Developing a Decision-Making Support System for a Smart Construction and Demolition Waste Transition to a Circular Economy

Abstract

This research work aimed to develop a decision support system (DSS) to select the most appropriate concrete waste management method, which is the most critical issue in the construction industry. The research process began with the study of the current situation of concrete waste management problems. Related theories and literature were reviewed, and experts were interviewed in depth. After that, the Delphi technique and the fuzzy analytic hierarchy process (FAHP) were used to analyze the decision-making structure and consider factors related to the waste management methods. Then, the FAHP process was analyzed, calculated, and prioritized using MS Excel until the results were obtained. Finally, decision structures were shown, evaluated, and prioritized using a case study by a group of experts belonging to the Thai circular economy construction industry (CECI). The contribution of this research line in the DSS model is by analyzing, calculating, and finding the most appropriate alternative solution for the construction waste industry. Limitations, recommendations, and future research directions are also presented.

1. Introduction

Currently, it is known that the construction industry highly influences three sustainability pillars, namely, the environment, economy, and society [1]. Smol et al. (2015) [2] stated that the construction industry is an industry that promotes employment opportunities, promotes habitat diversity, provides convenience, and supports GDP. Although there are many benefits to the economy and society, the construction industry also has a negative impact on the environment throughout the life cycle of a building. The negative impacts of project construction can include the use of natural materials, waste production, energy consumption, greenhouse gas emissions, water and resource depletion, noise and dust, and habitat destruction. Studies have explored the contributing factors and processes involved in these impacts, such as the extraction of materials, transportation, on-site operations, and the disposal of materials at the end of their life cycle [3]. These were considered to be the main cause of the deteriorating environment [4]. This issue is also linked directly to waste production. Natural minerals are used at a rate as high as 50% [5], and create as much as 35% of the waste going to landfill [6]. The construction sector’s carbon emissions in 2020 reached 11.79 gigatons of carbon dioxide, representing 37% of all carbon emissions worldwide. Seventeen percent of these emissions were attributable to the operational carbon emissions of residential buildings [7,8]. The construction industry’s rapid growth due to increased populations and city expansion around the world has created tremendous amounts of waste [9]. Global construction and demolition waste has the highest quantity when compared to other activities. Construction waste comprises as much as 40% of a city’s total waste [10], mainly from construction, building renovation, and demolition. China produces around 2.5 billion tons of construction waste per year due to its large-scale city expansion and city restoration [11]. Data from Euro-stat (2020) [12] showed that the total waste in European countries from the economy and households reached 2151 million tons. Waste from the construction industry occupied 37.1% of this, or 798 million tons. Data from US EPA (2018) [13] showed that construction waste in the USA reached about 600 million tons. During the year 2018–2019, Australia created 27 million tons of demolition waste (44% of total waste). Even though 76% of this waste was recycled, the waste increased by 61% from 2006 to 2017 [14]. In 1998, Hong Kong produced 32,710 tons of construction and demolition waste per year. Concrete, brick, and sand waste was deposited in public spaces [15,16].

Focusing on the south-east Asian region, Ngoc Han Hoang et al. [17] reviewed a lot of documents related to construction and demolition waste (CDW) and found that Malaysia, Singapore, and Vietnam were the only three countries in the region to publish construction waste statistics and data. Singapore has produced about 1.54 million tons of construction waste per year since 2015. Malaysia produced around 26,000 t per day. Vietnam did not specify the construction waste amount, but the report mentioned that construction waste comprises around 10–12% of the daily local waste, which is 6000–7200 t per day. Furthermore, no updated waste information for Vietnam has been posted for the past 8 years. There is no official construction waste report for the rest of this region, including for Thailand. Kofoworola and Gheewala [18] studied construction waste management and forecasts in Thailand. Manowong [19] investigated factors influencing construction waste management in developing countries and discovered that most of the construction waste is dumped in open landfills in almost every Southeast Asian country. The findings of Luangcharoenrat et al. (2019) indicate that project characteristics, including project size, duration, and complexity, have a significant influence on construction waste generation. Furthermore, management practices, including waste sorting and segregation, recycling initiatives, and waste management training, are crucial in reducing waste generation. The capabilities of contractors, including their experience and knowledge of sustainable construction practices, also impact waste generation rates [9]. Russia had a concrete waste ratio during demolition, building, and construction as high as 60% [20]. The US had a concrete waste ratio of about 67% of construction waste in 2018 [21], while the concrete waste in Canada comprised about 52% of the construction waste [22]. Begum et al. found that the construction waste ratio in Malaysia was as high as 65% [23].

Due to the high environmental impact of the construction industry and the high rate of waste production, waste management is a priority for global sustainable development [24]. Although reusing and recycling are common waste recovery methods, they are still not highly efficient, especially in emerging countries, where only 20–30% of construction and demolition waste is recovered and there are inadequate waste management plans [25]. Consequently, a substantial amount of waste ends up in landfills or is illegally discarded without adequate environmental measures [26]. The end-of-life (EOL) phase of the assets, in particular the demolition process, contributes significantly to the waste generated by the construction industry and accounts for 50% of waste worldwide [27]. In the UK alone, 30% of waste sent to landfills comes from demolitions, resulting in an annual cost of GBP 200 million [28]. As a result, researchers are increasingly interested in asset EOL management and construction and demolition waste (CDW), aiming to shift the industry toward the paradigm of a circular economy (CE) [29,30].

The CE approach, supported by the Ellen MacArthur Foundation and by initiatives such as the European Union’s CE Implementation Action Plan, aims to shift from a linear system to one that promotes recycling, resource efficiency and reduced waste [31,32]. However, some limitations were identified during the implementation process. Although recycling has been extensively developed, it is considered a less favorable option compared to other aspects of the CE, and EU policy tends to focus on end-of-life solutions without adequately addressing the socio-ecological implications of the circularity transition [32,33]. In the construction industry, the predominant approach remains linear, where demolition typically manages EOL [34]. Defining the circularity of a building was a challenge, mainly because of the unique nature of buildings compared to manufactured products. Each building is tailor-made to meet the requirements of individual owners (private, public, or corporate). Additionally, the management of a building throughout its life cycle involves diverse stakeholders with different skills and interests [35]. Furthermore, the different phases of a building’s life cycle vary significantly in terms of timescale, with the operational phase typically lasting at least 30 years.

The transition to a circular economy system in the construction industry offers various benefits, including economic growth, improved material efficiency, increased value throughout life cycles, waste reduction, resource efficiency, and environmental sustainability [36,37]. Overcoming these barriers requires a comprehensive approach, including regulatory reforms, engagement with stakeholders, capacity building, and knowledge dissemination [38]. MU Hossain et al. [39] stated that the utilization of the circular economy in construction can facilitate the transition to sustainable construction practices.

The current situation regarding the sustainable development of the construction industry has made notable progress, accompanied by increased recognition of the importance of sustainable practices. Efforts to enhance energy efficiency and reduce carbon emissions have gained significant momentum, focusing on energy-efficient design and the integration of renewable energy sources. It has been widely acknowledged that incorporating advanced technologies and innovative approaches is crucial for improving sustainability performance [40]. The construction industry has placed a strong emphasis on sustainable materials and resource management, including the utilization of recycled materials and environmentally friendly products [41]. The adoption of circular economy principles and efficient waste management practices has led to a reduction in construction waste and a more efficient use of resources [42]. Social sustainability and stakeholder engagement have become critical aspects, highlighting the significance of fair labor practices, community involvement, and the creation of local employment opportunities [43]. Green building certifications and standards continue to drive sustainable practices in the construction industry, underscoring the necessity of green buildings for sustainable environmental development. However, the adoption of green building practices presents various dilemmas and challenges, such as cost considerations, limited awareness, regulatory hurdles, and the need for collaborative efforts [44,45]. Supportive policies and regulations play a crucial role in promoting sustainability in the construction industry. The implementation of building codes, certification systems, financial incentives, and sustainability standards aims to encourage and incentivize the adoption of sustainable practices [46].

Businesses in the Thai private sector related to the construction industry have gathered together as the “Network of Business Organizations for Circular Economy in the Construction Industry (CECI)”. This network represents a collaboration among business sectors in the construction industry. This network was assembled for the first time at SCG’s “SD Symposium 2020” to promote the concept of a “circular economy”, encouraging the construction industry to use resources more efficiently and reduce environmental impacts. The main goal was to reduce landfilling as much as possible and to implement ideas for efficient waste management in the construction industry. Currently, there are 30 members from the private sector.

However, there are still issues in construction waste management, such as waste reduction, recycling and reusing materials, and landfilling. Each management choice has different environmental, technical, social, and economic characteristics. To select the most suitable management method, all aspects related to the environment, techniques, society, and the economy should be thoroughly considered, including both advantages and disadvantages. Multi-criteria decision analysis (MCDA) is a method of decision-making that considers multiple evaluations. The objective is to find a clear method for answering questions and supporting decision-making.

According to the literature review, waste management problems have been addressed for more than three decades using MCDA techniques. This involves integrating environmental, political, social, cultural, and economic values with the preferences of stakeholders, while considering the challenges of monetizing non-monetary factors [47,48]. Several research papers have presented numerous applications of MCDA techniques in the context of waste management [49,50,51,52,53]. These studies typically emphasize the importance of a comprehensive decision-making process that considers a wide range of impacts, including social, cultural, environmental, economic, land use, resource use, reuse, and recycling. MCDA provides the advantage of incorporating both qualitative and quantitative data, which is valuable when decision-makers encounter challenges with qualitative information. In real-world decision-making scenarios, MCDA techniques are employed to address both types of data, often utilizing advanced approaches such as fuzzy set theory to handle data uncertainty [54]. Among the interactive methods available, pairwise comparison is frequently used to establish trade-off relationships between criteria [55,56].

Analytical hierarchy process (AHP) approaches have proven to be useful decision-making tools in real-life applications. Vaidya and Kumar [57] grouped the application areas as follows: selection, evaluation, benefit–cost analysis, allocations, planning and development, priority and ranking, and decision-making. Additionally, Williams [58] went further to elaborate that the natural simplicity and enormous flexibility of AHP have provided it with even wider applicability. For instance, it is adopted in education, engineering, government, industry, management, manufacturing, personnel, politics, society, marketing, and sports [58,59,60]. Many researchers have reported that the rationale for applying APH in this study, as has been seen in other applications of MCDA (e.g., ELECTRE, MacBeth, MAUT, MAVT, TOPSIS, SMART, PROMETHEE, and UTA), is simplicity, familiarity, and flexibility [61,62]. The AHP is noteworthy for its ability to assess both quantitative and qualitative data on the same preference scale of nine levels, which is particularly useful when involving multiple stakeholders. This ability distinguishes AHP from other multi-criteria decision analysis (MCDA) methods and enhances its advantages [60,61]. However, Huang et al. [62] pointed out that no matter which MCDA methods are used, similarities in the outcome of the methods are nearly guaranteed. Consequently, the choice of method is mainly based on the preference of the decision maker. Cegan, J.C. et al. [63] found that 26% of the articles used a combination of AHP and fuzzy logic. Penades-Pla, V. et al. [64] examined various methods and sustainability criteria for making decisions about each bridge’s life cycle, from design to recycling or demolition. It was found that 68% of the methods used the fuzzy analytical hierarchy process (FAHP). It was used as a tool to support multiple-criteria decision-making (MCDM). The criteria could be both quantitative and qualitative. The FAHP was developed as an extension of the AHP. Thomas L. Saaty published this in the 1980s. Since then, FAHP has become a widely used methodology in decision making and has been applied to various fields, including engineering, finance, and environmental science. FAHP can make the decision under conditions of vagueness and uncertainty, like human thought, so the decision making was more efficient [65]. The FAHP model is a decision-making tool that integrates fuzzy logic to handle uncertainty and imprecise information. It allows decision-makers to express their judgments in linguistic terms and handles uncertainty through fuzzy inference. On the other hand, if the decision problem involves more precise and quantifiable data without significant uncertainty, models such as step-wise weight assessment ratio analysis (SWARA), the best-worst method (BWM), or the linear biased weighting approach (LBWA), which do not explicitly consider uncertainty or incorporate fuzzy logic, may be more appropriate [66]. In this project, fuzzy AHP was used as a prioritizing tool.

The conceptual “inverted pyramid” model of steps in the research is shown in Figure 1, and summarizes the overview of the Introduction to this study and addresses the problem of increasing amounts of construction and demolition waste around the world and their negative impact on the environment.

The research study area focused on addressing waste management challenges in the construction sector, specifically concrete waste, by employing the concept of a circular economy. The research area was divided into four distinct components. These components were based on relevant theories and literature reviews. The ultimate aim of these components was to identify research gaps and questions, as presented in Figure 2.

Research Study Area 1: The construction industry consumes significant amounts of resources and generates substantial amounts of construction waste. It is important to study this area to improve the efficiency of construction and reduce the environmental impact of construction waste. Numerous studies have focused on enhancing the efficiency of the construction sector and reducing waste production. Concrete waste, the most prevalent type of construction waste, is frequently disposed of in landfills or occupies space without providing any value or benefits.

Research Study Area 2: A circular economy is a term used to describe an economy where resources are used in a sustainable way on a global scale. The European Union’s Circular Economy Action Package was introduced in 2018 to reduce waste and promote more sustainable policies. Reducing landfill waste and increasing recycling rates contribute to the preservation of the environment. In Thailand, recycling is expected to account for approximately 30% of the total waste volume by 2030. Under the circular economy framework, the government prioritizes the development of environmental management systems, targeting industries such as construction materials, plastics, tires, electronic parts, electric vehicle batteries, and solar cells.

Research Study Area 3: The research and development of robust tools to address decision-making challenges involves the creation of decision support systems (DSS) with various analytical processes. Based on theories and literature reviews, the fuzzy analytical hierarchy process (FAHP) is considered an appropriate tool, having been employed by numerous researchers in waste management analysis.

Research Study Area 4: The integration of the three research study areas led to the identification of research questions and gaps, and the development of a decision-making support system to address challenges while promoting the concept of a circular economy. The research questions are as follows:

RQ 1: What is an appropriate hierarchy structure to find the best solution for concrete waste management?

RQ 2: What is the appropriate algorithm for solving problems with construction waste management?

RQ 3: What are the main criteria and sub-criteria, as well as the best alternative solutions?

2. Materials and Methods

The research methodology adopted in this study consisted of two main phases: the first phase involved identifying the research gap, defining research objectives, and creating a decision support system (DSS) using a hierarchy chart. In the second phase, a calculation method was used to determine priorities and alternatives based on the fuzzy analytical hierarchy process. Figure 3 presents the research methodology adapted within the study.

Phase 1: Identifying the research gap and creating a DSS using the AHP process. The first phase aimed to identify the research gap and develop a decision support system (DSS) for evaluating and comparing options for concrete waste management in the construction industry. This phase proposed a new framework for determining the most suitable method of waste management using the analytical hierarchy process (AHP).

In Phase 1, Section 1, a theoretical study and a review of the relevant literature were conducted to investigate the current situation and issues related to concrete waste management in the circular economy of Thailand’s construction industry (CECI). The outcomes of this section led to research questions and objectives, and alternatives, criteria, and sub-criteria were collected from previous studies [67,68,69,70,71].

In Phase 1, Section 2, criteria and options for construction concrete waste management were identified through a literature review. An in-depth interview with CECI experts was conducted using the Delphi technique to identify various criteria for proper selection.

In Phase 1, Section 3, the factors evaluated by experts from the CECI group were used to generate the structure of the decision hierarchy chart using the analytical hierarchy process (AHP), developed by Thomas L. Saaty in the 1980s. Hierarchical charts imitate human thought processes, and the chart was divided into several levels depending on the complexity of the problem.

Phase 2: Calculation Methodology: The second phase of the research framework involved the assessment and prioritization of decisions to determine the most appropriate way to manage concrete waste from the results of the questionnaire.

In Phase 2, Section 1, a questionnaire was created to use in decision making, using an analytical hierarchy process. The priority value was scored for each factor under the principle of pairwise comparison.

In Phase 2, Section 2, the questionnaire was used to perform in-depth interviews with a group of experts. The questionnaire was created by comparing the priorities of the evaluation criteria with the objective of the problem.

In Phase 2, Section 3, discretionary consistency checks were carried out using the AHP method [72]. This method measured the level of consistency of each discretionary set by calculating the consistency ratio (CR) in each matrix.

In Phase 2, Section 4, after the consistency ratio (C.R.) was checked, evaluated, and scored based on the questionnaires of every expert (the resulting consistency ratio had to be less than 0.1 for the pairwise comparison to be considered reasonable and reliable), the main factors and sub-factors were prioritized to decide the most suitable method for concrete waste management. The calculation was performed by using MS Excel, and the analysis was conducting according to the fuzzy AHP method. The geometric mean method was used [73]. In the case of multiple experts in hierarchical analysis ranking an option differently, the geometric mean was used to add up or sum all the data from the experts before calculating the weights from the matrix of mean values [74].

2.1. Delphi Method

The Delphi method is characterized by its ability to structure group communication procedures, enhance communication efficiency, foster group cohesion, and effectively address complex problems [75]. In particular, the expert Delphi method, which is commonly used, aims to gather expert opinions to obtain answers to specific questions. It is well-suited to identifying the core elements of a problem, reducing implicit and complex knowledge, and providing concise statements that objectively clarify diverse evaluations or opinions [76]. Through the Delphi technique, accurate and aligned conclusions can be obtained without requiring experts to attend a brainstorming meeting. This allows for the inclusion of diverse opinions from experts located in different locations, timeframes, and geographical conditions [77]. This technique facilitates the free expression of opinions by experts and is particularly valuable in expert group settings where problem-solving and research processes benefit from the gathered information [78]. In this study, a modified Delphi method was adopted to handle the complexity of selecting and determining key performance indicators. In the realm of multi-criteria decision-making (MCDM), the Delphi method and the analytic hierarchy process (AHP) are widely utilized tools [79].

The Delphi method serves as a valuable approach to gather expert opinions and insights regarding decision problems. It employs techniques such as semi-structured interviews, group discussions, and questionnaires to facilitate effective communication and mutual understanding among professionals with relevant field experience [80]. Through this method, experts contribute their opinions, ideas, knowledge, and expertise to collectively address the problem at hand [75,81]. The Delphi process consists of several steps, including expert selection, multiple rounds of questionnaire surveys, and iterative feedback until a consensus is reached among the experts [82]. Notably, there are no strict limitations on the number of experts involved in providing feedback; however, it is generally recommended to have a minimum of 9 to 18 experts to ensure a comprehensive decision-making process [83]. To increase data accuracy, it is recommended to use Delphi groups comprising five to twenty experts, as suggested by G. Rowe et al., with the number of experts not being less than five [76]. In the present study, twenty experts were consulted to obtain meaningful feedback regarding the decision problem. The experts consulted in this study belonged to the Circular Economy of the Construction Industry Group (CECI), consisting of five subgroups, namely architects and project consultants, real estate developers, contractors, manufacturers and distributors, and waste management operators, as presented in Figure 4. The questionnaires were sent to 30 relevant experts from diverse backgrounds with over 10 years of experience in the construction industry, holding executive positions responsible for defining the organizational strategy and vision. However, only 20 out of the 30 experts responded to the questionnaires, resulting in a response rate of 74%, which was considered good [84,85].

The experts were reached via e-mail and direct messages. The questionnaire prepared with Google Forms was sent to them via the same means. Furthermore, interviews with the expert group were conducted through online meetings. References [86,87] were consulted in the implementation of these methods. Microsoft^® Excel^® 2016, running under a Windows 10 system on a Dell Inc. (Round Rock, TX, USA) Inspiron 5767 (Intel^® Core^TM i7-7200U CPU, 2.70 GHz, 16 GB of RAM, 64-bit) laptop platform, was used for the calculations of the fuzzy AHP.

2.2. The Analytical Hierarchy Process (AHP)

The analytical hierarchy process (AHP) is a valuable method for addressing uncertain situations and decision-making problems that involve multiple evaluation criteria. Its main feature is its ability to structure complex and unstructured problems by using a hierarchical framework to systematically establish relationships between influence factors. Decision makers can more effectively reflect their intentions through pairwise comparisons [88]. One of the main advantages of the AHP method is its ability to improve decision-making processes traditionally based on intuition. It achieves this by conducting hierarchical system integration analyses that involve clear layers and improve the reliability of assessments [89]. By applying an analysis-level procedure, the AHP method organizes complex multi-objective decision-making problems into a hierarchical structure. Each layer contains different elements that enable the systematic handling of numerous qualitative factors. The decision-making process is guided by hierarchical relationships between criteria and alternatives, which leads to the determination of priorities for each plan. The adopted plan has a higher priority if the values are higher [90]. Throughout the analysis, the AHP method uses eigenvectors to determine the priority order between elements within a particular hierarchical level. It calculates individual element eigenvalues, which serve as a basis for assessing the relative importance of pairwise comparison matrices performed on a nominal scale. When experts’ opinions are consistent, the priority values represented by the eigenvectors can be used for decision making or selection according to reference [55]. The AHP method calculates the relative weights in eigenvector values. The weight of each alternative is then obtained by multiplying the eigenvector values for each hierarchy by the number of levels in the hierarchy. The purpose of this process is to determine the final weights for decision making.

The use of the analytic hierarchy process to solve practical problems generally includes four steps [91].

Step 1. Establish a hierarchical structure of the researched problem, define the performance index problem, confirm the goal, and identify the criteria and attributes that must meet the goal. In the problem hierarchy, goals are at the first level, criteria are at the second level, attributes are at the third level, and decision-making alternatives are at the fourth level.

Step 2. Establish a pairwise comparison judgment matrix: there are N factors in the hierarchy and a total of [N ∗ (N − 1)]/2 combinations, according to the pairing method.

Step 3. The elements at a particular hierarchical level are compared using a nine-point numerical scale to define how much more important one element is from another [92]. An example of the pair comparison numerical scale of the AHP method is shown in

Table 1. Analytic hierarchy process (AHP) method pair comparison numerical scale [93].

Pair Comparison of A and B
Evaluating factor A	Absolute unimportance 1/9	Very strong unimportance 1/7	Essential unimportance 1/5	Weak unimportance 1/3	Equal Importance 1	Weak importance 3	Essential importance 5	Very strong importance 7	Absolute importance 9	Evaluating factor B

[93]. If A and B are the elements at a particular hierarchical level to compare, “1/9” means that B is of absolute importance, “1” means that A and B are of equal importance, and “9” means that A is of absolute importance. All pairwise comparisons are given in a judgment matrix [92].

Solve the weight of each level and verify its consistency; for calculating the relative weight of each element from the judgment matrix, the AHP method adopts the eigenvector method. Because the comparisons in the judgement matrix are made subjectively, a consistency test needs to be performed. The consistency test is based on Saaty’s [55] recommendations, using the consistency index (C.I.) and consistency ratio (C.R.) to test. C.I. is the degree of difference between the maximum eigenvalue (λ_max) and the order (n). The principal eigenvalue (max) for each matrix is calculated using Equation (1).

Aw = λ_maxw

(1)

where A is the comparison matrix, λ_max is the principal eigenvalue and w is the normalized right eigenvector (priority vector). Second, we estimate the consistency index (CI) for each matrix with the dimension ‘n’ using Equation (2):

$$CI=\frac{{\lambda }_{max-n}}{n-1}$$

(2)

Then, we finally calculate the CR using Equation (3):

$$CR=\frac{CI}{RI}$$

(3)

where RI is the random index. The value of RI is selected depending on the dimension of the comparison matrix (n).

Table 2. Random index (RI) values for different matrix sizes [94].

Matrix Size (n)	1	2	3	4	5	6	7	8	9	10
Random index	0	0	0.58	0.9	1.12	1.24	1.32	1.41	1.45	1.49

illustrates the different RI values for matrices having the order n from 1 to 10. The more the R.I. value increases with the order of the matrix, if C.R. ≦ 0.1, then the more acceptable the rating in the pairwise comparison matrix.

Step 4. Solve the dominance value of each decision alternative; after passing the consistency test, the dominance value of each decision alternative can be calculated. This is obtained by multiplying the weights of each level and adding them up.

In this study, we checked the consistency ratio (CR) in the analytic hierarchy process (AHP) before prioritizing weights using the fuzzy analytic hierarchy process (FAHP) geometric mean method. This was consistent with researchers discussing the importance of calculating the CR to evaluate the consistency of the pairwise comparison judgments made by the decision-maker. If the consistency ratio exceeds a predefined threshold, it indicates that the judgments are not consistent and may need to be revised before proceeding with the prioritization of weights using the fuzzy analytical hierarchy process geometric mean method [95,96,97].

2.3. Fuzzy Analytical Hierarchy Process (FAHP)

Fuzzy AHP is an extension of AHP and is a beneficial technique for solving complicated decision problems. Any complicated problem can be deconstructed into different hierarchical levels of criteria. Within each hierarchy, a series of pair-wise comparisons are conducted to determine the importance of criteria. In AHP, experts use crisp numbers (e.g., 1, 2, …, 9) to determine the importance of criteria, whereas fuzzy AHP experts use natural linguistic terms (e.g., equally important, weakly important) to express their judgments. The linguistic terms represent the corresponding fuzzy numbers defined in fuzzy membership functions [98].

2.3.1. Establishing Fuzzy Numbers

Zadeh [99] introduced fuzzy sets, which are sets with elements of different membership degrees. Fuzzy membership functions define the fuzzy sets based on a 0–1 interval. Triangular fuzzy membership functions are widely used [100,101]. The triangular fuzzy membership function is adopted for its computational simplicity and ability to deal with fuzzy data [102].

A fuzzy number Ne on R will be a triangular fuzzy number (TFN) if its membership function ${\mu }_{\stackrel{\mathrm{~}}{N}}\left(x\right):\mathbb{R}\to$0, 1 is equal to Equation (4), as follows:

$${\mu }_{\tilde{N}}\left(x\right)=\left\{\begin{array}{c}\frac{x-l}{m-l},l\le x\le m,\\ \frac{u-x}{u-m},m\le x\le u,\\ 0,otherwise,\end{array}\right.$$

(4)

where l, m, and u are the lower, mean, and upper bounds, respectively, of the fuzzy number $\tilde{N}$. TFN can be denoted by $\tilde{N}$ = (l, m, u). The operational laws of TFN $\tilde{N}$ = (l₁, m₁, u₁) and $\tilde{N}$ = (l₂, m₂, u₂) are provided by Equations (5)–(9) (Figure 5).

Addition of the fuzzy number ⨁:

$$\widetilde{N_1}⨁\widetilde{N_2}=\left(l_1,m_1,u_1\right)⨁\left(l_2,m_2,u_2\right)=\left(l_1+l_2,m_1+m_2,u_1+u_2\right)$$

(5)

Subtraction of the fuzzy number ⊝:

$$\widetilde{N_1}⊝\widetilde{N_2}=\left(l_1,m_1,u_1\right)⊝\left(l_2,m_2,u_2\right)=\left(l_1-l_2,m_1-m_2,u_1-u_2\right)$$

(6)

Multiplication of the fuzzy number ⊗:

$$\widetilde{N_1}\otimes \widetilde{N_2}=\left(l_1,m_1,u_1\right)\otimes \left(l_2,m_2,u_2\right)=\left(l_1{l}_2,m_1{m}_2,u_1{u}_2\right)$$

(7)

for $l_1,l_2>0,m_1,m_2>0,u_1,u_2>0$.

Division of the fuzzy number ⊘:

$$\widetilde{N_1}\oslash \widetilde{N_2}=\left(l_1,m_1,u_1\right)\oslash \left(l_2,m_2,u_2\right)=\left(l_1{/l}_2,m_1/m_2,u_1/u_2\right)$$

(8)

for $l_1,l_2>0,m_1,m_2>0,u_1,u_2>0$.

Reciprocal of the fuzzy number:

$${\tilde{N}}^{-1}={\left(l_1,m_1,u_1\right)}^{-1}={\left(1{/l}_1,1/m_1,1/u_1\right)}^{-1}$$

(9)

for $l_1,l_2>0,m_1,m_2>0,u_1,u_2>0$.

In

Table 3. Fuzzy conversion scale of FAHP pair-wise comparisons [103].

Saaty Scale	The Relative Importance of the Two Sub-elements	Fuzzy Triangular Scale	Reciprocal Fuzzy
1	Equally important	1, 1, 1	1, 1, 1
2	Intermediate value between 1 and 3	1, 2, 3	1/3, 1/2, 1
3	Slightly important	2, 3, 4	1/4, 1/3, 1/2
4	Intermediate value between 3 and 5	3, 4, 5	1/5, 1/4, 1/3
5	Important	4, 5, 6	1/6, 1/5, 1/4
6	Intermediate value between 5 and 7	5, 6, 7	1/7, 1/6, 1/5
7	Strongly important	6, 7, 8	1/8, 1/7, 1/6
8	Intermediate value between 7 and	7, 8, 9	1/9, 1/8, 1/7
9	Extremely important	9, 9, 9	1/9, 1/9, 1/9

, the triangular fuzzy scale implemented in the current study is introduced. For an example, let us assume that the criterion i has been strongly ranked by experts compared to criterion j. The criterion i will be evaluated with fuzzy number $\tilde{N}$ = (4, 5, 6). Alternatively, in the case where criterion j appears less important than criterion i, the pairwise comparison between criteria j and i could be represented by the reciprocal of the fuzzy number: ${\tilde{N}}^{-1}$ = (1/6, 1/5, 1/4).

2.3.2. Determining the Linguistic Variables

Linguistic variables are words or sentences in a natural or artificial language. Each membership function (scale of fuzzy numbers) is defined by three parameters for a symmetrical triangular fuzzy number (i.e., left, middle, and right points) in the range over which a function is defined (see Table 3).

2.3.3. The Fuzzy AHP Method

Step 1: Conduct pair-wise comparison matrices for all criteria in the dimensions of the hierarchy system. Equation (10) shows that $\widetilde{d_{ij}^k}$ represents the $k^{th}$ decision-maker’s preference of the $i^{th}$ criterion over the $j^{th}$ criterion via TFNs.

$$\widetilde{A^k}=\left[\begin{array}{cc}\begin{array}{cc}\widetilde{d_{11}^k}& \widetilde{d_{12}^k}\\ \widetilde{d_{21}^k}& \widetilde{d_{22}^k}\end{array}& \begin{array}{cc}\dots & \widetilde{d_{1n}^k}\\ \dots & \widetilde{d_{1n}^k}\end{array}\\ \begin{array}{cc}⋮& ⋮\\ \widetilde{d_{n1}^k}& \widetilde{d_{n2}^k}\end{array}& \begin{array}{cc}\ddots & ⋮\\ \dots & \widetilde{d_{ij}^k}\end{array}\end{array}\right]$$

(10)

Step 2: If more than one decision-maker is present, then the preferences for each decision maker are averaged, as shown in the following equation:

$$\widetilde{d_{ij}^k}=\frac{{\sum }_{k=1}^k\widetilde{d_{ij}^k}}{K}$$

(11)

Step 3: Update the pair-wise comparison matrices for all criteria in the hierarchy system dimensions on the basis of the averaged preferences.

$$\tilde{A}=\left[\begin{array}{ccc}\widetilde{d_{11}}& \cdots & \widetilde{d_{n1}}\\ ⋮& \ddots & ⋮\\ \widetilde{d_{1n}}& \cdots & \widetilde{d_{nn}}\end{array}\right]$$

(12)

Step 4: Use the geometrical mean technique to define the fuzzy geometrical mean and fuzzy weights of each criterion.

$$\widetilde{r_i}={\left({\prod }_{j=1}^n\widetilde{d_{ij}}\right)}^{1/n},i=1,2,\dots ,n$$

(13)

Step 5: Determine the fuzzy weight of the criteria.

$$\widetilde{w_i}={\widetilde{r_i}\otimes (\widetilde{r_1}\oplus \widetilde{r_2}\dots \oplus \widetilde{r_n})}^{-1}$$

(14)

Step 6: Calculate the average and normalized weight criteria.

$$M_i=\frac{\widetilde{w_1}\oplus \widetilde{w_2}\oplus \dots \oplus \widetilde{w_n}}{n}$$

(15)

$$N_i=\frac{M_i}{M_1\oplus M_2\oplus \dots \oplus M_n}$$

(16)

3. Empirical Data Analysis

3.1. AHP Model Construction

The first step in the proposed AHP model is determining alternatives and criteria. For this purpose, a comprehensive literature review was carried out and most of the alternatives and criteria were derived through previous studies [50,68,69,94] (also [70,104,105,106,107,108,109,110,111,112]). In addition to the above methodology, the determination of alternatives and criteria was further refined through comprehensive discussions held during professional meetings with experts in this field. The criteria were effectively categorized into the following groups: social, economic, environmental, and technical. The social group encompasses factors such as new job creation and public acceptance. The economic group consists of operating costs and investment costs. The environmental group considers aspects such as air pollution and energy consumption. Finally, the technical group considers factors related to final quality and technical feasibility. These classifications are presented in

Table 4. Description of criteria and sub criteria in the analytical hierarchy process (AHP) model.

Main Criteria	Sub Criteria	Description
Economic	Operating costs (E1)	All operating costs occurred during the alternative’s operation regarding concrete management.
	Investment costs (E2)	All investment costs occurred during the alternative’s operation regarding concrete management.
Environmental	Air pollution (EN1)	Contaminants were distributed in the air during the alternative’s operation regarding concrete management.
	Energy consumption (EN2)	Energy consumption during the alternative’s operation regarding concrete management.
Social	New job creation (S1)	New employment opportunity during the alternative’s operation regarding concrete management.
	Public acceptance (S2)	Creating motivations to stimulate public acceptance for the alternative’s operation regarding concrete management.
Technical	Final quality (T1)	Efficiency and quality of the finished product after the alternative’s operation regarding concrete management.
	Technical feasibility (T2)	Technology requiring expert personnel for the alternative’s operation regarding concrete management.

and

Table 5. Description of concrete waste management alternatives used in AHP analysis.

Concrete Waste Management Alternative	Description
Landfill (LA)	All construction and demolition concrete waste is eliminated in landfill.
Recycled coarse aggregate (RCA)	Recycled coarse aggregate comes from the concrete pile remains from demolition, such as concrete pillars, beams, and walls, crushed into a standard size. The reinforced steel was separated from the concrete. The concrete was used as a substitute for Natural Coarse Aggregate (NCA) such as natural rock and gravel.
Upcycling recycled concrete. (URC)	Recycled concrete (RC) means concrete that is composed of mortar and recycled coarse aggregate (RCA), combined to produce new materials, new products with better quality, or new products with better environmental conservation value.

for reference. Additionally, the decision hierarchy for AHP was constructed, as shown in Figure 6.

3.2. Pairwise Comparison by Solicitation of Experts’ Opinions

Upon receiving the responses from the online survey conducted among 20 experts, a rigorous analysis of the data was conducted. The experts were asked to evaluate, score, and compare various factors related to the management of concrete waste based on the objectives established for each issue. The priority values for each factor were determined through the process of pairwise comparison, where the experts indicated the factors that were either more or less important in relation to other factors influencing concrete waste management. These comparisons were represented using numerical values ranging from 1 to 9, as outlined in

Table 6. An example of an expert scoring for a pairwise comparison between each important factor which affects concrete waste management.

Criteria	9	8	7	6	5	4	3	2	1	2	3	4	5	6	7	8	9	Criteria
Economic											x							Environmental
Economic											x							Social
Economic							x											Technical
Environmental							x											Social
Environmental					x													Technical
Social							x											Technical

. The experts provided their assessments by marking factors that were either superior or equal to other factors in terms of their impact on concrete waste management.

Subsequently, the pairwise comparison results were translated into a matrix format, known as the pairwise comparison matrix. The weight for each factor within each matrix was then calculated using the eigenvector method, implemented in Microsoft Excel. In accordance with the hierarchical structure, experts made comparisons between factors in a pairwise manner. The analytic hierarchy process (AHP) was employed to ensure the consistency and agreement of experts’ decisions before proceeding with the prioritization process. The desired method was then applied, and the data were further analyzed using fuzzy AHP with the geometric mean method.

This comprehensive approach ensured that the experts’ judgments and preferences were carefully considered and incorporated into the decision-making process, ultimately leading to robust and reliable outcomes.

The experts’ assessments regarding the importance of each factor in concrete waste management are presented in the matrix table below. The ratings for each factor were compared pairwise, and the results of these comparisons are displayed in

Table 7. Examples of evaluating the values of design under cost criteria.

Criteria	Economic	Environmental	Social	Technical	Eigenvector
Economic	1	1/3	1/3	3	0.159
Environmental	3	1	3	5	0.501
Social	3	1/3	1	3	0.263
Technical	1/3	1/5	1/3	1	0.077

.

Matrix A

$$\mathrm{A}=\left[\begin{array}{cccc}{a}_{11}& a_{12}& a_{13}& a_{1j}\\ a_{21}& a_{22}& a_{23}& a_{2j}\\ a_{31}& a_{32}& a_{33}& a_{3j}\\ a_{41}& a_{42}& a_{43}& a_{4j}\end{array}\right]$$

For example, if a₂₄ = 5, then the Environmental (EN) objective is slightly more important than the Technical (T) objective, and a₄₂ = 1/5.

Then, the corresponding normalized matrix is:

Example a₁₁ = 1/(1 + 3 + 3 + 1/3) = 0.136

a₁₂ = 1/3/(1/3 + 1 + 1/3 + 1/5) = 0.179

a₂₂ = 1/(1/3 + 1 + 1/3 + 1/5) = 0.536

a₁₄ = 3/(3 + 5 + 3 + 1) = 0.250

$$\mathrm{A}{ }_{\mathrm{Normalized}}=\left[\begin{array}{ccc}0.136& 0.179& \begin{array}{cc}0.071& 0.250\end{array}\\ 0.409& 0.536& \begin{array}{cc}0.643& 0.417\end{array}\\ \begin{array}{c}0.409\\ 0.045\end{array}& \begin{array}{c}0.179\\ 0.107\end{array}& \begin{array}{cc}\begin{array}{c}0.214\\ 0.071\end{array}& \begin{array}{c}0.250\\ 0.083\end{array}\end{array}\end{array}\right]$$

Eigenvector = (0.136 + 0.179 + 0.071 + 0.250)/4 = 0.159,

(0.409 + 0.536 + 0.643 + 0.417)/4 = 0.501, (0.409 + 0.179 + 0.214 + 0.250)/4 = 0.263,

(0.045 + 0.107 + 0.071 + 0.083)/4 = 0.077.

Following the pairwise comparisons, we computed the consistency index (C.I.) according to Saaty’s method [55]. The consistency index measures the degree of inconsistency within the judgments provided. This metric allows for a systematic approach to enhance the consistency of the evaluations.

The measure of inconsistency was applied to ensure the accuracy and reliability of the judgments made by the experts. By analyzing the consistency index, adjustments can be made to improve the consistency of the provided judgments. The resulting matrix, displaying the calculated consistency index values is presented below:

$$\left[\begin{array}{ccc}1& 1/3& \begin{array}{cc}1/3& 3\end{array}\\ 3& 1& \begin{array}{cc}3& 5\end{array}\\ \begin{array}{c}3\\ 1/3\end{array}& \begin{array}{c}1/3\\ 1/5\end{array}& \begin{array}{c}\begin{array}{cc}1& 3\end{array}\\ \begin{array}{cc}1/3& 1\end{array}\end{array}\end{array}\right]\times \left[\begin{array}{c}0.159\\ 0.501\\ \begin{array}{c}0.263\\ 0.077\end{array}\end{array}\right]=\left[\begin{array}{c}0.644\\ 2.152\\ \begin{array}{c}1.138\\ 0.318\end{array}\end{array}\right]$$

λ_max = ((0.644/0.159) + (2.152/0.501) + (1.138/0.263) + (0.318/0.077))/4 = 4.201;

C.I. = (λ_max − n)/(n – 1);

C.I. = (4.201 − 4)/(4 − 1) = 0.067;

C.R. = C.I./R.I. = 0.067/0.90 = 0.075 < 0.1 (The judgments are consistent).

The consistency ratio (C.R.) is determined by comparing the consistency index (C.I.) with the corresponding values from the sets of random index (R.I.) numbers provided in Table 2. For this study, with a total of N = 4 factors, the appropriate R.I. value was 0.90. The R.I. values represent the average random consistency index obtained from a large sample of reciprocally generated matrices using a scale ranging from 1/9 to 9.

To evaluate the consistency of the judgments made by the decision-makers, the C.R. was calculated and compared to the threshold value of approximately 0.10. If the C.R. fell within this range, it indicates that the judgments were deemed consistent. In the present study, all of the judgments provided by the decision-makers demonstrated consistency, since the C.R. values were below the threshold of 0.10.

As part of the methodology, the consistency of the pairwise comparison data obtained from each expert (totaling 20 experts) was assessed using the analytical hierarchy process (AHP) approach. This step was undertaken to ensure the reliability and accuracy of the data, before proceeding with the prioritization of weights using the fuzzy analytic hierarchy process (FAHP) with the geometric mean method.

3.3. Implementation of the Fuzzy Analytic Hierarchy Process (FAHP) Method

Once the problem hierarchy structure had been defined and the consistency ratio (CR) had been checked using the analytic hierarchy process (AHP), the subsequent step involved estimating data to prioritize factors and identify the optimal alternative for problem resolution. To accomplish this, the fuzzy analytic hierarchy process (FAHP) was employed. The FAHP methodology allows for a more comprehensive and nuanced analysis by considering the uncertainty and vagueness that may exist in decision making. By incorporating fuzzy logic principles, the FAHP enables a more flexible and robust evaluation of alternatives and criteria.

Therefore, in this study, the FAHP was utilized to estimate the necessary data for prioritizing factors and ultimately determine the most suitable alternative for addressing the problem at hand. This approach considers various aspects of uncertainty and ensures a more comprehensive and accurate decision-making process. The FAHP method encompasses the following steps:

Step 1: The fuzzy conversion scale of the FAHP pair-wise comparison in Table 3 is used to construct a matrix for each criterion.

Steps 2 and 3: Since there were twenty decision-makers, the preferences of each decision-maker are averaged using Equation (11); the following calculation shows how to average the preference for the first criterion (Economic) over the second criterion (Environmental).

Table 8. Comparison matrix for criteria.

Criteria	Economic	Environmental	Social	Technical
Economic	(1, 1, 1)	$(1, \frac{3}{2}, 2)$	$(1, \frac{3}{2}, 2)$	$\left(\frac{12}{7}, \frac{5}{2}, \frac{13}{4}\right)$
Environmental	$\left(\frac{3}{2}, 2, \frac{5}{2}\right)$	(1, 1, 1)	$\left(\frac{11}{6}, \frac{5}{2}, \frac{16}{5}\right)$	$\left(\frac{17}{7}, \frac{23}{7}, \frac{29}{7}\right)$
Social	$\left(\frac{8}{5}, \frac{11}{5}, \frac{17}{6}\right)$	$(\frac{6}{7},\frac{6}{5},\frac{8}{5})$	(1, 1, 1)	$\left(\frac{19}{9}, \frac{14}{5}, \frac{7}{2}\right)$
Technical	$(\frac{1}{2}, \frac{5}{7}, 1)$	$\left(\frac{4}{9}, \frac{3}{5}, \frac{5}{6}\right)$	$\left(\frac{2}{3}, 1, \frac{4}{3}\right)$	(1, 1, 1)

shows the pair-wise comparison matrices for all of the criteria in the hierarchy system dimensions based on average preferences.

$${\stackrel{\mathrm{~}}{\mathrm{d}}}_{12}=\left(\begin{array}{c}\mathrm{a}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{g}\mathrm{e}\left(2,\frac{1}{4},2,1,\frac{1}{6},2,\frac{1}{4},\frac{1}{4},3,\frac{1}{6}2,2,\frac{1}{4},2,1,\frac{1}{7},\frac{1}{4},\frac{1}{4},\mathrm{1,1}\right),\\ \mathrm{a}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{g}\mathrm{e}\left(3,\frac{1}{3},3,1,\frac{1}{5},3,\frac{1}{3},\frac{1}{3},4,\frac{1}{5},3,3,\frac{1}{3},3,2,\frac{1}{6},\frac{1}{3},\frac{1}{3},\mathrm{1,1},\right),\\ \mathrm{a}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{g}\mathrm{e}\left(4,\frac{1}{2},4,1,\frac{1}{4},4,\frac{1}{2},\frac{1}{2}5,\frac{1}{4},4,4,\frac{1}{2},4,3,\frac{1}{5},\frac{1}{2},\frac{1}{2}1,1\right)\end{array}\right)=\left(1,\frac{3}{2},2\right)$$

Step 4: Calculate the fuzzy geometric mean. The economic criteria are calculated using Equation (13). Then, the same procedure is repeated for each criterion.

$${\stackrel{\mathrm{~}}{\mathrm{r}}}_1=\left[{\left(1\ast 1\ast 1\ast \frac{12}{7}\right)}^{\frac{1}{4}},{\left(1\ast \frac{3}{2}\ast \frac{3}{2}\ast \frac{5}{2}\right)}^{\frac{1}{4}},{\left(1\ast 2\ast 2\ast \frac{13}{4}\right)}^{\frac{1}{4}}\right]=\left(1.1809, 1.5357, 1.8832\right)$$

Table 9. Fuzzy geometric mean for criteria.

Criterion	${\tilde{\mathit{r}}}_{\mathit{i}}$
Economic	(1.1809, 1.5357, 1.8832)
Environmental	(1.6056, 2.0101, 2.4067)
Social	(1.3016, 1.6432, 1.9917)
Technical	(0.6358, 0.8033, 1.0125)
Total	(4.7240, 5.9922, 7.2942)
Reverse	(0.2117, 0.1669, 0.1371)
Increasing order	(0.1371, 0.1669, 0.2117)

shows the geometric mean of fuzzy comparison values for each criterion (${\tilde{r}}_i$), the vector summation of each r_i and the inverse power of the summation vector in increasing order.

Step 5: Calculate the fuzzy weight of each criterion, ${\tilde{w}}_i$, using Equation (14). Below is the computation of the fuzzy weight for the Economic criterion:

$${\tilde{w}}_i=\left(1.1809\left(0.1371\right),1.5357\left(0.1669\right),1.8832\left(0.2117\right)\right)=\left(0.1619,0.2563,0.3987\right)$$

The same process is repeated for the calculation of the fuzzy weight for each criterion.

Table 10. Fuzzy weight, weight, relative weight and rank for each criterion.

Criteria	$\mathbf{Fuzzy} \mathbf{Weight} \left({\tilde{\mathit{w}}}_{\mathit{i}}\right)$	$\mathbf{Weight} \left({\mathit{M}}_{\mathit{i}}\right)$	$\mathbf{Relative} \mathbf{Weight} \left({\mathit{N}}_{\mathit{i}}\right)$	Rank
Economic	(0.1619, 0.2563, 0.3987)	0.2723	0.2559	3
Environmental	(0.2201, 0.3355, 0.5095)	0.3550	0.3337	1
Social	(0.1784, 0.2742, 0.4216)	0.2914	0.2739	2
Technical	(0.0872, 0.1341, 0.2143)	0.1452	0.1365	4

shows the fuzzy weight (${\tilde{w}}_i$), the weight ($M_i$), the relative weight of ($N_i$) and the rank for each criterion.

Step 6: Calculate the weight of each criterion. Below is the calculation of the weight for criteria using Equation (15), and by applying the same equation the weight acquired for each criterion was as follows: Economic = 0.2723, Environmental = 0.3550, Social = 0.2914 and Technical = 0.1452, respectively. The weight of each criterion is shown in Table 10.

$$M_1=\frac{0.1619+0.2563+0.3987}{3}=0.2723$$

Find the relative weight and rank all the criteria. Use Equation (16) to calculate the relative weight of each criterion and alternative. The working example below shows how to calculate the weight for the Economic criterion:

$$N_1=\frac{0.2723}{0.2723+0.3550+0.2914+0.1452}=0.2559$$

The same process of calculation is repeated to determine the relative weights for each criterion. The alternative concrete waste management options are ranked according to the following main criteria, with the results shown in Table 10.

The next crucial step in the assessment process involves calculating the score assessment matrix to determine the highest scores for each main criterion associated with concrete waste management options, namely landfill, RCA (recycled concrete aggregate), and up-cycling recycled concrete. The weighted values for the pairwise sub-criteria comparisons for each main criterion can be found in

Table 11. Pairwise comparison of sub-criteria with respect to relative criteria. (Note: CR = 0.0).

Criteria	Economic (E)		Environmental (EN)		Social (S)		Technical (T)		Priority Vector
Sub- criteria	(E1)	(E2)	(EN1)	(EN2)	(S1)	(S2)	(T1)	(T2)
E1	1, 1, 1	$(\frac{3}{2},2,\frac{18}{7})$	-------	--------	--------	--------	--------	--------	0.4634
E2	$(\frac{11}{5},\frac{11}{4},\frac{10}{3})$	1, 1, 1	--------	--------	--------	--------	--------	--------	0.5366
EN1	--------	--------	1, 1, 1	$(\frac{24}{7},\frac{38}{9}, 5)$	--------	--------	--------	--------	0.7144
EN2	--------	--------	$(\frac{1}{2},\frac{2}{3},\frac{5}{6})$	1, 1, 1	--------	--------	--------	--------	0.2856
S1	--------	--------	--------	--------	1, 1, 1	$(\frac{4}{9},\frac{4}{7},\frac{3}{4})$	--------	--------	0.2608
S2	--------	--------	--------	--------	$(\frac{23}{6},\frac{33}{7},\frac{17}{3})$	1, 1, 1	--------	--------	0.7392
T1	--------	--------	--------	--------	--------	--------	1, 1, 1	$(\frac{5}{2},3,\frac{29}{8})$	0.5676
T2	--------	--------	--------	--------	--------	--------	$(\frac{13}{9},\frac{16}{9},\frac{19}{9})$	1, 1, 1	0.4324

.

In the evaluation process, when a criterion consists of several sub-criteria, the ratings assigned to each alternative are multiplied by both the local weights and the global weights of each sub-criterion. As an example for the Economic criterion, which includes the sub-criteria of “operating costs” and “investment costs”, the specific numerical values are provided in

Table 12. Local and global weights of the main criteria and sub-criteria.

Criteria	Weight	Sub-Criteria	Local Weight	Alternatives	Global Weight
Economic	0.256	Operating costs	0.463	Landfill	0.136
				RCA	0.391
				URC	0.472
		Investment costs	0.537	Landfill	0.144
				RCA	0.351
				URC	0.505
Environmental	0.334	Air pollution	0.714	Landfill	0.329
				RCA	0.317
				URC	0.354
		Energy consumption	0.286	Landfill	0.216
				RCA	0.331
				URC	0.453
Social	0.274	New job creation	0.261	Landfill	0.160
				RCA	0.358
				URC	0.482
		Public acceptance	0.739	Landfill	0.095
				RCA	0.385
				URC	0.521
Technical	0.136	Final quality	0.568	Landfill	0.104
				RCA	0.398
				URC	0.498
		Technical feasibility	0.432	Landfill	0.234
				RCA	0.323
				URC	0.443

. The following procedure is followed to determine the priority vector for alternatives related to the Economic criterion:

$$\left[\begin{array}{cc}0.1361& 0.1442\\ 0.3914& 0.3511\\ 0.4724& 0.5046\end{array}\right]\left[\begin{array}{c}0.4634\\ 0.5366\end{array}\right]=\left[\begin{array}{c}0.1405\\ 0.3698\\ 0.4897\end{array}\right]$$

The computed priority values clearly demonstrate that upcycling recycled concrete attained the highest priority score for the Economic criterion, with a value of 0.489. This indicates that, among the various concrete waste management options evaluated, upcycling recycled concrete exhibited the strongest economic advantage.

Subsequently, the priority values for the selection of concrete waste were calculated for all of the main criteria. In the next stage, the focus shifted towards evaluating the most appropriate concrete waste management options that aligned with the objectives of this study. To achieve this, the rating of each alternative was multiplied by the weights assigned to each main criterion, as well as the priority vector representing the relative importance of alternatives for each criterion. By aggregating these calculated values, a priority vector could be obtained which highlighted the best alternatives for concrete waste management. The resulting priority vector is presented below:

$$\left[\begin{array}{c}\begin{array}{ccc}0.1405& 0.2967& \begin{array}{cc}0.1118& 0.1603\end{array}\end{array}\\ \begin{array}{ccc}0.3698& 0.3213& \begin{array}{cc}0.3778& 0.3653\end{array}\end{array}\\ \begin{array}{ccc}0.4897& 0.3820& \begin{array}{cc}0.5104& 0.4744\end{array}\end{array}\end{array}\right]\left[\begin{array}{c}0.2559\\ 0.3337\\ \begin{array}{c}0.2739\\ 0.1365\end{array}\end{array}\right]=\left[\begin{array}{c} 0.1875\\ 0.3552\\ 0.4573\end{array}\right]$$

The calculated priority values indicate that the most optimal alternative for concrete waste management is the utilization of up-cycling recycled concrete, with a priority value of 0.457. Following closely behind is the option of recycled coarse aggregate, with a priority value of 0.355. Lastly, landfilling is the least preferable alternative for concrete waste management, with a priority value of 0.188.

4. Research Results and Discussion

The decision-making process in this study involved assigning weights to different criteria, and the criterion with the highest weight was considered the most important. The FAHP priority weight estimates for each criterion were used to determine the criterion with the highest weight, and the results are presented in Table 12.

According to the findings, experts placed the highest priority on the environmental criteria, with a weight of 0.334. This suggests that construction industry groups involved in the study were particularly concerned about construction and demolition waste issues. The construction industry is widely recognized as a major source of pollution, as its waste significantly contributes to environmental degradation. Unfortunately, a significant portion of construction waste is often disposed of in landfills or open areas, leading to further pollution. The second-highest priority for the construction industry was given to the social criteria, with a weight of 0.274. This implies that the construction industry recognizes its role in enhancing overall quality of life and social welfare. The effective management of concrete waste can positively impact various social aspects, such as improving living conditions, community well-being, and public health. By effectively managing concrete waste, the industry can contribute positively to society by reducing negative impacts and promoting sustainable practices. The economic criteria represented the third-highest priority for the construction industry (0.256), suggesting that the industry values economic considerations in the decision-making process. The efficient management of concrete waste can lead to cost savings, resource optimization, and the potential creation of economic opportunities through the utilization of recycled materials.

This study conducted a comprehensive comparison of different concrete waste management alternatives, focusing on four key criteria: economic, social, environmental, and technical. The findings of this study, as presented in Figure 7, revealed that upcycling recycled concrete waste emerged as the most effective waste management option. Across all four criteria, upcycling recycled concrete received the highest priority score, indicating that it is considered the best method for managing concrete waste.

Upcycling, a process that involves reusing materials to create products of higher value or quality, offers a transformative approach to handling waste concrete. By repurposing waste concrete, it can be transformed into new and useful products, contributing to resource efficiency and reducing the need for virgin materials. This method demonstrates superiority across multiple aspects, including economics, social impact, environmental impact, and technical feasibility. Economically, upcycling recycled concrete can prove advantageous by promoting resource conservation and reducing material costs. This allows for the creation of valuable products, potentially opening up new markets and business opportunities. From a social perspective, upcycling contributes to sustainable development and improves the population’s quality of life. It helps to mitigate the negative impacts associated with traditional waste disposal methods and fosters a positive perception of the construction industry. In terms of environmental impact, upcycling recycled concrete is an environmentally friendly approach. It reduces the dependence on landfilling, which is associated with various environmental issues. Landfilling, on the other hand, emerged as the least-preferred option for managing concrete waste in this study. It obtained the lowest priority scores across all four criteria: environment, economy, social, and technical.

Landfilling involves the dumping of waste materials in designated areas, which can have detrimental effects on the environment. It contributes to land degradation, poses risks to groundwater and soil quality, and generates greenhouse gas emissions. Moreover, landfilling has negative social implications, such as causing the degradation of local communities’ well-being and fostering negative perceptions of the construction industry. From an economic standpoint, reliance on landfilling is not sustainable in the long run due to land scarcity and limited landfill capacities.

This study carried out a comprehensive assessment of various alternative methods for managing concrete waste, and the results indicate that upcycling recycled concrete is the best option, with a priority weight of 0.457. This finding highlights the effectiveness of upcycling recycled concrete compared to other alternatives. Upcycling involves the process of reusing waste concrete to create new and valuable products, contributing to the circular economy and reducing the need for raw materials. By assigning the highest weight to upcycling, this study indicates that it is considered the most favorable method for managing concrete waste. The next most-viable alternatives identified in the study were recycled coarse aggregate, with a priority weight of 0.355, and landfilling, with a weight of 0.188, as presented in Figure 8.

Recycled coarse aggregate involves crushing and reusing concrete waste as a replacement for natural aggregate in new construction projects. While this method provides some level of waste reduction and resource conservation, it falls behind upcycling in terms of its effectiveness. Landfilling, on the other hand, received the lowest priority weight among the alternatives, indicating that it is the least preferred option for managing concrete waste.

By prioritizing the upcycling of recycled concrete, the construction industry acknowledges the need to address these challenges and adopt more sustainable waste management practices. Upcycling not only offers an effective solution for reducing the environmental impact of concrete waste, but also provides economic and social benefits. It allows for the creation of new and valuable products, contributing to job creation, resource efficiency, and the overall sustainability of the construction industry.

In summary, this study’s findings indicate that upcycling recycled concrete is the most effective alternative for managing concrete waste, surpassing other options such as recycled coarse aggregate and landfilling. The prioritization of upcycling reflects the construction industry’s concern about the high volume and weight of concrete waste and highlights the industry’s commitment to sustainable waste management practices. By embracing upcycling and other innovative approaches, the construction industry can mitigate the environmental impact of its activities and contribute to a more sustainable and circular economy.

5. Conclusions

The conclusions of this study highlight several important achievements and contributions to the field of construction waste management. Firstly, in response to research question 1, this study successfully determined an appropriate hierarchical structure for a decision-support system in concrete waste management. The hierarchical chart was divided into four levels, each level containing different definitions and criteria. This hierarchical structure provides a systematic framework for evaluating concrete waste management options and facilitates informed decision-making.

Addressing research question 2, the study emphasized the importance of the circular economy concept in construction waste management. By focusing on environmental criteria and sustainable practices, the research identified upcycling recycled concrete as the best alternative for managing concrete waste, followed by the recycling of coarse aggregates and landfilling. This emphasis on sustainability aligns with the objectives of efficient resource utilization and reducing environmental impacts.

Regarding research question 3, this study proposed the fuzzy analytic hierarchy process (FAHP) as an effective tool for determining the importance of factors in concrete waste management. The FAHP integrates fuzzy logic theory, enabling the evaluation of both quantitative and qualitative data. This approach considers uncertainty and ambiguity in decision-making, mimicking human thought processes. The FAHP is the preferred algorithm for solving construction waste management problems, providing a more comprehensive and nuanced analysis.

Furthermore, this study acknowledges several limitations that should be considered. Firstly, the focus of the CECI Group on residential and commercial construction projects limits the generalizability of the findings. Future research should explore concrete waste management in other construction project types, such as infrastructural, industrial, underground and marine work, to obtain a more comprehensive understanding of waste management practices in diverse contexts. Additionally, the number of experts involved in the FAHP method is crucial, as fewer than five experts may lead to limited preferences that can bias the results. The subjective nature of the FAHP method emphasizes the importance of addressing potential biases introduced by experts’ linguistic judgments. Although the geometric mean can help consolidate individual judgments, it should not be solely relied upon to ensure the validity of the results. To strengthen validity and minimize individual biases, it is advisable to involve more experts who understand the subject matter and theoretical framework underpinning the analytical hierarchical decision-making process. The careful selection of experts who are committed, knowledgeable, and willing to invest their time in providing accurate opinions, coupled with clear and well-designed questionnaires, plays a crucial role in ensuring the reliability and consistency of the information obtained.

To advance the field of construction waste management, the authors have identified key areas that warrant further research. They propose the development of an online technology system that efficiently matches construction waste with potential users, thereby reducing environmental waste. This system would enhance the user experience by integrating with social networking platforms and web-based systems, facilitating improved communication and collaboration among relevant stakeholders. Given that selecting a construction waste management method often benefits from group decision making, the proposed system would enable waste management companies and users to effectively exchange information. Moreover, the authors emphasize the need for the construction industry to enhance its waste matching process to promote the adoption of a circular economy. To achieve this, they suggest designing a web-based system with an intuitive and easily accessible interface, offering advanced search options, waste material listings, and a communication feature to facilitate material transfer negotiations between users.

The research findings and recommendations contribute to the development of effective concrete waste management methods. By incorporating regulatory balance, technology systems, human management and economic management, the proposed management system offers a holistic approach to waste reduction. The introduction of an online technology system to match construction waste with potential users is a particularly innovative suggestion. This system, integrated into social networking platforms and web-based systems, enhances communication, collaboration, and the efficient utilization of construction waste.

Overall, this research work provides valuable insights and serves as a reference and guide for future sustainable operations in various sectors related to the construction industry. The achievements obtained through this study, including the identified hierarchical structure, the emphasis on the circular economy concept, and the application of the FAHP algorithm, contribute to advancing construction waste management practices. These achievements promote waste reduction, enhance efficiency, and foster sustainability in the construction industry.