Application of Hybrid MCDA Tools for Constructability Review in Infrastructure Projects: A Bridge Case Study

Abstract

Constructability assessment is a critical component of infrastructure project management, with substantial implications for a nation’s economic growth. Despite significant advancements in this field over the past decades, the development of a comprehensive constructability assessment model remains an ongoing challenge, particularly concerning the integration of all critical elements throughout the infrastructure project life cycle. A significant gap exists in the availability of a systematic Constructability Review Process (CRP) to generate customized decision matrices tailored to the unique specifications and requirements of individual projects. To address this challenge, this study identifies and evaluates the most critical constructability factors through an extensive literature review, followed by an online survey to quantify their relative importance. Based on these insights, a novel Decision Support System (DSS) is proposed which leverages a hybrid Multi-Criteria Decision Analysis (MCDA) framework for the constructability assessment of large-scale infrastructure projects. The application of the proposed methodology is demonstrated through a case study of a large-scale bridge project in Australia, providing a practical example of its efficiency. The results showed that the proposed framework can enhance decision-making processes in the complex conditions and not only to mitigate the risks of failure by applying CRP to evaluate project options based on specific criteria and constraints in the feasibility stage, but also improve the project outcomes by enabling designers and decision-makers to choose the best alternative.

1. Introduction

Recent technological advancements have significantly revolutionized the construction industry, leading to remarkable developments in construction and urban planning techniques. At the same time, the increased focus on aesthetics in urban design and projects becoming more complex at the construction site level demonstrates the industry’s demand for higher standards of design and functionality. However, the scale of failed or abandoned construction projects reveals the essential need for enhanced approaches to project planning and execution [1].

In recent decades, constructability assessment has become a key concept in the management of infrastructure projects, encouraging higher levels of knowledge and techniques that contribute to the greater effectiveness of design and construction processes. By prioritizing constructability assessments, contractors can deliver better value for money while achieving project objectives [2]. In particular, the early phases have a great impact on the overall project costs and success [3], so each construction project should be analyzed to optimize the systems and methodologies of its construction processes.

In the context of transportation infrastructure, which plays a vital role in the socioeconomic development of countries, a well-designed and efficiently constructed road and bridge network is essential for maximizing economic benefits [4]. The importance of constructability assessment is evident in several infrastructure projects, such as Nigeria's road construction project [5], where insufficient constructability analysis has led to widespread road defects, including extensive cracks and potholes that impair transportation systems [6].

Despite improvements in constructability assessment research, the development of a comprehensive constructability assessment framework remains a continuing problem. Existing approaches often lack the systematic integration of critical factors during the project life cycle, limiting their effectiveness in addressing the special specifications and requirements of individual projects. A key limitation in current practices is the lack of a practical Constructability Review Process (CRP) that enables the generation of customized decision matrices. This gap underscores the need for new decision-making methodologies that incorporate the diverse and often conflicting criteria inherent in the initiation of infrastructure projects, which typically occurs in the feasibility stage.

While Decision Support Systems (DSS) and Multi-Criteria Decision Analysis (MCDA) have proven effective in addressing the complexities of construction decision-making, their application is still limited by the inability to fully account for the interdependencies among various criteria and the dynamic nature of project requirements. MCDA, as a generalized framework for tackling complex decision problems, enables decision-makers to consider multiple, conflicting criteria and facilitate trade-offs between various objectives and constraints, both quantitative and qualitative [4,7]. Recent advancements in integrated MCDA methodologies have enhanced the capacity to balance these conflicts and identify optimal solutions across a wide range of construction scenarios, particularly in large-scale infrastructure projects [8,9]. However, many of the newer MCDA methods, such as the Best-Worst Method (BWM) [10], Stepwise Weight Assessment Ratio Analysis (SWARA) [11], and Combined Compromise Solution (CoCoSo) [12], are still in their developmental stages and have not yet been widely established as reliable and validated tools for infrastructure decision-making.

Through an extensive literature review followed with an online survey of stakeholders, this study introduces a practical solution toward this current challenge of CRP by identifying and ranking the most critical constructability factors. A new robust DSS framework using a hybrid MCDA methods for constructability assessment is proposed based on these insights. This framework helps achieve this by systematically assessing project options, integrating relevant data and criteria into an MCDA evaluation matrix, enabling asset managers to balance competing priorities and validate project selections against specific criteria and constraints.

The study begins with an extensive literature review to identify knowledge gaps and practical considerations in constructability. This is followed by a structured questionnaire survey of domain experts to identify the most significant criteria for a robust DSS. Based on these insights, a novel DSS is proposed, leveraging a hybrid MCDA framework for constructability assessment. To demonstrate the practical application of the proposed methodology, a real case study of a large-scale bridge project in Australia is evaluated. The results highlight the efficiency of the proposed system in enhancing decision-making processes, improving project outcomes, and supporting designers and decision-makers in evaluating project alternatives. The key novelties of this research are as follows:

• Application of the CRP at the feasibility stage of infrastructure projects, addressing a gap in current practices and contributing to early-stage decision-making.
• The introduction of a hybrid DSS model that integrates the most suitable MCDA tools with innovative strategies to evaluate constructability criteria comprehensively.
• Validation of the proposed DSS through application to a real-world case study, representing how to customize the decision matrix and also demonstrating its practicality and effectiveness in optimizing decision-making processes for large-scale infrastructure projects.

The outline of this paper is arranged as follows: Section 2 is dedicated to presenting a comprehensive literature review on the CRP and DSS methods. In Section 3, the proposed methodology will be presented. In Section 4, the evaluation results of a real bridge case study will showcase the proposed technique’s efficiency. Finally, the discussion and conclusions are outlined in Section 5 and Section 6, respectively.

2. Related Work

2.1. Constructability Review Process

The concept of constructability has gained increasing significance in addressing gaps in project criteria beyond the ease of construction since its inception in 1986. Constructability is considered a systematic evaluation process to select the most suitable options proposed by designers or clients, integrating buildability considerations into the early construction phases. Researchers have continually refined the definition of the constructability concept to correspond with evolving aspects of project management [13].

The success of a Constructability Review Process (CRP) is contingent upon four primary elements [14]: (1) Conducting CRP throughout all stages of the project life cycle, (2) Engaging team members with specialized expertise in constructability assessment, (3) Identifying key factors critical to the application of CRP, and (4) Establishing dedicated tools or strategies for CRP deployment.

CRP offers various advantages, such as reduced uncertainty about investing in a construction project and lower risk of failure. Therefore, it improves risk management and productivity performance during the construction and delivery phases by effectively detecting and mitigating unforeseen problems as early as possible [1,15]. Furthermore, efficient resource utilization, including personnel management for future projects, contributes to cost savings by minimizing overhead expenses [3].

In addition, CRP promotes productivity improvements by ensuring timely project delivery and minimizing delays caused by design flaws or suboptimal decisions during the feasibility stage [2]. This mechanism is crucial for managing design complexity, resulting in more reliable designs and the avoidance of poorly conceived structures [16]. Ultimately, CRP reinforces project management by delivering high-quality results and achieving the project objectives while contributing to improved stakeholder communication and collaboration, which also are beneficial to better industrial relations, customer and employee satisfaction, and overall project performance [14]. Given all its potential, CRP is increasingly implemented through Decision Support Systems (DSS) to optimize decision-making and streamline constructability assessments.

Indeed, with the extensive review of CRP trends and after delving into the various methods that have been employed for the last two decades, a clear gap still stands out, not only in developing a new robust database with an innovative point of view, but also concentration on infrastructure projects. This research has attempted to present a strategy in CRP rather than only enhancing or providing a novelty assessment. It consists of a database of factors, with characteristics that are also more related to the main and most likely obstacles each project may confront regarding the available alternatives, which make the CRP more challenging.

Accordingly, this research has attempted to review diverse projects to collect important factors or criteria to obtain an inclusive database, site characteristics, and labor availability, and then determine the project’s objectives to increase the potential of choosing the best option.

Finally, to address this challenge of factors specifically, a profound methodology is also provided within DSS to conduct the evaluation and finalize the analysis.

2.2. Decision Support System

Decision-making is an inherently complex process, which requires tools that can tackle multidimensional issues and provide the best solutions. Information-based computer systems have been integrated with Decision Support Systems (DSS), making them powerful problem-solving tools [17].

This allows for timely identification of potential solutions and a fair comparison between alternatives according to predetermined characteristics. Furthermore, DSS is a clear outlined process for representing complicated issues and their solutions and adding transparency on decision makers so discovering multipurpose problems can be handled more efficiently [18].

According to Lemass [19], DSS can improve decision-making performance and efficiency by providing mechanisms that help to achieve goals and optimize resource usage. These systems enable decision-makers to search various types of information (subjective and objective) to find ideas for the most favorable solutions, meeting competing project demands [20].

Since the 1980s, Multi-Criteria Decision Analysis (MCDA) has been widely recognized as a matrix-based approach within DSS frameworks. MCDA is particularly effective for addressing complex scenarios where projects must simultaneously meet multiple conflicting objectives. MCDA methods enable the clear ranking of variables, criteria, and alternatives while employing weighting systems to prioritize criteria based on their importance [21]. This approach provides decision-makers and analysts with a range of methodologies that integrate human judgment and preferences [22].

DSS can be used for various construction projects, either in building, commercial, and industrial fields or in infrastructure management projects. For instance, regarding bridge management, Rashidi et al. [23] proposed a DSS for bridge remediation, which utilizes a multi-criteria decision-making system that supports decision-makers through a balanced consideration of multiple criteria. Over the last decades, research has advanced MCDA models by developing algorithms to objectively evaluate individual criteria, further enhancing the utility of these methods in decision-making. Therefore, several MCDA approaches have been presented in the literature over the years. Each multi-criteria method has its own strengths and weaknesses. The choice of method primarily depends on the specific problem, available data, and decision-maker preferences. Different MCDA methods can yield conflicting recommendations, making it difficult for decision-makers to reconcile results and select a final solution, potentially leading to uncertainty and confusion [8].

In an example of bridge research, Mohammadi et al. [24] applied two MCDA methods, the Simple Multi-Attribute Rating Technique (SMART) and the Analytical Hierarchy Process (AHP), which are two commonly employed approaches in various engineering fields and have significant potential for incorporation into asset management of bridge infrastructure. In another study, Sharafi et al. [25] identified decision-making criteria through a comprehensive process involving literature reviews, surveys, and interviews with industry professionals and modular manufacturers. Utilizing these insights, they developed a streamlined MCDA framework that aids in selecting suitable construction systems and determining optimal modularization levels for building projects, facilitating more informed and efficient decision-making. Furthermore, DSS can be employed for very specific applications, such as work and safety assessment for using tower cranes, as conducted by Sadeghi et al. [26], or Digital Twin development, as proposed by Mousavi et al. [27].

According to the literature, despite the various applications of MCDA methods in different fields, limited studies on constructability assessment have utilized MCDA tools to make practical and intelligent decisions, allowing decision-makers to choose the optimal solution from suggested options. MCDA can provide efficient tools that combine several operations that can manage the considered factors and better tackle the difficulties of a construction project [20]. Furthermore, MCDA would be a powerful tool for decision-makers to set and investigate diverse environmental and social aspects. The various types of MCDA can be used according to a project’s needs to obtain the required result.

The literature provides a wide range of MCDM methods, from well-established models such as AHP, TOPSIS, VIKOR, ELECTRE, and SAW to newer approaches like the Best-Worst Method (BWM) [28], Stepwise Weight Assessment Ratio Analysis (SWARA) [29], and Combined Compromise Solution (CoCoSo) [12].

Newer MCDM models, such as BWM and SWARA, have gained attention for their enhanced criteria weighting and ranking capabilities. BWM, introduced by Rezaei [28], is designed to provide more consistent weight calculations by requiring decision-makers to compare the best and worst criteria directly. While BWM reduces the number of pairwise comparisons, it may suffer from limited scalability in large-scale decision problems. Similarly, SWARA, developed by Keršuliene et al. [29], emphasizes decision-maker preferences by introducing a stepwise re-evaluation of criteria weights. However, SWARA’s reliance on subjective assessments may introduce inconsistencies when applied to complex, multi-criteria infrastructure projects. These limitations highlight the challenge of adopting newer methods as reliable standards in practical decision-making contexts.

On the other hand, established MCDM methods continue to be widely utilized due to their proven reliability, transparency, and applicability in infrastructure decision-making. These methods offer well-defined computational frameworks that enable systematic evaluation of multiple criteria, making them suitable for constructability assessments in engineering projects. Taherdoost and Madanchian [17] have listed the 60 most widely used MCDM methods with their applications in diverse fields.

TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), first introduced by Hwang et al. [30], is one of the most used MCDA techniques. The ranking process includes creating two matrices: the decision matrix and the weighting matrix. The TOPSIS method is used to scale the alternatives. TOPSIS establishes two distinct sets of ideal and anti-ideal choices. It then ranks the available options by assessing their proximity to the ideal alternatives while measuring their distance from the anti-ideal alternatives. In this context, the ideal alternative seeks to maximize profitability measures while minimizing cost criteria. Conversely, the anti-ideal option emphasizes cost criteria minimization at the expense of profitability [22].

Simple additive weighting (SAW) [31] is another MCDM method that works based on a prominent technique for addressing multi-attribute decision problems. The fundamental principle behind the SAW method involves determining the weighted performance ratings for each alternative across all attributes. The alternative with the highest score is considered the optimal choice.

The PROMETHEE (Preference Ranking Organization Method For Enrichment Evaluation) method is a valuable and popular method among others in MCDA, first invented and developed by Brans et al. [32] in 1986. The implementation of PROMETHEE requires certain prerequisite data, namely the weights and decision-maker priority levels are first and second in a row. PROMETHEE is also a simple method to evaluate conception and application where conflicting criteria with several alternatives will be considered [33].

The COPRAS (Complex Proportional Assessment) method, introduced by Zavadskas et al. [34] in 1994, is supposed to consider a direct and proportionate connection of the perfect solution to the ratio of the anti-ideal solution. COPRAS grades alternatives based on their relative prominence (weight), and the final ranking is built using the positive and negative ideal solutions.

The CODAS (Combinative Distance-based Assessment) is known as one of the MCDA tools developed by Keshavarz Ghorbaee et al. [35] in 2016 to solve complicated problems based on the negative one as an opposite way for the best criteria in each group. CODAS will assess the distance using Euclidean and Taxicab measurements. It also represents the distance between the best and the negative solution for a reliable decision making process [36].

The VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method [37] shares similarities with TOPSIS in its reliance on distance-based evaluations. This technique assesses each alternative across all specified criteria and ranks them by measuring their proximity to the ideal solution. Unlike TOPSIS, however, VIKOR employs a distinct operational framework and a unique methodology for determining closeness to the ideal alternatives.

Table 1. MCDA tools: advantages and disadvantages.

Tool	Developer	Advantage	Disadvantage
AHP	Thomas L. Saaty [38] 1970s	Consistency factor can give the decision maker the idea of any essential reassessment [18]	If a new option is introduced after completing the calculation, the entire evaluation process must be restarted, making it time-consuming [18]
SMART	George T. Doran [39] 1981	High level of accuracy in certain tasks	Because of its simplicity, it may not be suitable in consideration of complicated circumstances [18]
TOPSIS	Hwang et al. [30] 1981	Can compare each alternative with the ideal solution [8]	The subjectivity of weighting criteria and the difficulty to create the decision matrix and judging the criteria value [18]
SAW	Zionts and Wallenius [40] 1968	Straight forward method [8]	Considers only maximizing evaluation criteria, requiring minimizing criteria to be transformed into maximizing ones using appropriate formulas before application. This limitation is overcome by the COPRAS [41]
COPRAS	Zavadkas et al. [34] 1994	An interdependent solution between criteria and alternatives [8]	Less stable than other methods in the case of data variation [41]
PROMETHEE	Brans et al. [32] 1986	An appropriate solution even when simple and efficient information is available [35].	Fails to offer a clear method for assigning weights [42].
CODAS	Keshavarz Ghorbaee et al. [35] 2016	A relatively recent approach measures the overall performance of an alternative based on its distance from the negative ideal point [43].	Sensitivity to the normalization approach [43].
VIKOR	Opricovic [37] 1998	A comparison solution once there are conflicting factors while considering both the worst and best solution [8]	Can be sensitive to changes in weights and thresholds. And, complexity increases with the number of criteria and alternatives [44].

presents a summary of the discussed MCDM methods

As a result of comparing each tool approach and advantages and disadvantages, a combination of the selected ones according to the infrastructure project complexity forms a robust evaluation framework, addressing the best, worst, compromise, and interdependent aspects of decision-making. Indeed, their combination enables a more holistic assessment of alternatives, incorporating both individual and collective criteria, trade-offs, and ranking stability.

For instance, VIKOR enhances SAW and TOPSIS by introducing a compromise-based approach, which is particularly useful for decision problems involving conflicting criteria. COPRAS was selected for its capability to account for interdependencies and feedback effects among criteria, complementing the other methods by explicitly considering these relationships [8], and also PROMETHEE and CODAS are added since PROMETHEE is capable of evaluating once we have different levels of information, and CODAS is helpful by using Euclidean and Taxicab distances, which can compare the very close options accurately [35].

The benefits of each method, along with their weakness, have motivated the authors to develop a hybrid MCDM method to apply from the very beginning of the project life cycle in large-scale infrastructure projects to provide the most beneficial MCDA. According to the literature and advantages and disadvantages of the aforementioned tools, TOPSIS, SAW, COPRAS, CODAS, PROMETHEE, and VIKOR are the selected MCDM methods that are more logical and can cover each other’s limitations. The proposed methodology of the combination of the selected MCDA–Hybrid MCDA is shown in

Table 2. Final selected Multi-Criteria Decision Analysis tools summary.

Tool	Summary
TOPSIS	The preferred option will be the closet to the ideal solution and the extreme away from the negative solution
SAW	The core concept involves calculating the weighted total of performance ratings for each option across all relevant characteristics
COPRAS	Alternatives will be ranked sequentially according to their importance
PROMETHEE	An interactive tool that is able to be quantitative and qualitative
CODAS	Assessment with the Euclidean and Taxicab distance measurements
VIKOR	Its result will be the closest option to the ideal solution

and explained in the following sections.

3. Proposed Methodology

This Section provides a detailed explanation of the research methodology employed in this study, outlining the rationale behind the chosen approach. It describes how the research objectives were addressed, such as how to apply the CRP with the DSS model in the feasibility stage of infrastructure projects with a provided robust database by survey analysis, and also validates the proposed approach by a case study.

3.1. Outline of the Proposed Method

The research methodology was meticulously designed to align with the study’s objectives, ensuring a coherent strategy to achieve its aim. The research process, as illustrated in Figure 1, is divided into four main phases to achieve the study’s objectives. Initially, in phase 1, the critical constructability criteria and sub-criteria were identified through an in-depth analysis of relevant works in the literature. These criteria and their relevant sub-criteria will serve as the basis for subsequent research stages.

Following this, in phase 2, data collection involves conducting an online survey to gather input from professionals, leveraging the established criteria and sub-criteria. The collected data undergo data analysis in phase 3, where they are categorized and ranked based on their significance and impact. Finally, in phase 4, the application of MCDA is carried out in three key steps: (1) developing a case study and creating a decision matrix to illustrate the constructability assessment process, (2) implementing a hybrid MCDA framework to evaluate and compare alternatives, and (3) validating the assessment results using the Borda Count method to ensure reliability and robustness.

The literature review provided the necessary theoretical groundwork, enabling the establishment of a robust database of key criteria and sub-criteria. These were employed in the design of the online survey and subsequent data collection. The data analysis and case study approach formed the foundation for the MCDA application, with the methodology for these processes detailed in the following sections.

3.2. Design and Conducting Survey

To identify key criteria for constructability assessment, a comprehensive online survey was conducted among experts in infrastructure and construction management. The survey focused on four main criteria with a total of 18 sub-criteria:

• Regulations (prerequisite project set-up such as sustainability);
• Engineering design and construction;
• Logistics, such as procurement;
• Project management,

Each main group included some sub-criteria, which are stated and defined in detail in

Table 3. Criteria and sub-criteria of survey.

Engineering Design and Construction	Logistics	Project Management	Regulations
Aesthetics and Innovation	Effective Communication and Teamwork	Operation and Maintenance	Sustainability
Construction Type	Legal and Political Changes	Profitability	Health and Safety
Construction Duration	Supply Chain	Project Duration and Scheduling	Urban Planning
Design and Complexity	Site Maneuverability	Quality	Contract Type and Form
Material Selection	Procurement and Contract Type

.

3.3. Identification of Key Criteria

In this step, the survey was conducted among a cohort of more than 100 experts and decision-makers specializing in two groups, including academics and professionals in civil engineering and construction management disciplines with a minimum of five years of experience (Group 1) and professionals working in civil and construction industries with a minimum of five years of experience in the roles of project managers and civil engineers (Group 2). A total of 73 individuals completed the survey, resulting in a robust response rate of above 70%. These participants were classified into six distinct occupational categories: civil/structural engineering, architecture, construction, research, education, and other fields. It is worth noting the five-year exclusion was included to obtain more solid answers from those who have gained some level of experience to represent a range of domains within the infrastructure sector.

The collected data provided a foundation for a decision matrix that serves as a universal reference for infrastructure projects. Participants rated the importance of criteria and sub-criteria, facilitating trend and pattern analysis across responses. This process enabled the prioritization of each factor, contributing to the development of a comprehensive constructability assessment framework.

Finally, the collected responses involved calculating the importance of data to discern trends and patterns across different criteria and sub-criteria to commence the analysis, which consisted of determining the level of importance of each criterion and sub-criteria based on the population portion, ultimately aiding in developing a comprehensive decision matrix as a constructability assessment framework. Figure 2 presents the portion of occupational distribution among the respondents.

The present research has collected the categorization of decision-making criteria. Accordingly, experts’ opinions on the dominant criteria for constructability-related decision-making have been sought regarding the minimization of overlaps between categories and criteria.

The principal emphasis of the survey was to ascertain the degree of recognition given to the main criteria and sub-criteria. As shown in Figure 3, ‘Project Management’ is the predominant factor in constructability assessment, comprising a substantial percentage of 26.55%. This result underscores the crucial role of effective project management in ensuring constructability. Furthermore, the survey results reveal that ‘Regulations’ was assigned a noteworthy rating of 25.06%, emphasizing its significance in constructability assessment in second place. ‘Logistics’ and ‘Engineering Design and Construction’ placed in the third and fourth spots, receiving ratings of 24.67% and 23.75%, respectively.

Subsequently, a robust statistical approach was employed to evaluate the importance of these findings quantitatively. The result is the dedicated number for each factor in their own group, which enables the comparison of their level of impact in decision-making. It is noteworthy that extra integer weights of 0, 1, 2, 3 and 4 were specified and applied to the level of importance with 1, 3, 5, 7 and 9, respectively, to make a solid calculation. This methodological rigor allowed us to understand the relative prominence of each sub-criterion, as reflected in the insightful responses provided by experts participating in this research. The detailed calculations are provided in Equation (1) according to

Table 4. Ascertain weight for level of importance.

Integer Weight Associated with the Level of Importance
Integer Weight = $B_i$	0	1	2	3	4
Importance level = $A_i$	1	3	5	7	9

.

$$W=\sum _{i=1}^5{A}_i\times B_i$$

(1)

where $W$ is the total weight of the importance level.

The total score of the sub-criteria level and integer weight (Xi) is calculated using Equation (2). Therefore, the percentage of the overall weight for each criterion (Zi) is calculated by Equation (3) accordingly as shown in

Table 5. Ascertain criteria percentage.

Criteria Percentage = $\mathit{Y}\mathit{i}$
Importance Level = $A_i$	1	3	5	7	9	Sub-criteria Importance	Overall Importance
Sub-criteria percentage	$ai$	$bi$	$ci$	$di$	$ei$	$Xi$	$Zi$%

.

$$Xi=(ai\times Ai\times Bi+\cdots +ei\times Ai\times Bi)/W$$

(2)

$$Zi=\left({\displaystyle \frac{Xi}{Yi}}\right)\times 100$$

(3)

Accordingly, the percentages associated with each sub-criterion category were meticulously derived by computing the sum of all percentages of each sub-criterion individually and multiplying it by its level of importance before dividing it by its own main criteria percentage, which is presented in the following tables individually from

Table 6. Overall importance of ‘regulations’ sub-criteria.

Importance Level	Sub-Criteria Importance	Overall Importance
Sustainability	23.00	5.77%
Health and Safety	30.70	7.70%
Urban planning	23.40	5.86%
Contract type and form	22.90	5.73%
Sum	100.00	25.06%

,

Table 7. Overall importance of ‘Engineering Design and Construction’ sub-criteria.

Importance Level	Sub-Criteria Importance	Overall Importance
Aesthetics and Innovation	16.10	3.83%
Construction type	19.40	4.61%
Construction duration	21.10	5.01%
Design and Complexity	23.30	5.54%
Material selection	20.10	4.77%
Sum	100.00	23.75%

,

Table 8. Overall importance calculation of ‘Logistics’ sub-criteria.

Importance Level	Sub-Criteria Importance	Overall Importance
Procurement and Contract type	19.60	4.83%
Effective communication and Teamwork	21.00	5.18%
Legal and Political changes	18.50	4.56%
Supply chain	21.70	5.36%
Site maneuverability	19.20	4.73%
Sum	100.00	24.67%

and

Table 9. Overall importance calculation of ‘Project Management’ sub-criteria.

Importance Level	Sub-Criteria Importance	Overall Importance
Operation and Maintenance cost	19.70	5.22%
Profitability	20.10	5.35%
Project duration and Scheduling	20.10	5.34%
Quality	19.90	5.28%
Productivity and efficiency	20.10	5.34%
Sum	100.00	26.54%

.

3.4. Hybrid Multi-Criteria Decision Analysis (MCDA)

As discussed in Section 2.2, MCDA methods can enhance the decision-making processing thanks to their distinct features. While individual MCDA methods demonstrate strong performance when applied independently, the development of a hybrid MCDA algorithm that integrates multiple results of MCDA methods simultaneously presents a more comprehensive and efficient approach. Such a method can address the limitations of individual MCDA tools while leveraging their respective strengths, thereby providing a more robust solution. Accordingly, in this paper, a hybrid MCDA framework is used, which combines six widely-used methods, including VIKOR, COPRAS, CODAS, SAW, PROMETHEE, and TOPSIS, to extract a more robust outcome.

The proposed approach offers a robust framework for analyzing complex projects involving multiple alternatives with overlapping features and key differences. It is particularly practical for selecting the best option in challenging scenarios where uncertainties and unresolved questions may hinder optimal decision-making. Finally, while applying each method individually may yield varying results due to differences in concepts and attributes, their combination addresses the limitations of individual methods and delivers more comprehensive and reliable outcomes.

Additionally, there was an attempt to introduce a system implementation that facilitates straightforward and accessible weight computations for any decision matrix and the capability to compare outcomes obtained through different weighting methodologies, introduced as a hybrid MCDA. Numerous studies have also investigated the integration or hybridization of two or more MCDA methods in diverse case studies. These endeavors aim to manage conflicting criteria and determine the optimal alternative solution [8].

In order to integrate multiple MCDA methods into a unified decision-making framework, the Borda Count method is employed to combine the results. The computation details are explained in the next Section.

3.4.1. Definition of Decision Matrix

The MCDA tools in this study involve a set of n options, represented which are evaluated based on the quality criteria discussed in Section 3.3, denoted as (A_j = 1, 2, 3, …, 19). These criteria can be categorized as either benefit or cost. For benefit criteria, higher scores are preferred, indicating that better alternatives are those with higher values. Conversely, for cost criteria, lower values are favored.

• Decision Matrix

3.4.2. MCDA Ranking

• TOPSIS:

The TOPSIS method evaluates the relative closeness of each alternative to the ideal solution. The decision matrix is normalized and weighted, and then the Euclidean distance of each alternative to the ideal and anti-ideal solutions is calculated:

$$S_i^{+}=\sqrt{\sum _{j=1}^n(v\mathrm{i}\mathrm{j}-{v_i^{+})}^2}$$

(4)

$$S_i^{-}=\sqrt{\sum _{j=1}^n(v\mathrm{i}\mathrm{j}-{v_i^{-})}^2}$$

(5)

• SAW:

The fundamental principle behind the SAW method involves determining the weighted performance ratings for each alternative across all attributes [21]. The alternative with the highest score is considered the optimal choice.

$$rij={\displaystyle \frac{xij}{\max xij}}\hspace{1em}\mathrm{i}=1,2,3,4,\hspace{1em}\hspace{1em}\mathrm{mj}=1,2,3,\dots ,16$$

(6)

$$rij={\displaystyle \frac{xij}{\min xij}}\hspace{1em}\mathrm{i}=1,2,3,4,\hspace{1em}\hspace{1em}\mathrm{mj}=1,2,3,\dots ,16$$

(7)

The favorite value for each alternative (Vi) is given as:

$$\mathrm{Vi}={\sum }_{j=1}^{n}wjrij\hspace{1em}\mathrm{i}=1,2,3,4$$

(8)

• VIKOR:

In VIKOR each alternative is gauged according to all deemed criteria and the middle ground ranking is applied by assessing how close the measurement is to the ideal alternative [45].

$$f_j^{*}=\mathit{max}{x}_{ij}.f_j^{-}=\mathit{min}{x}_{ij} j=1,2\dots n$$

(9)

$$f_j^{-}=\mathit{min}{x}_{ij}.f_j^{-}=\mathit{max}{x}_{ij} j=1,2\dots n$$

(10)

$$S_i=\sum _{j=1}^6{w}_j\left(f_j^{*}-x_{ij}\right)/\left(f_j^{*}-f_j^{-}\right)$$

(11)

$$R_i=max\left[w_j\left(f_j^{*}-x_{ij}\right)/\left(f_j^{*}-f_j^{-}\right)\right]$$

(12)

• PROMETHEE II

There are PROMETHEE I and PROMETHEE II. The first delivers partial ranking, whereas the second version is applied for full ranking. PROMETHEE II is used in this research. There are five steps to prepare PROMETHEE to be deployed as follows [42]:

Step 1:

Normalize the decision matrix

Step 2:

Assess the evaluative variances of option i compared with the remaining alternatives.

Step 3:

A preference function $P_j$ is a function of the contrast between evaluations of two criteria (a and b), indicated as $P_j(a,b)$. There are six types of preference functions; Type 1 has been used in this research, which is $p=\left\{\begin{array}{c}0,ifd<0\\ 1,ifd\ge 0\end{array}\right.$.

Step 4:

The multi-criteria preference index $\Pi$ is subsequently characterized as the weighted mean of the preference functions.

Step 5:

Establish the leaving and entering outranking flows, in which m is the of alternatives while each alternative has to deal with (m − 1) number of other options as below.

Leaving (or positive) flow for the I alternative that presents how dominant each alternative is compared with the rest:

$${\phi }^{+}(a)={\displaystyle \frac{1}{m-1}}{\sum }_{b=1}^m\pi (a,b)(a\ne b)$$

(13)

Entering (or negative) flow for the I alternative, which represents how dominant other alternatives are when facing each one:

$${\phi }^{-}(a)={\displaystyle \frac{1}{m-1}}{\sum }_{b=1}^m\pi (b,a)(a\ne b)$$

(14)

Step 6:

Compute the gross outranking flow for each option, as shown below $\phi \left(a\right)$ belongs to the alternative a:

$$\phi \left(a\right)={\phi }^{+}\left(a\right)-{\phi }^{-}\left(a\right)$$

(15)

Step 7:

Seeking the best alternative by looking at the $\phi \left(a\right)$ value as the highest one will best interpret the variance of leaving flow with entering flow as the most significant [42].

• COPRAS:

The COPRAS method is defined in the following steps:

Step 1:

A decision matrix (${\mathrm{D}}_{\mathrm{m}\times \mathrm{n}}$) is formed and normalized.

Step 2:

The sum total of the weighted normalized values is specified using ${\mathrm{S}}_{+\mathrm{i}}$ for profit criteria and ${\mathrm{S}}_{-\mathrm{i}}$ for cost criteria:

$$S_{+i}=\sum _{j=1}^k{v}_{ij}$$

(16)

$$S_{-i}=\sum _{j=k+1}^6{v}_{ij}$$

(17)

where k is the number of profit/benefit criteria, and the rest of the criteria from k + 1 to n are the number of cost criteria. The $S_{+i}$ and $S_{-i}$ values show the degree of accomplishment of the objective for alternatives. A higher value for $S_{+i}$ and a lower value for $S_{-i}$ points to better alternatives.

Step 3:

Determine the comparative importance of alternatives.

Step 4:

The ultimate ranking is carried out based on the computed $U_i$ values as outlined below.

$$U_i={\displaystyle \frac{Q_i}{Q_i^{max}}}·100\%$$

(18)

where $Q_i^{max}$ is the maximum value of the relative significance of alternatives. Alternatives (tie points) with higher values of $U_i$ will have a higher rank.

• CODAS:

There are eight steps to conduct CODAS, consisting of [36]:

Step 1:

Build the decision matrix as a first step, like all MCDA methods.

Step 2:

Normalize the decision matrix to the range of 0 to 1 ($n_{ij}$).

Step 3:

Weight calculation of the normalized matrix.

Step 4:

Verify the negative solution (ns) by the equation below:

Step 5:

Measure the Euclidean and Taxi distance of each alternative from the negative ideal solution by calculating the below equations:

$$E_i=\sqrt{\sum _{j=1}^m{\left(n_{ij}-{ns}_j\right)}^2}$$

(19)

$$T_i=\sum _{j=1}^m\left|n_{ij}-{ns}_j\right|$$

(20)

Step 6:

Assessment matrix will be developed by:

Step 7:

Conclude the evaluation rank for each alternative.

$$L_i=\sum _{k=1}^n{l}_{ik}$$

(21)

Step 8:

Sort the scores by descending $L_i$ and select the best option.

3.4.3. Borda Count Method

The Borda Count serves as a method within MCDA, applied when employing a combination of diverse methods that yield various rankings. This method aids in determining the ultimate optimal choice. Originating in 1994, the Borda Count simplifies the ranking process by assigning a value of N to the top rank when there are K options, with K being equal to N. The subsequent choices are then assigned values such as N − 1 and so forth [46].

Then, the final score of each alternative will be obtained from Equation (22), which means that the number of times of each obtained rank in every alternative will be considered through all other previous methods.

$$R_G={\sum }_{i=1}^n{s}_i\times R_i$$

(22)

In which $s_i$ is the designated score for the $i^{th}$ rank, $R_i$ is the number of times where the scenario obtains the $i^{th}$ rank and $R_G$ is the final score of the option. The highest value will be the superior choice of the alternative list resulting from the sequence of different MCDA models applied previously [46].

4. Bridge Case Study for CRP Assessment

In this section, the performance of the proposed methodology in a real case study is evaluated. To this end, a summary of the case study and its current condition is provided. Then, the upgrading scenarios are discussed, and the results of the proposed framework are shown in the following sections.

4.1. Summary of Existing Bridge

This scenario examines a 60-year-old bridge in Australia, as represented in Figure 4, with five lanes of prestressed concrete that crosses a river with a 1.4 km length and 21 spans, of which 18 spans are over the water and 3 are over the land. The highest position in the middle of the bridge is 65 m above the water, as is the navigation channel. The bridge consists of two narrow walkways on either side supporting a maintenance and inspection gantry, bridge lighting, and major services.

4.2. Description of Upgrading Scenario

The upgrade will provide improved pedestrian and cyclist access over the bridge to encourage active transport options across the bridge corridor safely and efficiently. The key features include:

• The provision of a 3.5 m shared path on each side of the bridge with improvements to connection to the existing path network on the eastern and western sides of the river, as shown in Figure 5b.
• Safety railings on each side of the pathways that adopt all suicide prevention measures.
• Improvements to path lighting.
• Upgrades to the bridge feature lighting.
• Upgrades to the bridge maintenance and inspection gantry.
• Bridge strengthening to carry current traffic loadings.

Based on the current and upgraded scenarios, the design team has proposed four options, outlined in

Table 10. Case study available upgrading options.

Alternatives	Strengthening Type	Pathway	Civil Works (Western and Eastern Approaches)
Option 1	Post-tensioning	Double pathway with cantilever below	Eastern approach
Option 2	Post-tensioning	Single pathway with cantilever below	Western approach
Option 3	Post-tensioning	Single pathway without cantilever below	Eastern approach
Option 4	Addition of piling and piers	Single pathway without cantilever below	Western approach

, and also shown in Figure 6, Figure 7 and Figure 8, while also accounting for certain challenges, which include: (1) a safe, practical, and aesthetically pleasing outcome, (2) broad community and stakeholder support, (3) minimum traffic disruption during construction, (4) minimum disruption to the use of the bridge for active transport during construction, and, (5) the project can be delivered under a D&C contract.

4.3. Customize a Decision Matrix for a Case Study: Upgrade and Strengthening of an Existing Bridge

The MCDA process involves assessing a set of available options and customizing criteria to this specific case study. This leads to eliminating some criteria owing to having the same status for all options and preventing errors once running the MCDA for the final result, totaling 16 criteria; Figure 9 represents an overview of the decision matrix. However, it is vital to accurately split the values of the removed criteria between other sub-criteria into their own groups based on their portion amount. Subsequently, in

Table 11. Customized decision matrix.

Main Groups	Criteria	Sub-Criteria Weight	Option 1	Option 2	Option 3	Option 4
Regulations	Sustainability	7.51%	6	4	3	8
	Health and Safety	9.99%	4	8	5	6
	Urban Planning	7.57%	9	6	4	4
Engineering Design and Construction	Aesthetics and Innovation	3.83%	9	6	8	3
	Construction Type	4.61%	9	9	9	5
	Construction Duration	5.01%	6	3	4	9
	Design and Complexity	5.54%	1	1	1	0.001
	Material Selection	4.77%	3	6	8	8
Project Management	Operation and Maintenance Cost	5.22%	4	2	7	5
	Profitability	5.35%	3	5	7	8
	Project Duration and Scheduling	5.34%	8	4	2	6
	Quality	5.28%	9	9	9	6
	Productivity and Efficiency in Resource Management	5.34%	5	7	8	3
Logistics	Legal and Political Changes	7.68%	6	8	9	3
	Supply Chain	9.03%	4	5	7	9
	Site Maneuverability	7.96%	4	6	8	9

, it can be seen that the sub-criteria’s values in regulation and logistics have increased adequately.

These alternatives are then appraised in relation to the specified criteria, which can be quantitatively expressed in three ways:

• The benefit-based scale ranges from 1 to 10, where higher values indicate a more favorable outcome.
• The minimum-based scale ranges from 1 to 10, where lower values signify a more desirable result.
• The binary solution, with options represented as 0 or 1, where the preferred choice is assigned a value of 1.

4.4. Bridge Case Study Results

Following the completion of the decision-making matrix, the hybrid MCDA method with different MCDM methods, including TOPSIS, SAW, VIKOR, PROMETHEE, COPRAS, and CODAS was implemented based on their respective algorithms within the MATLAB R2024a software, as outlined in the research methodology.

As a result, Figure 9 displays the outcomes of the hybrid MCDA analysis.

Option 3 stands out with two scores of 1 in both PROMETHEE and COPRAS and then with four other close scores around 0.6 to 0.7 in TOPSIS, SAW, VIKOR, and COPRAS. In summary, it is evident that Option 3 did attain high rankings in the hybrid decision matrix values. Despite the comparisons and discussions, certain ambiguities persist in selecting the optimal choice.

The Borda Count method is employed to conclude the best decision as the second approval. Despite the comparisons and the discussion, certain ambiguities persist in selecting the optimal choice; the Borda Count method is also utilized to assess the outcomes generated by various methods. Initially, rankings and their corresponding scores are taken into account. In this process, ranks ranging from 1 to 4 are assigned based on the highest to lowest results. Each rank is then associated with a score from 4 to 1, reflecting the highest score for the top result. The final score of each alternative will be obtained from the formula below, which means that the number of times each obtained rank in every alternative will be considered through all other previous methods.

The results are illustrated with the final calculated score for each option in Figure 9 and Figure 10; as it is seen, yet again, Option 3 attains the highest value, scoring 20, which not only serves as robust evidence supporting the analysis conducted in the preceding section—wherein we assumed Option 3 to be the top choice—but also confirms Option 2 is the closest positioned to Option 3 as the second choice.

4.5. Comparative Results to Single MCDM Methods

To validate the effectiveness of the proposed hybrid MCDM approach, a comparative analysis was conducted between the results obtained from individual MCDM methods and the hybrid methodology. Figure 11 presents a graphical analysis of each method’s results. As illustrated, each MCDM method selects a different alternative as the best option, demonstrating the variability in decision outcomes when relying on a single method. In contrast, the hybrid MCDM approach identifies yet another alternative as the optimal choice, emphasizing its ability to provide a more comprehensive evaluation.

The hybrid MCDM approach integrates multiple decision-making techniques, leveraging their respective strengths while mitigating their individual limitations. Combining the results of multiple methods ensures a more balanced and robust assessment of constructability factors, reducing biases and enhancing decision reliability.

5. Discussion

This study’s primary contribution lies in enhancing the database through a combination of empirical findings and surveys. These results were largely qualitative but complemented by quantitative techniques employed during the survey process. The vast amount of data collected through a well-structured questionnaire was then analyzed to identify key decision-making factors relevant to constructability assessments in infrastructure projects. This allowed the identification of critical data points and informed the development of a hybrid MCDA approach, which serves as a robust decision-making tool within the CRP at the feasibility stage of infrastructure projects.

A significant innovation of this research is the introduction of a customizable Decision Support System for constructability analysis. This system is designed to accommodate various stakeholders’ and clients’ objectives, thus providing a tailored approach for addressing complex decision-making scenarios. The integration of CRP in the early project phases, specifically during feasibility, enables the identification and resolution of potential conflicts between criteria early in the project lifecycle, which is crucial for the long-term success of large infrastructure projects.

In this study, a hybrid MCDA methodology was applied, incorporating well-established methods such as TOPSIS, SAW, COPRAS, PROMETHEE, CODAS, and VIKOR. These tools were used to rank alternatives and analyze the optimal outcomes. To effectively combine the results from multiple MCDA methods, the Borda Count method was employed to aggregate the rankings, thereby providing a more reliable and comprehensive decision-making framework. This multi-method approach is particularly advantageous as it mitigates the weaknesses of individual MCDA techniques while leveraging their strengths, resulting in a more balanced and robust solution.

The case study applied in this research adhered closely to the proposed methodology, illustrating how this comprehensive approach can be used to make informed decisions in complex project scenarios. By developing a customized decision matrix based on real project data, the study demonstrates the importance of adapting the decision-making framework to suit the specific conditions and objectives of each project. The flexibility of the DSS ensures that it can be tailored to exclude irrelevant factors or those with negligible influence on the decision-making process. This customization makes the DSS a valuable tool for guiding project managers in making well-informed decisions during the feasibility phase.

The approach presented in this research provides a systematic framework for analyzing large-scale infrastructure projects with multiple alternatives and conflicting attributes. It is particularly useful in scenarios where uncertainties and unresolved variables present significant challenges. By integrating diverse MCDA tools and offering a comprehensive decision-making process, the proposed DSS can help project managers identify optimal solutions in the early stages of a project, thereby improving the efficiency and effectiveness of decision-making.

Ultimately, the findings of this research will benefit both project managers and decision-makers by enabling them to prioritize, predict, and optimize their plans for infrastructure projects. Additionally, the broader engineering sector stands to benefit from this research, as it offers a valuable framework for evaluating alternatives in the feasibility stage, thus improving the project’s performance during both construction and operational phases.

6. Conclusions

This paper highlights the importance of the hybrid MCDA tool in making constructability assessments for infrastructure projects. Using a systematic approach, this study identifies and assesses essential criteria and sub-criteria that impact decision-making processes in construction management. It combines input from specialists with an in-depth survey methodology to develop a decision matrix that allows a systematic comparison of options, specifically accounting for shared characteristics and distinguishing differences between possible solutions.

It emphasizes that the disciplines of project management, regulations, logistics, and engineering design are the key factors affecting constructability. Due to the ever-changing and dynamic nature of the construction industry, this research supports such a framework/guide to making informed decisions in construction. Following this, the evaluation of alternatives was conducted using hybrid MCDA with six MCDA algorithms, and the final result was determined and concluded using the Borda Count method, solidifying the analysis of the results.

While this research provides valuable insights, it does have some limitations. The study primarily relied on survey data, which may not fully represent the diversity of conditions and perspectives across all infrastructure projects. Additionally, some factors that could further enhance the constructability analysis, such as project-specific risks and stakeholder involvement, were not included. These limitations suggest the need for further studies to include a broader range of survey designs and factor considerations.

Future research could expand on this work by incorporating additional factors not covered in the current study. For example, integrating earned value management (EVM) with constructability assessments could provide a more comprehensive approach. Furthermore, a deeper investigation into how the CRP method performs in actual projects is recommended. By analyzing completed projects, it would be possible to assess whether the selected alternatives were indeed the optimal choices and quantify any potential negative impacts from suboptimal decisions.

In future work, applying state-of-the-art MCDM methods in the hybrid MCDA, including the Best-Worst Method, could enhance the robustness and precision of decision-making processes. Furthermore, the integration of simulation and visualization techniques, such as Building Information Modelling (BIM) [47] with the proposed hybrid DSS could provide a more dynamic and interactive approach to comparing alternatives, improving the overall decision-making process.