Abstract:

Sustainable decision-making in civil engineering, construction and building technology can be supported by fundamental scientific achievements and multiple-criteria decision-making (MCDM) theories. The current paper aims at overviewing the state of the art in terms of published papers related to theoretical methods that are applied to support sustainable evaluation and selection processes in civil engineering. The review is limited solely to papers referred to in the Clarivate Analytic Web of Science core collection database. As the focus is on multiple-criteria decision-making, it aims at reviewing how the papers on MCDM developments and applications have been distributed by period of publishing, by author countries and institutions, and by journals. Detailed analysis of 2015–2017 journal articles from two Web of Science categories (engineering civil and construction building technology) is presented. The articles are grouped by research domains, problems analyzed and the decision-making approaches used. The findings of the current review paper show that MCDM applications have been constantly growing and particularly increased in the last three years, confirming the great potential and prospects of applying MCDM methods for sustainable decision-making in civil engineering, construction and building technology.

Keywords: civil engineering; construction building technology; sustainability; decision-making; MCDM; literature review

 

Introduction

Civil engineering fundamentally relies on established scientific principles, with the design and construction of infrastructure and buildings deeply rooted in core disciplines such as mathematics, physics, and chemistry. In recent years, specifically from 2013 to 2017, several comprehensive review articles have emerged that explore advancements in these foundational sciences and their applications within civil engineering and the broader building and construction sectors. Notably, optimization techniques inspired by natural phenomena grounded in chemistry [1], physics [2], and other natural sciences [3] have been extensively documented. Innovative algorithms, including gravitational search [4], simulated annealing [5], and central force metaheuristic optimization [6], have been applied as nature-inspired heuristic frameworks for solving complex engineering challenges. Considerable emphasis has been placed on vibration mitigation and structural health monitoring for various engineering constructs, encompassing bridges [12,13], high-rise buildings [14–16], and general structural systems [7–11]. A thorough review on the application of tuned mass dampers as a vibration control mechanism further consolidates this field [17].

In addition to these physical science applications, a number of critical review papers focus on civil engineering issues facilitated by advances in information technology. The deployment of support vector machines for structural engineering tasks has been explored [18,19], alongside the role of artificial neural networks in optimizing and monitoring civil infrastructure [20]. Automation technologies within construction operations, including the integration of automated equipment in building phases, have also been systematically reviewed [21]. Moreover, transportation infrastructures and related technological innovations are evaluated comprehensively [22]. Given the growing significance of sustainable development, research output focusing on sustainability within construction has notably increased, as illustrated in Figure 1. Topics addressed include sustainable and efficient structural design [23,24], sustainable building methodologies [25], sustainability considerations in tall building designs [26], and integrated sustainable building planning [27]. Furthermore, models for structural health monitoring of high-rise buildings [28] and vibration control in smart structural systems [29] with sustainability in mind are well documented. Urban sustainable design remains pivotal for holistic sustainability efforts [30]. Specific methodologies, such as the time-dependent performance evaluation of electric road infrastructure [31], intelligent building energy management through sense–think–act frameworks [32], and multi-objective optimization of environmentally sustainable road networks [33], are emerging research areas. Additionally, multi-objective seismic resilience optimization for critical infrastructure [34], alternative resilience metrics for urban ecosystems vulnerable to natural disasters [35], and energy harvesting from bridge–vehicle interactions considering road surface and device characteristics [36] further reflect the evolving focus on sustainability and resilience in civil engineering research.

 

Figure 1. Number of publications on the topic “sustainability” in civil engineering and construction building technology Web of Science categories (Web of Science core collection database, 15 October 2017).

 

When implementing sustainability principles, it is essential to address not only technological and economic factors but also environmental and social dimensions. Consequently, decision-makers frequently encounter the challenge of assessing multiple criteria to determine the most appropriate project choices. The complexity arising from heterogeneous and diverse data types can be effectively managed through multi-criteria decision analysis (MCDA) techniques [37]. Multiple-criteria decision-making (MCDM) encompasses a variety of distinct methodologies that are typically divided into two primary categories: discrete multi-attribute decision-making (MADM) and continuous multi-objective decision-making (MODM). This bifurcation originates from two influential schools of thought on human decision behavior: the French and American schools. The French approach predominantly endorses the outranking method for evaluating discrete options, whereas the American perspective relies on multi-attribute value functions and utility theory. Recently, there has been a notable increase in MCDM applications that integrate both MODM and MADM strategies, as illustrated in Figure 2.

 

 

Figure 2. Number of publications on the topic “MCDM” in Web of Science core collection database (15 October 2017).

 

There is a limited number of comprehensive review articles dedicated to the evaluation of multiple-criteria decision-making (MCDM), encompassing both multi-objective decision-making (MODM) and multi-attribute decision-making (MADM), specifically within the context of civil engineering applications. An extensive study was conducted by Kabir et al. [38], providing valuable insights into this area. Similarly, Jato-Espino et al. [39] contributed a review summarizing the predominant multi-criteria techniques and their principal applications within construction. The evolution of MCDM methodologies from their inception in 1772 through to 2015 has been thoroughly chronicled by Zavadskas et al. [40,41], with the earliest documented mention attributed to Franklin’s letter [42]. The pioneering works of Pareto [43] hold significant influence in this domain. Numerous Nobel laureates such as Debreu (1959), Frisch (1969), Samuelson (1970), Arrow (1972), Nash (1994), Kantorovich and Koopmans (1975), Dantzig (1976), and Sen (1998) have profoundly shaped decision theory. Simon’s work (1978) [44] is particularly noteworthy for advancing modern MCDM theory. Other seminal contributions have been made by Saaty [45], Zeleny [46], and Zadeh [47], with the latter introducing fuzzy set theory. In 2015, Herrera-Viedma edited a special issue honoring Zadeh’s fifty years of impact on the field [48], while Kou and Ergu curated a commemorative edition celebrating Saaty’s 90th birthday, which included an extensive review of pairwise comparison matrices in multi-criteria decision-making [49,50]. Further, Zavadskas et al. offered a comprehensive review of MCDM applications in civil engineering up to 2015 [40,41]. Specific domains within civil engineering have been addressed in numerous papers, including Zavadskas et al.’s 2016 review of hybrid MCDM (HMCDM) techniques in engineering [51], which also traced the historical development of MCDM but with a broad engineering focus rather than specifically on building and construction. Another significant review by Zavadskas et al. [52] explored HMCDM methods applied to sustainability challenges such as technology and product development, personnel selection, organizational management, site selection, and supply chain optimization. Contributions from Yi and Wang [53] detailed a multi-objective mathematical programming model for equitable labor assignment in construction, whereas Pons et al. [54] focused on applying MCDM techniques to sustainability evaluation in architectural and engineering design. Penades-Pla et al. [55] examined sustainable bridge design, and Keshavarz Ghorabaee et al. [56] provided a wide-ranging overview of MCDM applications in supply chain management. Reviews by Si et al. [57] highlighted MCDM in assessing green technologies, and other studies addressed decision-making frameworks for green buildings, sustainable design, and energy-related challenges [58,59]. Additionally, Cerveira et al. [60] analyzed the optimization of wind farm distribution networks. Collectively, these review articles demonstrate the state-of-the-art application of MCDM methods, including both MADM and MODM, to address sustainability concerns in civil engineering, construction, and building technology. The total number and continuous growth of publications utilizing MCDM in these fields are depicted in Figure

Figure 3. Number of publications on the topic “MCDM” in civil engineering and construction building technology Web of Science categories (Web of Science core collection database, 15 October 2017).

 

Comprehensive review articles that examine the evolution of MCDM methodologies have significantly contributed to the field. Extensive surveys on broad MADM frameworks and fuzzy MADM approaches have been provided by Mardani et al. [61,62], Kahraman et al. [63], and Antucheviciene et al. [64]. The TOPSIS method, which ranks alternatives based on their closeness to an ideal solution, was critically analyzed by Zavadskas et al. [65] and Behzadian et al. [66]. The progression of the VIKOR method (an acronym in Serbian for multicriteria optimization and compromise solution) was elaborated by Mardani et al. [67]. Balezentis and Balezentis [68] offered a detailed review of the MULTIMOORA approach, combining ratio analysis with full multiplicative form. Furthermore, Behzadian et al. [69] presented a wide-ranging discussion on the advancements and diverse implementations of the PROMETHEE method. Research on multiobjective inventory routing under uncertain demand through population-based metaheuristics was conducted by Yang et al. [70], while Pan et al. [71] proposed a regional diversity maintenance strategy for many-objective optimization. Marttunen et al. [72] addressed practical challenges related to combining methods in multiple-criteria decision analysis. Foundational texts on MCDM authored by Tzeng [73–75], Kou et al. [76], Bisdorff et al. [77], and Liu [78] have played pivotal roles in guiding researchers in selecting suitable techniques. The following section provides an analysis of MCDM applications in civil engineering and construction, emphasizing trends across publication years, geographic distribution, institutional contributions, and journals, with a focused review of recent studies (2015–2017) discussed in Section 3.

 

2. Research Methodology and Preliminary Results

 

A comprehensive search was conducted on 15 October 2017 using the Web of Science core collection database, focusing on the keyword “MCDM.” The search strategy is illustrated in Figure 4. Out of 3571 documents initially retrieved, 2605 relevant articles were selected, encompassing research papers and review articles while excluding conference proceedings and book chapters. Further filtering for MCDM applications within the civil engineering and construction building technology categories yielded 195 papers, comprising 160 research and review articles. The findings highlighted that MCDM methodologies have been employed by researchers affiliated with over 100 institutions across 91 countries worldwide, spanning more than 100 research disciplines. Specifically, in civil engineering and construction building technology, studies originated from 34 different countries (see Figure 5). Notably, Iran, the United States, and Lithuania emerged as the top contributors, with Vilnius Gediminas Technical University in Lithuania leading with 30 publications, followed by the University of Tehran, Iran, with 17 publications, as summarized in Table 1.

Figure 4. The research procedure and preliminary results.

 

A vast number of publications on MCDM advancements and their practical implementations have appeared across over 100 journals, predominantly within the fields of operations research and computer science. Specifically, MCDM applications related to civil engineering, construction, and building technology have been disseminated through 57 journals, primarily focused on engineering disciplines, as detailed in Table 2. While earlier reviews covered MCDM use in civil engineering up to 2015 [40,41], the present study concentrates on an in-depth evaluation of works published between 2015 and 2017. Key trends observed during this interval, summarized in Table 3, indicate a 56% growth in the overall number of MCDM-related publications. Furthermore, the diversity of author locations expanded from 72 to 91 countries. Regarding civil engineering applications, there was a 41% rise in published papers, with contributing countries increasing from 28 to 34 within the same timeframe. Among prominent contributors, Vilnius Gediminas Technical University stood out, producing 84 publications on MCDM from 2015 to 2017, of which nine specifically addressed civil engineering and construction building technology topics. The Journal of Civil Engineering and Management led the publishing outlets with 24 articles, underscoring its central role in this research domain.

Table 1. Publications by institutions on the topic “MCDM” civil engineering and construction building technology Web of Science categories (Web of Science core collection, 15 October 2017).

 

Institutions	Number of Articles
Vilnius Gediminas Technical University	30
University of Tehran	17
Amirkabir University of Technology	7
University of Naples Federico II	5
University of Arizona	5
Polytechnic University of Catalonia	5
Iran University Science Technology	5
Hong Kong Polytechnic University	5
University of British Columbia	4
Seoul National University of Science Technology	4
Istanbul Teknik University	4
Indian Institute of Technology IIT	4
Texas A&M University System	3
Royal Institute of Technology	3
Kaunas University of Technology	3
Islamic Azad University	3
Hohai University	3

 

Note: The names of institutions listed correspond exactly to those recorded in the Web of Science core collection database. This table excludes any institutions that have contributed fewer than three publications on the subject. Specifically, institutions with two articles include Yonsei University, Yildiz Technical University, University of North Carolina, University of Nebraska System, University of Nebraska Lincoln, University of Illinois System, University of California System, Universiti Teknologi Malaysia, Universiti Malaya, Univ Mohaghegh Ardabili, Tsinghua University, Tennessee Technological University, Telecom Italia, Poznan University of Technology, Pontificia Universidad Catolica De Chile, Parthenope University Naples, Pacific Century Premium Development Ltd., National Central University, Nan Kai University of Technology, Munzur University, Indian Institute of Technology IIT Roorkee, Engref, Engineering Research Institute for Natural Disaster Shakhes Pajouh, and Birla Institute of Technology Science. Additionally, institutions with a single publication on this topic include Lublin University of Technology, Laval University, Lasbela University of Agriculture, Kunsan National University, Korea University, Korea Environment Institute (KEI), Korea Advanced Institute of Science and Technology (KAIST), Kocaeli University, Klaipeda University, Karlsruhe Institute of Technology, Izmir Katip Celebi University, Istanbul Bilgi University, Isfahan University of Technology, Higher Institute of Applied Biological Sciences of Tunis, Institute of Land Reclamation and Grassland Farming, Indian Institute of Technology IIT Kharagpur, Indian Institute of Technology IIT Kanpur, Indian Institute of Science IISc Bangalore, Indian Institute of Remote Sensing, Imperial College London, Imam Khomeini International University, IETT, Hyundai Institute of Construction Technology, Hyundai Engineering and Construction Co., Ltd., Huafan University, Hong Kong University of Science and Technology, Heriot-Watt University, Hellenic Institute of Transport, Harp Akademileri Komutanligi, George Mason University, Gaziosmanpasa University, Firat University, Feng Chia University, Federal University of Petroleum Resources, Fateh Research Group, Eskisehir Osmangazi University, El Paso Metropolitan Planning Organization, Ecole Nationale du Genie Rural, East Carolina University, Dogus University, Dalian University of Technology, Council of Scientific and Industrial Research (CSIR) India, Concordia University Canada, Chongqing Jiaotong University, Chia Nan University of Pharmacy and Science, Centre National de la Recherche Scientifique (CNRS), Canik Basari University, California Department of Transportation, Cairo University, Bursa Technical University, Bureau of Geological and Mining Research, Brandon University, Bialystok Technical University, Beijing University of Technology, Beijing Normal University, Asian Institute of Technology, Aristotle University of Thessaloniki, Akdeniz University, and Academy of Scientific Innovation and Research (ACSIR).

 

Table 2. Publications by journal on the topic “MCDM” in civil engineering and construction building technology Web of Science categories (Web of Science core collection, 15 October 2017).

 

Title of Journal	Number of Articles
Journal of Civil Engineering and Management	24
Water Resources Management	23
Archives of Civil and Mechanical Engineering	7
Stochastic Environmental Research and Risk Assessment	6
Journal of Hydroinformatics	6
Energy and Buildings	6
Water Resources Bulletin	5
Journal of Construction Engineering and Management	5
Tunnelling and Underground Space Technology	4
Ocean Engineering	4
Journal of Advanced Transportation	4
Automation in Construction	4
Transportation	3
Sustainable Cities and Society	3
Structure and Infrastructure Engineering	3
Building and Environment	3

 

Note: The table intentionally omits journals that featured fewer than three publications on the selected research theme. Specifically, journals with two publications include Transportation Research Record, Transportation Research Part E: Logistics and Transportation Review, Journal of Water Resources Planning and Management, Journal of Performance of Constructed Facilities, Journal of Irrigation and Drainage Engineering, Journal of Hydrology, Journal of Computing in Civil Engineering, Computer-Aided Civil and Infrastructure Engineering, Civil Engineering and Environmental Systems, and Baltic Journal of Road and Bridge Engineering. Meanwhile, the list of journals that published only one article includes Water International, Thin-Walled Structures, Stochastic Hydrology and Hydraulics, Smart Structures and Systems, Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, Preservation of Roadway Structures and Pavements, Latin American Journal of Solids and Structures, KSCE Journal of Civil Engineering, Journal of Water Supply: Research and Technology – Aqua, Journal of Water Resources Planning and Management ASCE, Journal of Urban Planning and Development ASCE, Journal of Urban Planning and Development, Journal of Transportation Engineering ASCE, Journal of Structural Engineering ASCE, Journal of Management in Engineering, Journal of Information Technology in Construction, Journal of Hydrologic Engineering, Journal of Earthquake Engineering, Journal of Construction Engineering and Management ASCE, Journal of Building Engineering, Journal of Aerospace Engineering, Iranian Journal of Science and Technology – Transactions of Civil Engineering, International Journal of Geomate, International Journal of Concrete Structures and Materials, International Journal of Civil Engineering, Gradevinar, European Journal of Environmental and Civil Engineering, Earthquakes and Structures, Construction and Building Materials, Computers and Structures, and Advances in Structural Engineering.

Figure 5. Publications by country on the topic of “MCDM” in civil engineering and construction building technology Web of Science categories (Web of Science core collection database, 15 October 2017)

 

 

Table 3. Changes in number of publications on the topic of “MCDM” in Web of Science core collection database.

Publications                            Number of Publications

	1991–2014 1991–2017 (15 October) Publications on MCDM methods     All 2290 3571 Articles: 1589 2605 •    Countries 72 91 •    Institutions >100 >100 •    Journals >100 >100 Publications on MCDM in Civil Engineering and Construction Building Technology All 138 195 Articles: 113 160 •    Countries 28 34 •    Institutions >100 >100 •    Journals 38 57

 

Articles on the topic of “MCDM” in the Web of Science categories of civil engineering and construction building technology, published 1 January 2015–15 October 2017, are further analyzed in detail in Section 3.

Detailed Analysis of Articles Published in the Period of 2015–2017

An extensive evaluation was conducted on scholarly articles published between 2015 and 2017, focusing on the fields of civil engineering and construction building technologies. The selected publications were categorized into seven thematic domains (as illustrated in Figure 6), each representing a distinct phase within the sustainable building lifecycle. The emphasis was placed on analyzing recent contributions during the specified timeframe. Initially, 61 journal papers were identified based on relevant subject areas. However, after further examination, documents listed under the Web of Science categories such as “Water Resources,” “Environmental Sciences,” “Mechanical Engineering,” “Marine Engineering,” “Aerospace Engineering,” and “Industrial Engineering” were excluded due to their limited alignment with the core objectives of this study. Ultimately, 36 research and review articles were retained and assessed based on their domain relevance, the specific sustainability challenges addressed, and the multi-criteria decision-making (MCDM) methods employed.Findings presented in Table 4 indicate that “sustainable construction” and “construction technology” emerged as the leading application domains, accounting for 28% and 22% of the selected publications, respectively. Following closely were “building structures and systems,” “construction management,” and “retrofitting,” each comprising approximately 11% of the articles reviewed. “Building maintenance” and “location selection” appeared less frequently, each contributing around 8% to the overall distribution.Various sustainability-focused challenges within the construction industry were approached using a diverse set of MCDM techniques. For instance, Naubi et al. [79] introduced the Watershed Sustainability Index (WSI) to pinpoint critical problem zones within watersheds. De la Fuente et al. [80] proposed a framework to assess the environmental sustainability of different concrete and steel reinforcement combinations for tunnel linings. Arroyo et al. [81,82] explored several decision-making methodologies suitable for sustainability-driven architectural and construction design choices. Ignatius et al. [83] designed an innovative model grounded in fuzzy logic to evaluate green buildings according to stakeholder preferences. Hosseini et al. [84] developed an approach for evaluating temporary housing solutions after disasters. Chen and Pan [85] merged BIM with MCDM to create a fuzzy decision-making model for identifying low-carbon building strategies. Jalaei et al. [86] proposed a BIM-based decision framework to aid sustainable design component selection at early project stages. Medineckiene et al. [87] developed a novel MCDM technique for choosing relevant sustainability assessment indicators, while Nakhaei et al. [88] introduced a vulnerability analysis model for buildings exposed to explosive hazards.

Figure 6. Application domains in research areas of civil engineering and construction building technology.

 

The application of MCDM techniques was particularly prominent in addressing issues within the field of construction technology. For instance, Yousefi et al. [89] applied this methodology to determine the optimal choice among various Combined Cooling, Heating, and Power (CCHP) systems. Kalibatas and Kovaitis [90] utilized MCDM tools to evaluate waterproofing systems for multifunctional inverted flat roofs. Turskis et al. [91] developed a decision-support model to assess and select suitable building foundation options, while Leonavičiūtė et al. [92] examined personal safety equipment to mitigate fall risks using an advanced MCDM approach. Turskis and Juodagalvienė [93] formulated a framework for selecting residential stair configurations. Ebrahimian et al. [94] proposed a structured hybrid MCDM model to identify optimal construction methods for urban stormwater systems. Nezarat et al. [95] employed MCDM to assess tunneling project risks. Shariati et al. [96] introduced a model to evaluate key nanotechnology implementation factors. Additional MCDM applications included material reuse in concrete [97], structural fire safety [99], decommissioning techniques [98], and fan selection for coal mining [100]. Lesser usage was noted in construction management [101–104], retrofitting [57,105–107], maintenance [108–110], and site selection [111–113].

 

Table 4. Multiple-criteria decision-making (MCDM) applications in the domains of civil engineering and construction building technology research areas.

 

Application Domain	Problem Solved	MCDM Method(s) Applied	Publication
Sustainable Construction (27.78%)	Watershed sustainability	PROMETHEE	Naubi et al. (2017) [79]
	Sustainability-based approach to determine the concrete type and reinforcement configuration	AHP, MIVES	de la Fuente et al. (2017) [80]
	Sustainable building design	CBA, WRC	Arroyo et al. (2016) [81]
	Choosing problem in building detailed design	AHP, CBA	Arroyo et al. (2015) [82]
	Green building assessment approach	Fuzzy ANP	Ignatius et al. (2016) [83]
	Assessing sustainability of post-disaster temporary housing	MIVES	Hosseini et al. (2016a) [84]
	BIM-aided fuzzy model for selecting Low-Carbon Building (LCB) measures	Fuzzy PROMETHEE	Chen & Pan (2016) [85]
	Selection of sustainable building components	TOPSIS	Jalaei et al. (2015) [86]
	Sustainable building assessment/certification	AHP, ARAS	Medineckiene et al. (2015) [87]
	Vulnerability assessment of office buildings to blast	SMART, SWARA	Nakhaei et al. (2016) [88]
Construction Technology (22.22%)	Hybrid CCHP system integration into commercial buildings	AHP	Yousefi et al. (2017) [89]
	Selecting waterproofing membranes for inverted flat roofs	SAW, Hurwicz, Laplace, Bayes rules	Kalibatas & Kovaitis (2017) [90]
	Evaluation of building foundation alternatives	AHP, WASPAS-G	Turskis et al. (2016) [91]
	Analysis and prevention of construction site accidents	WASPAS-G	Leonavičiūtė et al. (2016) [92]
	Decision model to assess stairs for dwelling houses	AHP, SAW, MEW, TOPSIS, EDAS, ARAS	Turskis & Juodagalvienė (2016) [93]
	Selecting urban storm water construction method	Fuzzy AHP, CP	Ebrahimian et al. (2015) [94]
	Ranking of geological risks in mechanized tunneling	Fuzzy AHP	Nezarat et al. (2015) [95]
	Critical factors for applying nanotechnology in construction	IFS, ANP	Shariati et al. (2017) [96]
Construction Management (11.11%)	Selecting best bidder during tendering	WRC, BVS, CBA	Schöttle & Arroyo (2017) [101]
	Dispute resolution methods for construction projects	Laplace, Hurwicz, Hodges-Lehmann rules	Khanzadi et al. (2017) [102]
	Evaluation of private sectors for partnership projects	SWOT, Fuzzy VIKOR, PROMETHEE	Dadpour & Shakeri (2017) [103]
	Supply vendor selection for LNG megaprojects	Fuzzy TOPSIS	Jang et al. (2016) [104]
Retrofitting (11.11%)	Optimal seismic upgrade of school building using metal-based devices	TOPSIS	Formisano et al. (2017) [105]
	Assessment of building-integrated green technologies	AHP	Si et al. (2016) [57]
	Seismic retrofitting and vertical addition of buildings	TOPSIS, ELECTRE, VIKOR	Formisano & Mazzolani (2015) [106]
	Energetic retrofitting analysis of masonry buildings	TOPSIS	Terracciano et al. (2015) [107]
Building Maintenance (8.33%)	Maintenance-inspection policy tool	Multicriteria delay-time model	Cavalcante et al. (2016) [108]
	Condition assessment of telecom towers	Fuzzy TOPSIS	Verma et al. (2015) [109]
	Procurement strategy selection in maintenance work	AHP	Lin et al. (2015) [110]
Location Selection (8.33%)	Distribution center location selection	ELECTRE I	Agrebi (2017) [111]
	Garage location for residential house	AHP, WASPAS-SVNS	Baušys & Juodagalvienė (2017) [112]
	Site location selection for temporary housing	AHP, MIVES	Hosseini et al. (2016b) [113]

 

PROMETHEE (Preference Ranking Organization METHod for Enrichment Evaluations), AHP (Analytic Hierarchy Process), MIVES (Integrated Value Model for Sustainable Assessment), CBA (Choosing By Advantages), WRC (Weighting, Rating, and Calculating), ANP (Analytic Network Process), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), ARAS (Additive Ratio Assessment), SMART (Simple Multi Attribute Ranking Technique), SAW (Simple Additive Weighting), WASPAS-G (Weighted Aggregated Sum Product Assessment with grey numbers), MEW (Multiplicative Exponential Weighting), EDAS (Evaluation Based on Distance from Average Solution), CP (Compromise Programming), IFS (Intuitionistic Fuzzy Set), BVS (Best Value Selection), VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje—Multicriteria Optimization and Compromise Solution), ELECTRE (ELimination Et Choix Traduisant la REalité—Elimination and Choice Expressing Reality), WASPAS-SVNS (WASPAS with Single-Valued Neutrosophic Set), and FM (Full Multiplicative Utility Function) are among the most widely recognized tools in the realm of multi-criteria decision-making (MCDM). Researchers and industry professionals constantly deliberate which of these methodologies is best suited for particular decision problems, a trend that continues in contemporary scholarly discourse. Schöttle and Arroyo [101], for instance, examined and contrasted the effectiveness of WRC, BVS, and CBA in determining tender outcomes, demonstrating the nuanced differences in their evaluative impacts. Meanwhile, Khanzadi et al. [102] designed a hybrid MCDM model combining discrete matrix games with grey numbers—incorporating strategies from game theory, including Laplace, Hurwicz, Bayes, and Hodges-Lehmann—to resolve construction-related disputes. By integrating multiple decision rules, they managed to counteract individual methodological limitations while reinforcing the overall robustness of the model.

In another example, Dadpour and Shakeri [103] presented a hybrid analytical approach that unified SWOT analysis with the fuzzy VIKOR method for appraising and selecting private entities for public–private partnership (PPP) projects. They contrasted the results of their framework with those yielded by PROMETHEE to validate its effectiveness. Additionally, several case studies within the reviewed literature relied on singular decision-making methodologies. Si et al. [57] utilized the AHP to calculate performance metrics for retrofit technologies aimed at reducing energy usage and carbon footprints in existing infrastructure. Lin et al. [110] also employed AHP for evaluating procurement strategy options. In a different context, Formisano et al. [105] applied the TOPSIS method to assess retrofitting techniques using criteria such as cost-effectiveness, structural integrity, and environmental impact, concluding that TOPSIS provides reliable support in decision-making for retrofit solutions. Formisano and Mazzolani [106] had previously integrated TOPSIS, ELECTRE, and VIKOR to assess various retrofitting strategies, reinforcing the value of using multiple MCDM methods for comparative analysis. Similarly, Terracciano et al. [107] adopted TOPSIS to assess vertical expansion strategies for existing masonry structures, affirming through sensitivity testing that their conclusions were not influenced by subjective judgments. Agrebi [111] introduced a novel application of ELECTRE I for optimal location selection, emphasizing its practical utility in spatial planning. The integration of MCDM methodologies within broader decision frameworks is increasingly commonplace. Cavalcante et al. [108] implemented a delay-time-based multi-criteria model to quantitatively support maintenance planning in building operations. Verma et al. [109] enhanced the fuzzy TOPSIS method to better accommodate the uncertainty inherent in visual inspections by focusing ranking decisions on fuzzy positive ideal solutions. In a similar effort, Baušys and Juodagalviene˙ [112] utilized AHP alongside an extended WASPAS approach—specifically WASPAS-SVNS—when determining suitable locations for garage installations, leveraging the advanced capabilities of single-valued neutrosophic sets. Hosseini et al. [113] developed a sustainability evaluation framework integrating AHP with MIVES, incorporating a streamlined life cycle assessment (LCA) to enable comprehensive technology assessments. As summarized in Table 5, the application of MCDM techniques in civil engineering and construction technology has been extensive. The AHP [114], fuzzy logic approaches [47], and TOPSIS [115] emerged as the most frequently utilized methodologies over the observed period. AHP, which dates back to 1980 [114], was the most prevalent, primarily for criteria weighting and decision support—either on its own or combined with other models [58,80,82,87,89,91,93–95,98–100,110,113]. Fuzzy methods ranked second in popularity, serving both as standalone evaluative tools and in conjunction with models such as ANP [83,96], PROMETHEE [85], AHP [94,95], VIKOR [103], and TOPSIS [104,109]. The latter was applied extensively on its own [86,105,107] and in hybrid configurations [93,104,106,109], highlighting its adaptability to diverse engineering challenges.

Table 5. Methods applied in articles on civil engineering and construction building technology.

 

Method	Authors & Year	Number of Articles
AHP	Saaty, 1980 [114]	15
Fuzzy Sets	Zadeh, 1965 [47]	12
TOPSIS	Hwang & Yoon, 1981 [115]	7
MIVES	San-José & Cuadrado, 2010 [116]	3
WASPAS-G	Zavadskas et al., 2015b [117]	2
PROMETHEE	Mareschal & Brans, 1992 [118]	2
ARAS	Zavadskas & Turskis, 2010 [119]	2
VIKOR	Opricovic, 1998 [120]	2
SAW	MacCrimon, 1968 [121]	2
Laplace Rule	Laplace, 1814 [122]	2
Hurwicz Rule	Hurwicz, 1951 [123]	2
Bayes Rule	Bayes, 1763 [124]	2
WASPAS	Zavadskas et al., 2012 [125]	2
ELECTRE	Roy, 1968 [126]	1
ANP	Saaty, 1996 [127]	1
SWARA	Kersuliene et al., 2010 [128]	1
WSM	MacCrimon, 1968 [121]	1
EDAS	Keshavarz Ghorabaee et al., 2015 [129]	1
MEW	Yoon & Hwang, 1995 [130]	1
Hodges-Lehmann Rule	Hodges & Lehmann, 1952 [131]	1
FM (Full Multiplicative Utility Function)	Bridgman, 1922 [132]	1

 

Discussion

The escalating need for sustainable urban development presents one of the foremost challenges facing modern society. Achieving this objective hinges on the harmonized integration of ecological integrity, economic viability, and social equity. Sustainability goals involve addressing multifaceted concerns such as environmental preservation, energy conservation, intelligent transportation systems, digital urban innovations, and other interrelated issues throughout all stages of a building’s life span. These concerns encompass various stakeholders operating at different decision-making levels, each with their own interests. From a mathematical standpoint, such sustainability challenges fall under the domain of multi-criteria group decision-making (MCGDM). This is largely due to the inherently multidimensional nature of sustainability-driven choices, which arise from the broader philosophy of sustainable development.

One of the core strengths of multi-criteria decision-making (MCDM) methodologies is their inherent capability to manage situations involving opposing and often competing objectives. For this reason, the research concentrated on investigating the MCDM frameworks and techniques that are currently being applied to sustainability-focused decisions, especially within the construction industry. In this sector, choosing the most suitable solution often proves to be a highly complex task. Traditional structural engineering methods typically fall short when it comes to evaluating the sustainability of various alternatives. As has been observed in the literature, researchers frequently employ MCDM tools to validate multiple design options and computational results [91,105–107]. In many instances, sensitivity analysis is employed alongside these tools to ensure that the outputs are not unduly affected by subjective judgments of evaluators [83,86,99,101,105–107,109,111].

An extensive review highlighted that three MCDM methods—AHP, fuzzy set theory, and TOPSIS—stand out for their prominence in both long-term and recent academic publications. These methods consistently appear across numerous studies, signaling their established credibility and widespread adoption. A similar pattern of increased adoption of AHP and TOPSIS was documented by Zyoud and Fuchs-Hanusch [133]. The widespread application of the TOPSIS approach, in particular, can be attributed to several frequently cited advantages [66,86,104–107,109]. First, its conceptual model is both logical and intuitive. Second, the computational process is streamlined and easy to implement. This technique is widely acknowledged in the literature as one of the most user-friendly MCDM approaches due to its straightforward methodology [105,106], as well as its demonstrated consistency and dependability [107]. In situations that require evaluation of numerous criteria and alternatives, TOPSIS tends to outperform other methods such as ELECTRE in terms of speed and computational efficiency [86]. This method constructs two idealized reference points—positive and negative alternatives—which assist decision-makers in identifying the most suitable option by calculating its geometric proximity to the ideal solutions.

In general, MCDM tools facilitate objective decision-making by minimizing the influence of individual evaluators’ biases. Real-life problems are often plagued by vague or imprecise data, stemming from the natural uncertainty in human reasoning. To address this issue, extensions of classical decision-making techniques—such as fuzzy TOPSIS—have been introduced. These allow decision-makers to navigate complex and uncertain environments more effectively. One key advantage of fuzzy TOPSIS, as highlighted by Zavadskas et al. [66], is its capacity to accommodate a range of data types, including crisp, interval-based, fuzzy, and linguistic variables. Building upon Zadeh’s foundational work on fuzzy sets published in 1965 [47], fuzzy logic has since evolved into a robust analytical tool with diverse applications [48,134]. Integrating fuzzy theory into classical MCDM methods enhances their ability to handle ambiguity and subjectivity, ultimately improving the reliability and depth of decision-making outcomes.

Among the reviewed literature, many studies utilized the AHP technique to derive the relative importance, or weights, of decision criteria. AHP, introduced by Saaty in 1980 [114], is particularly known for its hierarchical structuring of complex problems. As noted by Si et al. [57], the configuration of this hierarchy significantly influences how weights are distributed among criteria. The addition or reordering of criteria can lead to shifts in their interrelationships, thereby altering the final prioritization. This variability in weight assignment can, in turn, impact the ultimate ranking of alternatives. Consequently, it becomes essential to achieve consensus on how the criteria hierarchy is structured. Conducting sensitivity analysis is also highly advisable to identify optimal weight ranges that align with the preferences of decision-makers. Despite certain limitations, AHP remains one of the most frequently employed methods in technology and economic planning due to its simplicity and effectiveness [49]. Its dominant role in academic research is further corroborated by the extensive number of studies that rely on it [133].

Another significant finding of this review is the growing tendency among researchers to employ hybrid MCDM approaches rather than relying on a single method. Reference [106] supports this trend, stating that combining multiple MCDM techniques enhances the likelihood of reaching an optimal, objective decision that accommodates multiple viewpoints. This inclination toward hybridization is also driven by the expanding need to integrate MCDM frameworks with other analytical or computational tools [62,72]. MCDM techniques are increasingly being adopted across different branches of civil engineering and the broader field of construction and building technologies. This growing usage reflects a heightened awareness among researchers about the importance of accounting for diverse and interlinked dimensions when pursuing sustainable solutions in construction. The implications of this shift extend beyond academia; it holds considerable relevance for policy-making. It underlines the fact that sustainability is an inherently intricate objective, demanding policymakers to look past mere economic evaluations. It stresses the need to adopt strategies that achieve balanced outcomes, considering costs and benefits for a wide array of stakeholders. This paper has meticulously examined research works indexed in the Web of Science Core Collection database. Although some pertinent studies may not have been captured due to the scope limitation, the authors maintain that the selected sample is a valid representation of the scholarly landscape. The Web of Science database is widely recognized for its rigorous inclusion criteria and its reputation for offering a comprehensive and unbiased view of high-quality research. The deliberate narrowing of the study’s scope was a conscious editorial decision aimed at maintaining a manageable article length. Nevertheless, this limitation leaves room for future scholars to broaden the investigation. Subsequent studies may delve deeper into the subject, incorporate a larger sample size, and explore other works not covered in this article.

Conclusions

The implementation of sustainable strategies in civil engineering, construction, and building technologies relies heavily on advanced scientific research, with multi-criteria decision-making (MCDM) frameworks offering significant support in this process. This study validates the necessity and practical benefits of adopting MCDM methodologies for promoting sustainability-focused decisions. A notable rise in scholarly attention toward “sustainability” topics became apparent from 2010 onward, which coincided with an upward trajectory in publications addressing “MCDM.” A similar publication pattern was also observed specifically within the Web of Science categories dedicated to civil engineering and construction-related technologies. A primary contribution of this research lies in its comprehensive evaluation of academic literature published within the past three years. Findings reveal a marked increase of over 40% in scholarly works exploring the role of MCDM in solving challenges in civil engineering and construction technology. Additionally, within this timeframe, the geographic distribution of contributing researchers broadened from 28 to 34 nations, and the number of publishing journals grew from 38 to 57, with authors affiliated with more than 100 institutions. The most prevalent thematic applications emerged within the domains of sustainable construction and building technology, which together accounted for half of all reviewed studies. Other significant areas of interest included structural systems, project management, and infrastructure renovation, each making up roughly 11% of the total dataset. Among the various MCDM tools employed during the reviewed period, the Analytic Hierarchy Process (AHP), fuzzy logic-based approaches, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) were most frequently applied. The outcomes of this review affirm that MCDM frameworks are vital instruments for enabling sound, sustainability-driven choices in the construction sector—especially for evaluating design scenarios, construction methodologies, and environmental resilience. A constraint of the present analysis is that it only includes research within two specific Web of Science domains: civil engineering and construction technology, although MCDM techniques have been applied extensively across more than 100 indexed categories, several of which are interconnected with civil engineering topics. Nonetheless, the methodology employed is adaptable and can be expanded in future studies to encompass a broader range of scientific fields. The primary objective of this article was to introduce key themes and synthesize the most current developments in sustainable construction research. In doing so, it provides a valuable resource for understanding recent advancements and serves as a foundation for future scholarly exploration. The review demonstrates that recent years have witnessed substantial progress in the deployment of decision-making techniques and that their integration has produced measurable benefits. Utilizing MCDM tools empowers decision-makers to evaluate complex trade-offs between competing options with greater precision. Moreover, the ongoing trend toward combining multiple decision-support methods underscores the research community’s pursuit of more effective, problem-specific solutions. As a forward-looking endeavor, the authors plan to conduct comparative assessments of various existing MCDM methodologies, weighing their advantages and limitations to better inform future applications. Consequently, this study offers a solid starting point for academics aiming to expand investigations into sustainable development and engineering decision processes.

Acknowledgments: The authors express their gratitude to the reviewers for their comments and valuable suggestions.

Author Contributions: All authors contributed equally to this work.

Conflicts of Interest: The authors declare no conflict of interest.

References

1. Siddique, ; Adeli, H. Nature-Inspired Chemical Reaction Optimisation Algorithms. Cogn. Comput. 2017, 9, 411–422.
2. Siddique, ; Adeli, H. Physics-based search and optimization: Inspirations from nature. Expert Syst. 2016,

33, 607–623. [CrossRef]

3. Siddique, ; Adeli, H. Brief History of Natural Sciences for Natural-Inspired Computing in Engineering.
4. Civ. Eng. Manag. 2016, 22, 287–301. [CrossRef]
5. Siddique, ; Adeli, H. Applications of Gravitational Search Algorithm in Engineering. J. Civ. Eng. Manag.

2016a, 22, 981–990. [CrossRef]

5. Siddique, ; Adeli, H. Simulated annealing, its variants and engineering applications. Int. J. Artif. Intell. Tools

2016, 25, 1630001. [CrossRef]

6. Siddique, ; Adeli, H. Central force metaheuristic optimisation. Sci. Iran. 2015, 22, 1941–1953.
7. Amezquita-Sanchez, J.P.; Adeli, H. Feature extraction and classification techniques for health monitoring of Sci. Iran. 2015, 22, 1931–1940.
8. Qarib, ; Adeli, H. Recent advances in health monitoring of civil structures. Sci. Iran. 2014, 21, 1733–1742.
9. Soto, M.G.; Adeli, H. Placement of control devices for passive, semi-active, and active vibration control of structures. Iran. 2013, 20, 1567–1578.
10. El-Khoury, ; Adeli, H. Recent Advances on Vibration Control of Structures under Dynamic Loading.

Arch. Comput. Methods Eng. 2013, 20, 353–360. [CrossRef]

11. Yeganeh-Fallah, A.; Taghikhany, T. A Modified Sliding Mode Fault Tolerant Control for Large Scale Civil Comput. Aided Civ. Infrastruct. Eng. 2016, 31, 550–561. [CrossRef]
12. Ghaedi, ; Ibrahim, Z.; Adeli, H.; Javanmardi, A. Invited Review: Recent developments in vibration control of building and bridge structures. J. Vibroeng. 2017, 19, 3564–3580.
13. Amezquita-Sanchez, J.P.; Adeli, H. Signal processing techniques for vibration-based health monitoring of smart Arch. Comput. Methods Eng. 2016, 23, 1–15. [CrossRef]
14. Aldwaik, ; Adeli, H. Advances in optimization of highrise building structures. Struct. Multidiscip. Optim.

2014, 50, 899–919. [CrossRef]

15. Soto, G.; Adeli, H. Tuned Mass Dampers. Arch. Comput. Methods Eng. 2013, 20, 419–431. [CrossRef]
16. Bakule, L.; Rehák, B.; Papík, M. Decentralized Networked Control of Building Structures. Aided Civ. Infrastruct. Eng. 2016, 31, 871–886. [CrossRef]
17. Karami, K.; Akbarabadi, S. Developing a smart structure using integrated subspace-based damage detection and semi-active Comput. Aided Civ. Infrastruct. Eng. 2016, 31, 887–902. [CrossRef]
18. Chou, J.S.; Pham, A.D. Smart Artificial Firefly Colony-based Support Vector Regression for Enhanced Forecasting in Civil Comput. Aided Civ. Infrastruct. Eng. 2015, 30, 715–732. [CrossRef]
19. Amezquita-Sanchez, J.P.; Valtierra-Rodriguez, M.; Aldwaik, M.; Adeli, H. Neurocomputing in civil Sci. Iran. 2016, 23, 2417–2428. [CrossRef]
20. Vaha, P.; Heikkila, T.; Kilpelainen, P.; Jarviluoma, M.; Gambao, E. Extending Automation of Building Construction—Survey on Potential Sensor Technologies and Robotic Applications. Constr. 2013, 36, 168–178. [CrossRef]
21. Streimikiene, ; Balezentis, T.; Balezentiene, L. Comparative assessment of road transport technologies.

Renew. Sustain. Energy Rev. 2013, 20, 611–618. [CrossRef]

22. Pongiglione, ; Calderini, C. Sustainable Structural Design: Comprehensive Literature Review. J. Struct. Eng.

2016, 142, 04016139. [CrossRef]

23. Dai, A wavelet support vector machine-based neural network meta model for structural reliability assessment. Comput. Aided Civ. Infrastruct. Eng. 2017, 32, 344–357. [CrossRef]
24. Asadi, ; Adeli, H. Diagrid: An innovative, sustainable, and efficient structural system. Struct. Des. Tall

Spec. Build. 2017, 26, e1358. [CrossRef]

25. Wang, M.; Adeli, H. Sustainable Building Design. J. Civ. Eng. Manag. 2014, 20, 1–10. [CrossRef]
26. Rafiei, H.; Adeli, H. Sustainability in highrise building design and construction. Struct. Des. Tall Spec. Build.

2016, 25, 643–658. [CrossRef]

27. Mikaelsson, L.A.; Larsson, Integrated Planning for Sustainable Building—Production an Evolution Over Three Decades. J. Civ. Eng. Manag. 2017, 23, 319–326. [CrossRef]
28. Oh, B.K.; Kim, K.J.; Kim, Y.; Park, H.S.; Adeli, H. Evolutionary learning based sustainable strain sensing model for structural health monitoring of high-rise Appl. Soft Comput. 2017, 58, 576–585. [CrossRef]
29. Soto, G.; Adeli, H. Multi-agent replicator controller for sustainable vibration control of smart structures.
30. Vibroeng. 2017, 19, 4300–4322. [CrossRef]
31. Akbari, H.; Cartalis, C.; Kolokotsa, D.; Muscio, A.; Pisello, A.L.; Rossi, F.; Santamouris, M.; Synnefa, A.; Wong, N.H.; Zinzi, M. Local Climate Change and Urban Heat Island Mitigation Techniques—The State of the Art. Civ. Eng. Manag. 2016, 22, 1–16. [CrossRef]
32. Ceravolo, R.; Miraglia, G.; Surace, C.; Zanotti-Fragonara, L. A computational methodology for assessing the time-dependent structural performance of electric road infrastructures. Aided Civ. Infrastruct. Eng. 2016, 31, 701–716. [CrossRef]
33. Katsigarakis, K.; Kontes, G.D.; Giannakis, G.I.; Rovas, D.V. Sense-think-act Methodology for Intelligent Building Energy Comput. Aided Civ. Infrastruct. Eng. 2016, 31, 50–64. [CrossRef]
34. Wang, Y.; Szeto, W.Y. Multiobjective environmentally sustainable road network design using Pareto Comput. Aided Civ. Infrastruct. Eng. 2017, 32, 964–987. [CrossRef]
35. Wang, Z.; Wang, Q.; Zukerman, M.; Guo, J.; Wang, Y.; Wang, G.; Yang, J.; Moran, B. Multiobjective Path Optimization for Critical Infrastructure Links with Consideration to Seismic Comput. Aided Civ. Infrastruct. Eng. 2017, 32, 836–855. [CrossRef]
36. Bozza, A.; Napolitano, R.; Asprone, D.; Parisi, F.; Manfredi, G. Alternative resilience indices for city ecosystems subjected to natural hazards. Aided Civ. Infrastruct. Eng. 2017, 32, 527–545. [CrossRef]
37. Cahill, ; Jaksic, V.; John Keane, J.; O’Sullivan, A.; Mathewson, A.; Ali, S.F.; Pakrashi, V. Effect of Road Surface, Vehicle and Device Characteristics on Energy Harvesting from Bridge-Vehicle Interactions. Comput. Aided Civ. Infrastruct. Eng. 2016, 31, 921–935. [CrossRef]
38. Cinelli, M.; Coles, S.R.; Kirwan, K. Analysis of the potentials of multi criteria decision analysis methods to conduct sustainability Ecol. Indic. 2014, 46, 138–148. [CrossRef]
39. Kabir, G.; Sadiq, R.; Tesfamariam, S. A review of multi-criteria decision-making methods for infrastructure Struct. Infrastruct. Eng. 2014, 10, 1176–1210. [CrossRef]
40. Jato-Espino, ; Castillo-Lopez, E.; Rodriguez-Hernandez, J.; Canteras-Jordana, J.C. A review of application of multi-criteria decision making methods in construction. Autom. Constr. 2014, 45, 151–162. [CrossRef]
41. Zavadskas, E.K.; Antuchevicˇiene˙, J.; Kaplin´ski, Multi-criteria decision making in civil engineering: Part I—A state-of-the-art survey. Eng. Struct. Technol. 2015, 7, 103–113. [CrossRef]
42. Zavadskas, E.K.; Antuchevicˇiene˙, J.; Kaplin´ski, Multi-criteria decision making in civil engineering. Part II—Applications. Eng. Struct. Technol. 2015, 7, 151–167. [CrossRef]
43. Franklin, Letter to Joseph Priesley, 1772; Reprinted in the Benjamin Franklin Sampler; Fawcett: New York,

NY, USA, 1956.

43. Pareto, Cours E-Economic; Universite de Lausanne: Lausanne, Switzerland, 1896/1897.
44. Simon, A. A behaviour model of rational choice. Q. J. Econom. 1955, 69, 99–118. [CrossRef]
45. Saaty, T.L. Decision Making for Leaders: the Analytical Hierarchy Process for Decisions in a Complex World; Lifetime Learning Publications: Belmont, CA, USA, 1982.
46. Zeleny, Multiple Criteria Decision Making; McGraw-Hill: New York, NY, USA, 1982.
47. Zadeh, A. Fuzzy sets. Inf. Control 1965, 8, 338–353. [CrossRef]
48. Herrera-Viedma, E. Fuzzy Sets and Fuzzy Logic in Multi-Criteria Decision Making. The 50th Anniversary of Lotfi Zadeh’s Theory: Introduction. Technol. Econ. Dev. Econ. 2015, 21, 677–683. [CrossRef]
49. Kou, ; Ergu, D. AHP/ANP Theory and Its Application in Technological and Economic Development: The 90th Anniversary of Thomas L. Saaty. Technol. Econ. Dev. Econ. 2016, 22, 649–650. [CrossRef]
50. Kou, ; Ergu, D.; Lin, C.S.; Chen, Y. Pairwise Comparison Matrix in Multiple Criteria Decision Making.

Technol. Econ. Dev. Econ. 2016, 22, 738–765. [CrossRef]

51. Zavadskas, E.K.; Antucheviciene, J.; Turskis, Z.; Adeli, H. Hybrid multiple-criteria decision-making methods: A review of applications in engineering. Iran. 2016, 23, 1–20.
52. Zavadskas, E.K.; Govindan, K.; Antucheviciene, J.; Turskis, Z. Hybrid multiple criteria decision-making methods: A review of applications for sustainability issues. Res. Ekon. Istraz. 2016, 29, 857–887. [CrossRef]
53. Yi, W.; Wang, S. Multi-objective mathematical programming approach to construction laborer assignment with equity consideration. Aided Civ. Infrastruct. Eng. 2016, 31, 954–965. [CrossRef]

 

54. Pons, O.; de la Fuente, A.; Aguado, A. The Use of MIVES as a Sustainability Assessment MCDM Method for Architecture and Civil Engineering Applications. Sustainability 2016, 8, 460. [CrossRef]
55. Penades-Pla, V.; Garcia-Segura, ; Marti, J.V.; Yepes, V. A Review of Multi-Criteria Decision-Making Methods Applied to the Sustainable Bridge Design. Sustainability 2016, 8, 1295. [CrossRef]
56. Keshavarz Ghorabaee, M.; Amiri, M.; Zavadskas, E.K.; Antucheviciene, J. Supplier evaluation and selection in fuzzy environments: A review of MADM approaches. Res. Ekon. Istraz. 2016, 30, 1073–1118. [CrossRef]
57. Si, J.; Marjanovic-Halburd, ; Nasiri, F.; Bell, S. Assessment of building-integrated green technologies: A review and case study on applications of Multi-Criteria Decision Making (MCDM) method. Sustain. Cities Soc. 2016, 27, 106–115. [CrossRef]
58. Streimikiene, ; Balezentis, T. Multi-criteria assessment of small scale CHP technologies in buildings.

Renew. Sustain. Energy Rev. 2013, 26, 183–189. [CrossRef]

59. Mardani, A.; Jusoh, A.; Zavadskas, E.K.; Cavallaro, F.; Khalifah, Z. Sustainable and Renewable Energy: An Overview of the Application of Multiple Criteria Decision Making Techniques and Approaches. Sustainability 2015, 7, 13947–13984. [CrossRef]
60. Cerveira, ; Baptista, J.; Solteiro Pires, E.J. Wind Farm Distribution Network Optimization. Integr. Comput.

Aided Eng. 2016, 23, 69–79. [CrossRef]

61. Mardani, A.; Jusoh, A.; Nor, K.M.D.; Khalifah, Z.; Zakwan, N.; Valipour, A. Multiple criteria decision-making techniques and their applications—A review of the literature from 2000 to Econ. Res. Ekon. Istraz. 2015, 28, 516–571. [CrossRef]
62. Mardani, A.; Jusoh, A.; Zavadskas, E.K. Fuzzy multiple criteria decision-making techniques and applications—Two decades review from 1994 to 2014. Expert Syst. Appl. 2015, 42, 4126–4148. [CrossRef]
63. Kahraman, ; Onar, S.C.; Oztaysi, B. Fuzzy Multicriteria Decision-Making: A Literature Review. Int. J.

Comput. Intell. Syst. 2015, 8, 637–666. [CrossRef]

64. Antucheviciene, J.; Kala, Z.; Marzouk, M.; Vaidogas, E.R. Solving Civil Engineering Problems by Means of Fuzzy and Stochastic MCDM Methods: Current State and Future Research. Probl. Eng. 2015, 2015, 362579. [CrossRef]
65. Zavadskas, E.K.; Mardani, A.; Turskis, Z.; Jusoh, A.; Nor, K.M.D. Development of TOPSIS Method to Solve Complicated Decision-Making Problems: An Overview on Developments from 2000 to 2015. J. Inf. Technol. Decis. Mak. 2016, 15, 645–682. [CrossRef]
66. Behzadian, ; Otaghsara, S.K.; Yazdani, M.; Ignatius, J. A state-of the-art survey of TOPSIS applications.

Expert Syst. Appl. 2012, 39, 13051–13069. [CrossRef]

67. Mardani, A.; Zavadskas, E.K.; Govindan, K.; Senin, A.A.; Jusoh, A. VIKOR Technique: A Systematic Review of the State of the Art Literature on Methodologies and Sustainability 2016, 8, 37. [CrossRef]
68. Balezentis, T.; Balezentis, A Survey on Development and Applications of the Multi-criteria Decision Making Method MULTIMOORA. J. Multi-Criteria Decis. Anal. 2014, 21, 209–222. [CrossRef]
69. Behzadian, ; Kazemadeh, R.B.; Albadvi, A.; Aghdasi, M. PROMETHEE: A comprehensive literature review on methodologies and applications. Eur. J. Oper. Res. 2010, 200, 198–215. [CrossRef]
70. Yang, ; Emmerich, M.; Baeck, T.; Kok, J. Multiobjective Inventory Routing with Uncertain Demand Using Population-based Metaheuristics. Integr. Comput. Aided Eng. 2016, 23, 205–220. [CrossRef]
71. Pan, ; He, C.; Tian, Y.; Su, Y.; Zhang, X. A Region Division Based Diversity Maintaining Approach for Many-Objective Optimization. Integr. Comput. Aided Eng. 2017, 24, 279–296. [CrossRef]
72. Marttunen, ; Lienert, J.; Belton, V. Structuring problems for Multi-Criteria Decision Analysis in practice: A literature review of method combinations. Eur. J. Oper. Res. 2017, 263, 1–17. [CrossRef]
73. Tzeng, -H.; Huang, J.J. Multiple Attribute Decision Making: Methods and Applications; CRC Press, Taylor and

Francis Group: Boca Raton, FL, USA, 2011; 349p.

74. Tzeng, G.-H.; Huang, J.J. Fuzzy Multiple Objective Decision Making; CRC Press, Taylor and Francis Group: Boca Raton, FL, USA, 2014; 313p.
75. Tzeng, G.-H.; Shen, K.-Y. New Concepts and Trends of Hybrid Multiple Criteria Decision Making; CRC Press, Taylor and Francis Group: Boca Raton, FL, USA, 2017.
76. Kou, ; Ergu, D.; Peng, Y.; Shi, Y. Data Processing for the AHP/ANP. In Quantitative Management; Springer: Berlin/Heidelberg, Germany, 2013; Volume 1.
77. Bisdorff, R.; Dias, L.C.; Meyer, P.; Mousseau, V.; Pirlot, M. (Eds.) Evaluation and Decision Models with Multiple Criteria: Case In International Handbooks on Information Systems; Springer: Berlin/Heidelberg, Germany, 2015.
78. Liu, -C. FMEA Using Uncertainty Theories and MCDM Methods; Springer: Singapore, 2016.
79. Naubi, I.; Zardari, N.H.; Shirazi, S.M.; Roslan, N.A.; Yusop, Z.; Haniffah, M.R.B.M. Ranking of Skudai river sub-watersheds from sustainability indices application of PROMETHEE Int. J. 2017, 12, 124–131. [CrossRef]
80. De la Fuente, A.; Blanco, A.; Armengou, J.; Aguado, A. Sustainability based-approach to determine the concrete type and reinforcement configuration of TBM tunnels linings. Case study: Extension line to Barcelona Airport Tunn. Undergr. Space Technol. 2017, 61, 179–188. [CrossRef]
81. Arroyo, P.; Fuenzalida, C.; Albert, A.; Hallowell, M.R. Collaborating in decision making of sustainable building design: An experimental study comparing CBA and WRC methods. Energy Build. 2016, 128, 132–142. [CrossRef]
82. Arroyo, P.; Tommelein, I.D.; Ballard, G. Comparing AHP and CBA as decision methods to resolve the choosing problem in detailed J. Constr. Eng. Manag. 2015, 141, 04014063. [CrossRef]
83. Ignatius, J.; Rahman, A.; Yazdani, M.; Šaparauskas, J.; Haron, S.H. An integrated fuzzy ANP–QFD approach for green building J. Civ. Eng. Manag. 2016, 22, 551–563. [CrossRef]
84. Hosseini, A.; de la Fuente, A.; Pons, O. Multicriteria decision-making method for sustainable site location of post-disaster temporary housing in urban areas. J. Constr. Eng. Manag. 2016, 142, 04016036. [CrossRef]
85. Chen, L.; Pan, W. BIM-aided variable fuzzy multi-criteria decision making of low-carbon building measures Sustain. Cities Soc. 2016, 27, 222–232. [CrossRef]
86. Jalaei, F.; Jrade, A.; Nassiri, M. Integrating Decision Support System (DSS) and Building Information Modeling (BIM) to Optimize the Selection of Sustainable Building J. Inf. Technol. Constr. 2015, 20, 399–420.
87. Medineckiene, ; Zavadskas, E.K.; Björk, F.; Turskis, Z. Multi-criteria decision-making system for sustainable building assessment/certification. Arch. Civ. Mech. Eng. 2015, 15, 11–18. [CrossRef]
88. Nakhaei, J.; Bitarafan, M.; Lale Arefi, S.; Kaplin´ski, Model for rapid assessment of vulnerability of office buildings to blast using SWARA and SMART methods (a case study of swiss re tower). J. Civ. Eng. Manag. 2016, 22, 831–843. [CrossRef]
89. Yousefi, H.; Ghodusinejad, M.H.; Noorollahi, Y. GA/AHP-based optimal design of a hybrid CCHP system considering economy, energy and Energy Build. 2017, 138, 309–317. [CrossRef]
90. Kalibatas, D.; Kovaitis, V. Selecting the most effective alternative of waterproofing membranes for multifunctional inverted flat J. Civ. Eng. Manag. 2017, 23, 650–660. [CrossRef]
91. Turskis, ; Daniu¯ nas, A.; Zavadskas, E.K.; Medzvieckas, J. Multicriteria evaluation of building foundation alternatives. Comput. Aided Civ. Infrastruct. Eng. 2016, 31, 717–729. [CrossRef]
92. Leonavicˇiu¯ te˙, ; De˙jus, T.; Antuchevicˇiene˙, J. Analysis and prevention of construction site accidents.

Građevinar 2016, 68, 399–410.

93. Turskis, ; Juodagalviene˙, B. A novel hybrid multi-criteria decision-making model to assess a stairs shape for dwelling houses. J. Civ. Eng. Manag. 2016, 22, 1078–1087. [CrossRef]
94. Ebrahimian, ; Ardeshir, A.; Rad, I.Z.; Ghodsypour, S.H. Urban stormwater construction method selection using a hybrid multi-criteria approach. Autom. Constr. 2015, 58, 118–128. [CrossRef]
95. Nezarat, H.; Sereshki, F.; Ataei, M. Ranking of geological risks in mechanized tunneling by using Fuzzy Analytical Hierarchy Process (FAHP). Undergr. Space Technol. 2015, 50, 358–364. [CrossRef]
96. Shariati, ; Abedi, M.; Saedi, A.; Yazdani-Chamzini, A.; Tamošaitiene˙, J.; Šaparauskas, J.; Stupak, S. Critical factors of the application of nanotechnology in construction industry by using ANP technique under fuzzy intuitionistic environment. J. Civ. Eng. Manag. 2017, 23, 914–925. [CrossRef]
97. Onat, ; Celik, E. An integral based fuzzy approach to evaluate waste materials for concrete.

Smart Struct. Syst. 2017, 19, 323–333. [CrossRef]

98. Na, K.L.; Lee, H.E.; Liew, M.S.; Zawawi, N.W.A. An expert knowledge based decommissioning alternative selection system for fixed oil and gas assets in the South China Ocean Eng. 2017, 130, 645–658. [CrossRef]
99. Naziris, A.; Lagaros, N.D.; Papaioannou, K. Optimized fire protection of cultural heritage structures based on the analytic hierarchy process. J. Build. Eng. 2016, 8, 292–304. [CrossRef]
100. Kursunoglu, N.; Onder, M. Selection of an appropriate fan for an underground coal mine using the Analytic Hierarchy Process. Undergr. Space Technol. 2015, 48, 101–109. [CrossRef]
101. Schöttle, A.; Arroyo, P. Comparison of Weighting-Rating-Calculating, Best Value, and Choosing by Advantages for Bidder J. Constr. Eng. Manag. 2017, 143, 05017015. [CrossRef]
102. Khanzadi, M.; Turskis, Z.; Ghodrati Amiri, G.; Chalekaee, A. A model of discrete zero-sum two-person matrix games with grey numbers to solve dispute resolution problems in J. Civ. Eng. Manag. 2017, 23, 824–835. [CrossRef]
103. Dadpour, M.; Shakeri, E. A Hybrid Model Based on Fuzzy Approach Type II to Select Private Sector in Partnership Projects. J. Sci. Technol. Trans. Civ. Eng. 2017, 41, 175–186. [CrossRef]
104. Jang, ; Hong, H.U.; Han, S.H.; Baek, S.W. Optimal supply vendor selection model for LNG plant projects using fuzzy-TOPSIS theory. J. Manag. Eng. 2016, 33, 04016035. [CrossRef]
105. Formisano, ; Castaldo, C.; Chiumiento, G. Optimal seismic upgrading of a reinforced concrete school building with metal-based devices using an efficient multi-criteria decision-making method. Struct. Infrastruct. Eng. 2017, 13, 1373–1389. [CrossRef]
106. Formisano, A.; Mazzolani, F.M. On the selection by MCDM methods of the optimal system for seismic retrofitting and vertical addition of existing Comput. Struct. 2015, 159, 1–13. [CrossRef]
107. Terracciano, G.; Di Lorenzo, G.; Formisano, A.; Landolfo, R. Cold-formed thin-walled steel structures as vertical addition and energetic retrofitting systems of existing masonry buildings. J. Environ. Civ. Eng. 2015, 19, 850–866. [CrossRef]
108. Cavalcante, C.A.V.; Alencar, M.H.; Lopes, R.S. Multicriteria Model to Support Maintenance Planning in Residential Complexes under J. Constr. Eng. Manag. 2016, 143, 04016110. [CrossRef]
109. Verma, ; Rajasankar, J.; Anandavalli, N.; Prakash, A.; Iyer, N.R. Fuzzy similarity approach for ranking and health assessment of towers based on visual inspection. Adv. Struct. Eng. 2015, 18, 1399–1414. [CrossRef]
110. Lin, S.C.J.; Ali, A.S.; Bin Alias, A. Analytic hierarchy process decision-making framework for procurement strategy selection in building maintenance work. Perform. Constr. Facil. 2015, 29, 04014050. [CrossRef]
111. Agrebi, M.; Abed, M.; Omri, M.N. ELECTRE I based relevance decision-makers feedback to the location selection of distribution J. Adv. Transp. 2017, 2017, 7131094. [CrossRef]
112. Baušys, ; Juodagalviene˙, B. Garage location selection for residential house by WASPAS-SVNS method.
113. Civ. Eng. Manag. 2017, 23, 421–429. [CrossRef]
114. Hosseini, S.A.; de la Fuente, A.; Pons, O. Multi-criteria decision-making method for assessing the sustainability of post-disaster temporary housing units technologies: A case study in Bam, Sustain. Cities Soc. 2016b, 20, 38–51. [CrossRef]
115. Saaty, L. The Analytic Hierarchy Process; McGraw-Hill: New York, NY, USA, 1980.
116. Hwang, L.; Yoon, K. Multiple Attributes Decision Making Methods and Applications; Springer: Berlin/Hedelberg, Germany, 1981.
117. San-José, J.T.; Cuadrado, J. Industrial building design stage based on a system approach to their environmental Constr. Build. Mater. 2010, 24, 438–447. [CrossRef]
118. Zavadskas, E.K.; Turskis, Z.; Antucheviciene, J. Selecting a contractor by using a novel method for multiple attribute analysis: Weighted Aggregated Sum Product Assessment with grey values (WASPAS-G). Inform. Control 2015, 24, 141–150. [CrossRef]
119. Mareschal, ; Brans, J.P. PROMETHEE V: MCDM Problems with Segmentation Constrains; Universite Libre de

Brusells: Brussels, Belgium, 1992.

119. Zavadskas, K.; Turskis, Z. A new additive ratio assessment (ARAS) method in multicriteria decision-making. Technol. Econ. Dev. Econ. 2010, 16, 159–172. [CrossRef]
120. Opricovic, Multicriteria Optimization of Civil Engineering Systems; University of Belgrade: Belgrade,

Serbia, 1998.

121. MacCrimmon, K.R. Decision Makingamong Multipleattribute Alternatives: A Survey and Consolidated Approach; RAND Memorandum, RM-4823-ARPA; RAND Corporation: Santa Monica, CA, USA, 1968.
122. Laplace, -S. Essai Philosophique sur les Probabilités; Courcier: Paris, France, 1814.
123. Hurwicz, Optimality Criteria for Decision-Making under Ignorance: Cowles Commission Paper. Statistics

1951, 370, 45–52.

124. Bayes, An Essay towards solving a Problem in the Doctrine of Chances. Philos. Trans. 1763, 53, 370–418. [CrossRef]
125. Zavadskas, E.K.; Turskis, Z.; Antuchevicˇiene˙, J.; Zakarevicˇius, A. Optimization of weighted aggregated sum product Electron. Electr. Eng. 2012, 122, 3–6. [CrossRef]
126. Roy, La methode ELECTRE. Rev. Inform. Rech. Oper. RIRO 1968, 8, 57–75.
127. Saaty, L. Decision Making with Dependence and Feedback. The Analytic Network Process; RWS Publications: Pitsburg, PA, USA, 1996; 370p.
128. Keršuliene˙, V.; Zavadskas, E.K.; Turskis, Z. Selection of rational dispute resolution method by applying new stepwise weight assessment ratio analysis (SWARA). Bus. Econ. Manag. 2010, 11, 243–258. [CrossRef]
129. Keshavarz Ghorabaee, M.; Zavadskas, E.K.; Laya, O.; Turskis, Z. Multi-Criteria Inventory Classification Using a New Method of Evaluation Based on Distance from Average Solution (EDAS). Informatica 2015, 26, 435–451. [CrossRef]
130. Yoon, ; Hwang, C. Multiple Attribute Decision Making: An Introduction; Sage Publications: London, UK, 1995.
131. Hodges, L.; Lehmann, E.L. The Use of Previous Experience in Reaching Statistical Decision. Ann. Math. Stud.

1952, 23, 396–407. [CrossRef]

132. Bridgman, W. Dimensional Analysis; Yale University Press: New Haven, CT, USA, 1922.
133. Zyoud, H.; Fuchs-Hanusch, D. A bibliometric-based survey on AHP and TOPSIS techniques.

Expert Syst. Appl. 2017, 78, 158–181. [CrossRef]

134. Dzitac, I.; Filip, G.; Manolescu, M.J. Fuzzy Logic Is Not Fuzzy: World-renowned Computer Scientist Lotfi
135. Zadeh. Int. J. Comput. Commun. Control 2017, 12, 748–789. [CrossRef]