducing modern smart technologies. All of these formulas rely on accurate data input, emphasising the importance of data-driven decision-making. By promoting data-driven decision-making and interdisciplinary integration, this assessment system establishes a mechanism for continuous improvement. The formulas involve multiple disciplines, reflecting the fact that BIM-TWA is an interdisciplinary field that requires the collaborative work of experts from different professional backgrounds. By regularly applying these formulas for evaluation, the BIM-TWA system can establish a mechanism for continuous improvement. Overall, this set of formulas not only provides tools for assessing and optimising BIM-TWA systems but also reflects the complexity and multi-dimensional nature of the field. Together, they form an integrated framework that can balance traditional preservation and modern technology, support data-driven decision-making, and facilitate interdisciplinary collaboration. The application of this framework will help to improve the efficiency of the conservation and management of traditional timber-framed buildings while ensuring that their historical values are respected and preserved.