Using a decision-making tool to select the optimal industrial housing construction system in Tehran

ABSTRACT Industrial dwelling construction (IDC) combines offsite and onsite construction methods. The current work proposes a multiple-criteria decision-making assistant system (DMAS) approach for evaluating home buildings, selecting decision agents, and determining acceptable IDC types and characteristics for large-scale housing enhancement. Using the analytical hierarchy process (AHP), a multicriteria DMAS was planned. Literature reviews and expert interviews were used to assess thirty decision agents. To validate the procedure, five IDCs were analyzed as case studies. Thirty professionals analyzed decision agents' and housing systems' priority vectors using questionnaires. Three decision agents were identified: (1) user needs, (2) the building industry, and (3) the supply chain. Consequently, Prefabricated Reinforced Concrete Slab, Beam, and Column Blocks (PRCSBCB) and Light Steel Frame Wall Panels (LSFWP) can be used to construct suitable dwellings. These construction options are low-cost, lightweight, and easy to install. They also use inexperienced local workers. Results showed the method's potential and usefulness with so many choices and decision agents. This research examines knowledge and skill gaps in Iran's IDC and encounters a shortage of IDC specialists and mass-construction variants. The study identifies key decision-agents and housing options for quick, appropriate, and cost-efficient mass housing and helps professionals, entrepreneurs, and decision-makers make better decisions.


Introduction
Suitable IDC and decision-making systems are available for large-scale and rapid construction, which makes the project more efficient. To promote IDC application, experts must acquire effective IDC decision criteria from local building components. As long as alternatives to IDCs exist, developers must choose the best IDC for mass housing progress. Shen estimates rapid house industrialization when GNP per capita surpasses 600 dollars (Jianzhong 2002). Iran's GNP indicates it has passed rapid industrialization point. Due to high housing demand, developing countries like Iran have struggled to provide adequate and affordable housing (Keivani and Werna 2001). In Tehran, where supply and demand are deeply polarized, low-cost housing is in high demand. In recent decades, neither private nor public housing constructions have met demand. One of the key challenges was selecting the right housing system (Burt, Aron, and Lee 2001). The government planned to address this by creating low-cost IDCs for dwellings. Two variables prevent appropriate IDC justification.: Firstly, IDC evaluations lack explicit decision factors. Many existing methodologies have been criticized for being quantitatively and financially exaggerated . These criticisms have led to inadequate IDC attention. High shipping and import costs, a lack of skills, and local resources have led to IDC failure in many developing nations (MR Abdullah and CEgbu 2010). Secondly, Since multicriteria DMASs use different approaches, many areas for decision-making enhancement remain unaddressed, including weighing, scoring, validation, and outcome testing, notably in IDC assessment (Blismas, Pasquire, and Gibb 2006). To build rapid and affordable housing in underdeveloped nations, particularly Central Asia, it is necessary to select appropriate IDCs in a systematic and effective manner at the appropriate time and location. This study assesses housing systems using an AHPbased multicriteria DMAS. The AHP analyzes the analogical relevance of linked decision agent variables and selects relevant IDCs for housing expansions (Kokaraki et al. 2019;Wong and Li 2008).

Theoretic context
Three main theoretical perspectives help to establish a framework for decision-making analysis (Edwards 1954;Chankong and Haimes 2008). The aim of the framework is to better understand the decision-making process with regard to the evaluation and selection of suitable IDCs (Engström and Hedgren 2012). Multi-methodology in decision making requires the application of appropriate multi-criteria DMASs (Hedgren and Stehn 2014;Dainty 2008).

Multi-criteria DMAS
A multi-criteria decision-making assistant system (DMAS) can organize the necessary information and procedures to allow decision makers to make an informed and effective decision (Saaty 2005). The implementation of a DMAS tool in housing system selection includes the participation of stakeholders and professional advisors (Medineckiene et al. 2015). It needs to meaningfully combine decision factors that influence the decision regarding which type of IDC to use while considering the local context (Zeleny 2012;Chen, Okudan, and Riley 2010;Karamoozian et al. 2019).

Decision agents
Although many decision agents have been employed to study housing systems, criteria integration has been neglected (W Pan, AR Dainty, and AG. Gibb 2012). Pan et al ) presented a conceptual model for synthesizing decision elements using a systemic value-based methodology and determining their importance in making judgments. The decision elements' concepts for analyzing imported dwelling systems are still lacking (Akmam Syed Zakaria et al. 2018). As a consequence of extensive literature study and expert judgment participation, this research addresses a gap in the literature with precise and integrated decision agents (Yu 2013;Shahpari et al. 2020;Pan, Wong, and Hui 2011) As shown in Table 1, the majority of the assessment criteria are composed of well-known concepts such as: The Building industry, User requirements, the Supply chain, Sub-structures, and the Environment and Surroundings, as well as increasingly relevant Civic-political aspects. Effective decision-making requires the integration of theoretical viewpoints with linked data and decision-makers' behavior. As like classical dominant variables, customer demand, affordability, and construction speed are indicated as major decision agents (Buchman 2020).

User requirements
User needs rely on the building construction industry's ability to create affordable home alternatives Luo et al. 2020;Wheeler and Beatley 2014).

Building industry
Because labor costs account for 30 to 50% of total construction costs, professional workforce accessibility determines overall building construction efficiency and sophisticated housing system applications (Jato-Espino et al. 2014;Karimi et al. 2018;Hussain, Xuetong, and Hussain 2020). To meet the huge demand for housing, the local building construction industry's general capacity must be improved (Madanipour 2006).

Sub-structures
Any discussion of construction development must take into account both existing structures and substructures (Schiller 2007). The lower the density of buildings in a given region, the greater the amount of material absorbed by infrastructure (Yang and Kohler 2008).

Environment and surroundings
The environmental role is an un deniable factor in the construction industry (Pries and Janszen 1995).
There are more changes in the environment, which will have a big effect on construction management. One of the most critical strategic decisions is whether or not to specialize or diversify. Energy efficiency, Pollution and Contamination control, Vulnerability conditions such as exposure to the side forces like earthquakes, floods, storms, etc (Oven and Rigg 2015). Recyclability/ losses, Healthiness and safety are the key sub-factors affecting the construction process. New construction processes and materials have addressed several problems raised by building waste, including resource conservation and pollution reduction (Afzali and Hamzehloo 2018). There are many varying conceptions of vulnerability, and many different viewpoints on what might or should not be used in the comprehensive analysis of vulnerability (Rashed and Weeks 2003). Since Iran is located on the earthquakes zone, and especially Tehran is near two major seismic faults, this paper also quantified the primary influencing environmental factors in minimizing susceptibility, and boosting construction ability to respond to environmental changes such as earthquakes, storms, floods, etc (Sadeghi, Ghafory-Ashtiany, and Pakdel-Lahiji 2015).

Civic-political
The process of constructing is a social procedure (Schriewer 2004). Therefore, in building construction systems, the civic-political dimension must be clearly considered (Higgins-D'Alessandro 2010).

Methodology
To fulfill the study's goals, literature studies, question reviews, knowledge extraction interviews, and quantitative and qualitative research were considered (Groat and Wang 2013;Patton 1990). A decision-analysis framework for the research topic requires both qualitative and quantitative work. The research revealed precise decision factors. Interviews with IDC specialists aided in defining key criteria. A questionnaire was used to collect quantitative and qualitative data for the assessment model. An online EOC (Expert Option Comparison) trading software platform based on AHP was used to provide a realistic assessment. The AHP comparison in pairs limits the experts' judgment consistency by status. The EOC allows responders to immediately assess their consistency ratio and weight grade. Additionally, this illustrates that decision-makers are capable of making discerning errors, as long as the errors are within defined limits, such as a constancy ratio of 0.1 or fewer (Saaty 1988).

Expanding the decision-making pattern
Expanding the pattern in three stages: The framework begins by identifying the critical decision elements that drive IDC selection; 2; it then defines the system selection procedure; and 3; it finally discusses the system selection methodology and analysis tools for the given model. Figure 1 depicts the general selection model.

Classifying the related decision elements
To expand the data set units of the decision-making model, a complete set of decision agents must be studied. This study used a multi-method approach to provide a perceptual decision-making framework for selecting IDCs. Professionals were interviewed to discuss IDCs. These interviews centered on the key problems and decision-making agents that affect IDC utilization and the duty of stakeholders in developing its implementation for local building construction. Ten specialists who affect the selection of building materials were interviewed.
Even though the local building sector has minimal experience utilizing IDCs the study picked ten accessible specialists from diverse parts of the local building industry. The experts were picked for their significant involvement in relevant IDC topics. Their experience ranged from university professors to construction executives, governmental politicians, manufacturing CEOs, and a supply department head. The experts were familiarized with the study's goals and methods of the suggested multicriteria decision-making (MCDM) system ( Figure 1) and the listing of defined criteria of relevant work before the interview (Table 1).
The primary interviews with each specialist took an average of 45 minutes. They next completed the questionnaire to categorize the linked decision aspects according to the 40 proposed decision criteria gathered from the related surveys. Considering that the interviews were explanatory/expressive, the answers were analyzed for discussion evaluation by arrangement, interaction, and clarifying information. The questions were categorized and coded. Ten experts rated the value of the influencing decision agents mentioned in Table 1 based on their relationship to IDC application in the local building sector. Among the forty specified decision elements, professionals identified thirty as key influencing factors for employing IDC in local building construction. Table 2 shows the experts' chosen thirty choice agents. As a result, the suggested multicriteria decision-making assistant system is evaluated as a tool and relevant decisionmaking elements are identified. As a consequence of expert interviews and questionnaire research, Table 2 shows the proposed decision agents as primary and sub-criteria.

The multi-criteria decision-making model's framework and elements
In order to build the model, the data generated in multiple modules must be logically linked together. These modules contain information to help with the assessment process. The multi-criteria decision-making assistance technique has interconnected parts. The extended model's analytical framework is shown in Figure 2.

The model for multi-criteria decision making
The basic MCDM model runs the IDC evaluation. Initially, experts were given a list of IDCs to choose from. The algorithm then consults experts to determine priority weight scores for the primary and subcriteria decision aspects. Professionals then decide on

Using the AHP-MCDM model
The next stage is the evaluation and selection of alternative IDCs. Five Iranian IDCs were chosen among the most common industrialized building constructions to be investigated and evaluated as alternative systems. The most appropriate IDC was determined using AHP in the expanded MCDM model. Professionals with extensive experience in IDC usage and AHP assessment were asked to review the MCDM model to achieve the research's principal purpose. Raw material utilization, product input, manufacturing technique, shipment and implementation, component load and type sorting, and labor involvement in the producing and implementation procedures vary between dwelling systems. The variations in defining characteristics over these five IDCs, as seen in Table 3, made it possible to conduct an in-depth assessment, which both improved the validity of the research findings and provided concepts for conducting additional research. The attributes of the dwellings were the subject of a comprehensive field study analysis, which was carried out in order to collect vital information regarding these characteristics.

Justifications for the use of AHP
Earlier studies have established decision-assistant approaches and procedures. The choice hypothesis and its application analysis (Keeney, Raiffa, and Meyer 1993), ranking by surrogate weights (Kunsch and Ishizaka 2019), weighted summation model (Marler and Arora 2010), and the Theorem of multiple-attribute value (Von Winterfeldt and Edwards 1993;Dyer 2005) are a few examples. The AHP aided experts' judgments in this study as an assessment tool. The AHP uses quantitative and qualitative methods to identify and prioritize essential success factors (Armacost et al. 1994;Banihashemi et al. 2017). AHP organizes a multi-dimensional complication into components with aims, entities, sub-agents, and alternatives to the qualitative method (Zanakis et al. 1998). The AHP uses pairwise comparison as a quantitative technique to rank the agents in order of importance. The consistency ratio must be less than 0.1 to be considered dependable. Using the technique to assess the consistency helps provide correct priority measures based on decision agents and alternatives to housing (Saaty 2008). This research uses AHP as it is mostly a simple conceptual approach to real-world situations (Saaty 2003). The work also uses a standard formula to determine an individual's or group's inevitable personal priorities (Saaty 2000). The AHP significance level obtained in the MCDM model under the uncertainty problem meets an additive value function (Lin, Wang, and W-D 2008). AHP elicitations are used to measure multi-attribute value (Dyer 1990). Also, normative concerns are range-sensitive in attribute weight (Yeh 2002). AHP selection and evaluation is depicted in Figure 3.

The deciding hierarchy's problem modeling
The standard AHP enables quick, straightforward, and classified problem segmentation (Sangiorgio, Uva, and Fatiguso 2018). Figure 4 shows the problem rating expression, primary and sub-criteria determination, and various possibilities to meet the primary aims. The primary purpose is to choose the IDC that is best suited for application in the mass housing development being carried out in the city of Tehran. After that, a group of possible architectural frameworks for dwellings that were accessible for selection was arranged ( Table 3). The approaches were selected on the basis of the knowledge-based regulations and their widespread application in project cases in Iran. Among the most prevalent industrialized building structures in Iran, five IDCs were selected for investigation and evaluation and constructing the MCDM assistant system as alternative solutions. The unique characteristics of these five IDCs enabled a comprehensive examination, which enhanced the validity of the study's findings and gave lessons for future research. The housing systems that are going to be evaluated are going to be chosen from the realistic case studies that can be found in the present research. In addition, the aspects of the suitable IDCs that have been identified are outlined in this article in order to promote the efficient application of IDC for the development of cost -effective accommodation.

Weight assessment of AHP
Assessment of intended decision agent value and choice weights follows the structural decision hierarchy arrangement. According to n arranged agents of comparison (for example, primary agents, secondary decision agents, or options concerning the primary and secondary decision agents), an (n × n) decision Matrix B is determined, Where the peak diagonal element b ij > 0 is generated via the Saaty ratio scales comparison of the ith element with jth (See Figure 5). The demonstration for the semantic coefficient scale is in Table 4, which contains numeric amounts (Jianzhong 2002 Solving the following eigenvector equation provides the weights: while δ is a particular eigenvector regarding the principal eigenvalue θ max . If slight changes are represented, then it accepts θ max �n  For one of the EOC objectives, the right-side normalized main eigenvector is computed as follows: where S is SumTotal; n stands for the objectives integer; c i is the ratio result for the option of the ith target, and d i is the global priority grade of the ith target. Eventually, the approach is validated by doing a sensitivity evaluation to assess the solution's stability.

Decision agents' weight assessment
The priority weight of primary and secondary decision agents is determined by pairwise comparisons of their respective importance rates. The AHP survey included ten experts with AHP knowledge and IDC program experience. Here's how experts are chosen: Primarily, the experts' associations provided a list of available experts. Outstanding professionals in each group also offered suggestions. On the basis of their recommendations, candidates with more IDC experience were chosen. Finally, a balanced view was achieved by selecting a similar number of participants from each group.
With the EOC, you can quickly conduct questionnaires and make assessments online. The EOC spontaneously supports the respondents in reviewing their best decisions' consistency ratio and weight. The consistency ratio should be smaller than 0.1 with this system. In order to determine the decision factors' relative importance, each pair of comparison matrices was treated as an equivalent eigenvalue problem (note Table 5).

Alternative IDCs' preferential weight assessment
The next stage was to obtain the overall normalized score and values for each dwelling system. This was followed by the weight value of each main and minor agent. According to AHP, a pairwise comparison was also performed by some selected experienced specialists from the Tehran building construction. The dearth of experienced professionals in IDC is due to the absence of practice in developing and performing industrial construction methods for local building construction. To address the constraints, an ordered questionnaire and redone field tests on IDC reliability options bound by visiting and conversing with the same people in multiple cases were utilized. Also, alternative IDCs were documented.
Thirty experts were chosen using the methods described in Section 4.4. Architects, executive directors, chairmen of production bureaus of prefab manufacturers, bosses of construction departments of producer companies, and professors of civil engineering, construction, and architecture departments of universities were major contributors. The specialists had 18 years of expertise on average. To ensure a balanced analysis, each category had an equal number of participants. Participants as individuals were also selected based on the suggestion of each group's expert leader,  Two activities with the same contribution Expertise and determination moderately recommend a step over the other Expertise and determination forcefully recommend a step over the other An action is advised Severely over the other At least an enormity order advises an action The accordance among two decisions Expertise and determination refuse a step over the other as well as their IDC skills and expertise. The Saaty Ratio Scale assigned to each secondary agent determined the relevance of five IDC options (Table 4). Finally, IDC's five normalized favorite importance levels were rated. Then the top two IDCs were chosen for Tehran's home expansions.

Results and discussions
The study identifies a gap in the creation of an effective decision-making model for accommodation in developing and underdeveloped countries, notably in the Middle East. The data show that the MCDM framework can be used to assess the decision variables and alternative IDCs that affect the selection of the most appropriate IDCS for residential development. The decision-making method utilized in this research enables clear and specific and understandable verification of the derived values of decision agents and acceptable solution IDCs based on the local background. Furthermore, the use of theoretical pluralism enhances understanding of the fundamental assessment in decision making.

Substantial decision agents
As shown in Table 5, the total scores on every main component indicate that the top three elements are the most influential in Tehran when it comes to dwelling system selection. Surroundings and Environment, Sub-structure, and Civic-political issues, on the other hand, are determined to be less significant. The results also indicated that the most important elements to affect the selection of IDC's application include factors such as affordability, new investment methods, raw material and substances, vulnerability conditions and the availability of trained labors. The following is a discussion of the top three main decision-making agents.

User requirements
Customer needs are contingent upon the construction industry's capacity to meet the challenge of providing adequate homes at a reasonable price. According to Table 5, under user requirements, Reasonable price and Standards and quality, are the most essential criteria influencing the decision to pick a suitable IDC. IDCs technology advancements in Iran are also geared   toward resolving challenges of price and quality. Making good housing more widely available is a difficult challenge in Iran. The lack of housing is due to the excessive concentration of the population in big cities, especially the capital of the country, Tehran. And also, the country's economic policy has made housing a commercial commodity, and there are even a large number of empty apartments in Tehran. Another obstacle to promoting the industrialization of construction and housing in Iran is the risk of increasing the unemployment of many workers who work in the traditional housing construction industry. In Tehran, this problem is more serious than in other cities, where slum dwellers are steadily increasing both outside and inside the capital. It is largely due to a lack of suitable materials for quality affordable housing. So, implementing affordable IDC initiatives helps establish and accelerate highquality housing construction.

Supply chain
Another critical component to address when selecting the proper housing system for effective dwelling development is the supply chain. Within the supply chain, new investment methods and Production capacity are critical sub-factors. The government's efforts to expand manufacturing capacity and build houses are dependent on imported resources. This issue exacerbates the difficulty of implementing IDCs on a wide scale in home construction in Iran.

Building industry
The building industry is shown in Table 5 as the third significant decision agent. Many sub-agents in the construction sector support this status. The two most important factors in the building construction sector are the number of skilled workers and the ability of the dwelling system to produce fast and easy buildings. These agents have a substantial impact on increasing building output and productivity. Typical of Tehran's construction industry is the use of conventional methods. Workers from rural areas and neighboring countries make up the majority of the construction crews, and many of them lack formal training. This has had a negative impact on the housing systems' production and advancement. A significant amount of focus should be placed on increasing the entire capacity of the local building industry in order to accomplish rapid dwelling development.

The five IDC options prioritization
Tables 6 and 7 show that of the five IDC alternatives, precast reinforced concrete slab, beam, and column blocks PRCSBCB and light steel frame wall panels LSFWP are the most suitable for use in Tehran. These practical solutions connect to the local environment's economic and civic gains. Figure 6 reveals that the PRCSBCB and LSFWP outperform other IDC solutions in all major decision agents. These housing solutions are lightweight, easy to assemble, and cost-effective for mass production. Compared to the typical concrete beam and slab method, PRCSBCB reduces expenses by 40% and material waste by 30% (Lizarralde 2014;Noorzai, Gharouni Jafari, and Moslemi Naeni 2020). LSFWP halves the cost of standard hollow concrete walls while having higher physical and mechanical attributes. Because these panels eliminate the need for cement plastering, less water is used during construction. PRCSBCB and LSFWP are semi-IDC groups that require low-to-medium funding. They necessitate only rudimentary skills, rely on locally sourced materials, and employ small to medium-sized machines and tools. These mass-produced components follow a modular pattern, size, and shape. In order to suit the needs of a varied spectrum of end users with varying budgetary situations, prefabricated construction components are an ideal and costeffective choice. These IDCs are flexible systems compatible with various conventional and newer approaches. As shown in Figure 7(a)-(c) a high score of substantial decision agents distinguishes PRCSBCB and LSFWP from other advanced systems (see Table 5). However, these systems are constrained by material shortages, component fragility in transit, joint fractures, and usage rigidity. The PRCSBCB's maximum span is five meters, restricting its potential to create huge expanses. Building multi-story buildings is equally difficult for PRCSBCB. Joint fractures that require prompt and continuous rehabilitation are common. LSFWP has the same fracture problem unless the joints are completely sealed after construction. Since it collects water from the environment, it requires a special treatment in humid places. Inadequate supply chains and production volume are among the problems that prevent installation on a huge scale of useful residential systems.

In brief, the features of suitable IDCs
In Table 8, the advantages and disadvantages of the two most significant IDCs for implementation in Tehran are outlined. In addition, Table 9 displays the fundamental values, which include structures, architectural details, industrialization procedures, shipping and installation issues, as well as political and social perspectives. In determining whether residential systems are suitable for use in developing nations like Iran, these characteristics are also crucial factors.

Conclusion
This study examines the AHP decision-making process to help Iran's IDC select large-scale affordable housing expansions. The investigation indicates the absence of a decision-making assistant system in Iran. Thirty decision agents are identified in the literature analysis under six key decision agents: the building industry, user requirements, supply chain, sub-structures, surrounds, and increasingly important civic-political agents.
The MCDM assistance system model alternatives chosen were the five existing IDCs. Using AHP, housing experts and developers can select appropriate IDCs and compare possibilities among decision agents. The expert's judgment was tested using EOC trading software. The most important decision agents are users' needs, the supplier chain, and the construction industry. The best programs for home expansions  are PRCSBCB and LSFWP. The study's findings acknowledge the model's viability and usefulness. The proposed method for developing the MCDM prototype demonstrated that the Decision-Making Assistant System DMAS could be efficiently used to measure the significance of materials. There were few IDC possibilities and limited IDC installation practice in large-scale housing developments. These obstacles hampered information collection. All accessible building construction professionals with IDC capabilities were asked to participate. Alternative IDCs from the marketplace are picked for assessment. Additionally, the analytical evaluation system utilized to identify key decision agents and typical requirements for affordable house building is intended to benefit home builders and policymakers in developing nations.

Possible future research areas
The model constituent elements can be designed or modified to suit the needs of each specific country. Although this research summarizes a subset of IDC options for dwelling expansion, the study's technique can be authorized in different contexts to prove its validity.

Disclosure statement
No potential conflict of interest was reported by the author(s).

Funding
This work was supported by the National key R & D plan of the 14th Five-Year Plan of High-quality green building design methods and intelligent collaboration platform [NO.2022YFC3803800].

Data availability
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.