Uptake and Dissemination of Multi-Criteria Decision Support Methods in Civil Engineering—Lessons from the Literature
Abstract
:1. Introduction
2. Background
3. Previous Work
4. Methodology of Literature Review
and(“multi criteria” OR “multi-criteria” OR “multi criterion” OR “multi-criterion”)
(“Civil Engineering” OR “structural engineering” OR “municipal engineering” OR “highway engineering” OR “transportation engineering” OR “geotechnical engineering” OR “water engineering” OR “water resources engineering”)
5. Analysis of Results
5.1. Basic Statistics
5.2. Most Active Geographical Regions
5.3. Scope of Results
6. Comparison with Focused Searches
7. Focus on Bridge Design
8. Focus on Earthquake Engineering
9. Focus on Cladding
10. Focus on Sewage Treatment
11. Focus on Route Selection
12. Focus on Transport Mode
13. Focus on Building Energy
14. Focus on Water Supply
- (i)
- There is a very substantial difference in the articles returned from a bibliometric search using broad keywords compared to using focused keywords. This is despite the expectation that the broader keywords should cover the topics of the focused search. This is a concern for at least two reasons. (a) Many useful publications can be missed if the search terms are too broad, even when a large number of publications are returned by the broad search. Additionally, (b) even when the engineer doing the search realizes that focused keywords are more useful, there is an added responsibility to make sure the focused terms cover all of the domains required in the search.
- (ii)
- The decision criteria keywords most frequently used in the broad search relate to sustainability, environment, risk, and safety, but cost does not feature in the list nor does physical resource usage.
- (iii)
- The results of the individual focused searches had only marginal overlap with the earlier broad civil engineering searche. However, some returned a relatively large number of articles covering many aspects of the specific topic, especially the “route selection”, “sewage treatment” and “water supply” searches.
- (iv)
- While many MCDA methods are mentioned, AHP is by far the most used method, followed by TOPSIS and SAW. Many papers using AHP justify their choice because it is the most frequently used method. This is not a sufficiently rigorous position and more emphasis is needed on choosing the most appropriate MCDA method to suit the specific problem and the information available. The non-compensatory methods are not as frequently mentioned, but of these, PROMETHEE is the most prominent.
- (v)
- In some cases, combinations of methods are used. For instance, AHP for determining weights has been used with TOPSIS for the decision methodology. PROMETHEE has also been used in conjunction with other methods. In one case, three methods were combined, AHP, TOPSIS and COPRAS.
- (vi)
- In the applications papers reviewed above, the largest number of criteria used in an example was 32 and the minimum was 3 with a mode of 7, so, in most cases, MCDA is being applied to decisions with considerable complexity.
- (vii)
- Only a small number of papers mention the drawbacks of many of the methods, particularly the possibility of rank reversal [158] and its dependence on the number and type of alternatives considered in the analysis. This is disappointing because there are precautions that can be taken to mitigate this potential problem [159] and most of the articles analyzed here do not appear to address this issue.
- (viii)
- While a reasonable number of papers integrated MCDA with GIS when the decisions had a spatial character, very few integrated with Building Information Systems (BIM). In the Civil Engineering context, more would be expected since BIM can be considered a major tool in the design and construction of structures [160].
- (ix)
- Relevant papers known to this author did not trigger the bibliographic search criteria so were not selected. This emphasizes the important of using a mixture of specific and broad keywords to ensure papers are found and included in reviews.
- (x)
- Papers are being published relating MCDA applied to Engineering education.
- (xi)
- The main application areas have a high degree of complexity either in design or resource allocation configurations that benefit from an automated decision support capability.
- (xii)
- Surprisingly, the name of any specific software package did not appear in the keywords of papers in the database. This may be because of a lack of awareness by Civil Engineers of the software support available, irrespective of which MCDA method they prefer. The following section lists some of the main packages available.
15. Mainstream Software
16. Conclusions
- In a broad search, there was an overlap of only 13 documents between the results of the search of SCOPUS and of Web of Science and this indicates the advisability of accessing multiple databases when conducting a literature review.
- The published literature on MCDA applications in civil engineering shows a wide geographical spread with Northern Hemisphere continents all represented. However, its coverage of the individual topic areas related to civil engineering is fragmented, focusing particularly on applications in the sustainability, environmental and risk areas and to some extent on project management decision making.
- The published applications do not demonstrate a wide use of all available methodologies, as only two methods (AHP and TOPSIS) appeared in a keyword analysis. However, analysis of citations contained in the published papers did demonstrate some awareness of alternative methods.
- There is a large difference between the results of searches using broad keywords and searches using focused keywords (Bridge Design, Earthquake Engineering, Cladding, Sewage Treatment, Foundation design, Truss design, Water Supply, Building Energy, Route selection and Transport mode). Engineers looking for the use of MCDA in specific focused topics should find relevant published work. However, searches trying to gauge the extent of use of MCDA in Civil Engineering may not uncover the full range of such applications or the methods used without a careful choice of search keywords and databases.
- The previous point implies that communication of information, via publications, on MCDA between engineers could be improved by careful choice of keywords. This applies both to those making literature searches and to the authors of technical articles who seek the widest dissemination of their work and should choose appropriate keywords. A mixture of broad and focused keywords (where applicable) seems to be preferable.
- There is a wide range of available software implementations of most methods, both free and licensed, however none feature highly in keyword lists or are mentioned in third-party published literature. This suggests a lack of awareness of the available software tools. Perhaps overview articles such as this one can help address this lacuna.
- Thus, a wider dissemination of knowledge on both the methods themselves, but particularly on the availability and practical use of software implementations, is recommended for civil engineers. This could include both enhancement of the treatment of the topic in University engineering curricula as well as in Continuing Professional Development (CPD) programs for practicing engineers.
- From the literature survey, it appears that civil engineers are not closely involved in developing new MCDA methodologies but tend to work in teams (most documents found in the search were multi-authored) and in the application of existing methods. The teamwork element is appropriate and is part of an engineers’ formation. However, a more active role in developing decision support methods would be welcome.
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Rogers, M.; Bruen, M. Non-monetary based decision-aid techniques in Environmental Impact Assessment—An overview. Proc. Instn. Civ. Engrs. Mun. Engr. 1995, 109, 98–103. [Google Scholar]
- Stigler, G. The Development of Utility Theory. I. J. Political Econ. 1950, 58, 307–327. [Google Scholar] [CrossRef]
- Shogren, J.F.E. Encyclopedia of Energy, Natural Resource, and Environmental Economics; Shogren, J.F., Ed.; Elsevier: Amsterdam, The Netherlands, 2013. [Google Scholar]
- Kaliszewski, I.; Podkopaev, D. Simple additive weighting—A metamodel for multiple criteria decision analysis methods. Expert Syst. Appl. 2016, 54, 155–161. [Google Scholar] [CrossRef]
- Keeney, R.L.; Raiffa, H. Decisions with Multiple Objectives—Preferences and Value Tradeoffs; John Wiley: Hoboken, NJ, USA, 1976. [Google Scholar]
- Saaty, T.L. The Analytic Hierarchy Process; McGraw-Hill: New York, NY, USA, 1980. [Google Scholar]
- Hwang, C.L.; Yoon, K. Multi Atribute Decision Making: Methods and Applications: A State of the Art Survey; Springer: Berlin/Heidelberg, Germany, 1981. [Google Scholar]
- Belton, V.; Stewart, T. Multiple Criteria Decision Analysis—An Integrated Approach; Springer: Berlin/Heidelberg, Germany, 2002. [Google Scholar]
- Roy, B. The outranking approach and the foundations of electre methods. Theory Decis. 1991, 31, 49–73. [Google Scholar] [CrossRef]
- Roy, B.; Hugonnard, J.C. Ranking of suburban line extension projects on the Paris metro system by a multicriteria method. Transp. Res. Part A Gen. 1982, 16, 301–312. [Google Scholar] [CrossRef]
- Brans, J.P.; Vincke, P. Note—A Preference Ranking Organisation Method. Manag. Sci. 1985, 31, 647–656. [Google Scholar] [CrossRef] [Green Version]
- Martel, J.-M.; Matarazzo, B. Other Outranking Approaches. In Multiple Criteria Decision Analysis: State of the Art Surveys; Figueira, J., Greco, S., Ehrogott, M., Eds.; Springer New York: New York, NY, USA, 2005; pp. 197–259. [Google Scholar] [CrossRef]
- Greco, S.; Matarazzo, B.; Słowiński, R. Decision Rule Approach. In Multiple Criteria Decision Analysis: State of the Art Surveys; Greco, S., Ehrgott, M., Figueira, J.R., Eds.; Springer: New York, NY, USA, 2016. [Google Scholar]
- Hamalainen, R.P.; Seppalainen, T.O. The analytic network process in energy-policy planning. Socio-Econ. Plan. Sci. 1986, 20, 399–405. [Google Scholar] [CrossRef]
- Lichfield, N. Cost-benefit-analysis in city-planning. J. Am. Inst. Plan. 1960, 26, 273–279. [Google Scholar] [CrossRef]
- Kaklauskas, A.; Zavadskas, E.K.; Raslanas, S.; Ginevicius, R.; Komka, A.; Malinauskas, P. Selection of low-e windows in retrofit of public buildings by applying multiple criteria method COPRAS: A Lithuanian case. Energy Build. 2006, 38, 454–462. [Google Scholar] [CrossRef]
- Diakoulaki, D.; Mavrotas, G.; Papayannakis, L. Determining objective weights in multiple criteria problems: The critic method. Comput. Oper. Res. 1995, 22, 763–770. [Google Scholar] [CrossRef]
- Costa, C.A.B.E.; Corte, J.-M.D.; Vansnick, J.-C. Macbeth. Int. J. Inf. Technol. Decis. Mak. 2012, 11, 359–387. [Google Scholar] [CrossRef]
- Poyhonen, M.; Hamalainen, R.P. On the convergence of multiattribute weighting methods. Eur. J. Oper. Res. 2001, 129, 569–585. [Google Scholar] [CrossRef] [Green Version]
- Brauers, W.K.M.; Zavadskas, E.K.; Peldschus, F.; Turskis, Z. Multi-objective decision-making for road design. Transport 2008, 23, 183–193. [Google Scholar] [CrossRef]
- Simos, J. Evaluer L’impact sur L’environnement: Une Approche Originale par l’analyse Multicritere et la Negociation; Presses Polytechniques et Universitaires Romandes: Lausanne, Switzerland, 1990. [Google Scholar]
- Zavadskas, E.K. and Antuchevičienè, J. Evaluation of buildings’ redevelopment alternatives with an emphasis on the multipartite sustainability. Int. J. Strateg. Prop. Manag. 2004, 8, 121–128. [Google Scholar] [CrossRef] [Green Version]
- Araña, J.E.; León, C.J. Understanding the use of non-compensatory decision rules in discrete choice experiments: The role of emotions. Ecol. Econ. 2009, 68, 2316–2326. [Google Scholar] [CrossRef]
- Abbas, A.E. Constructing Multiattribute Utility Functions for Decision Analysis. In Risk and Optimization in an Uncertain World; Informs: Catonsville, MD, USA, 2010; pp. 62–98. [Google Scholar] [CrossRef] [Green Version]
- Munda, G.; Nardo, M. Noncompensatory/nonlinear composite indicators for ranking countries: A defensible setting. Appl. Econ. 2009, 41, 1513–1523. [Google Scholar] [CrossRef] [Green Version]
- Sałabun, W.; Watróbski, J.; Shekhovtsov, A. Are MCDA methods benchmarkable? A comparative study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II methods. Symmetry 2020, 12, 1549. [Google Scholar] [CrossRef]
- Cinelli, M.; Kadziński, M.; Gonzalez, M.; Słowiński, R. How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy. Omega 2020, 96, 102261. [Google Scholar] [CrossRef]
- Zavadskas, E.K.; Antuchevičienė, J.; Kapliński, O. Multi-criteria decision making in civil engineering: Part I—A state-of-the-art survey. Eng. Struct. Technol. 2016, 7, 103–113. [Google Scholar] [CrossRef]
- Ogrodnik, K. Multi-Criteria Analysis of Design Solutions in Architecture and Engineering: Review of Applications and a Case Study. Buildings 2019, 9, 244. [Google Scholar] [CrossRef] [Green Version]
- Ghaleb, A.M.; Kaid, H.; Alsamhan, A.; Mian, S.H.; Hidri, L. Assessment and Comparison of Various MCDM Approaches in the Selection of Manufacturing Process. Adv. Mater. Sci. Eng. 2020, 2020. [Google Scholar] [CrossRef]
- Maciol, A.; Rebiasz, B. Multicriteria decision analysis (MCDA) methods in life cycle assessment (LCA). A comparison of private passenger vehicles. Oper. Res. Decis. 2018, 28, 5–26. [Google Scholar] [CrossRef]
- Srisawat, C.; Payakpate, J. COMPARISON of mcdm methods for intercrop selection in rubber plantations. J. Inf. Commun. Technol. Malays. 2016, 15, 165–182. [Google Scholar] [CrossRef]
- Kralik, L.; Senkerik, R.; Jasek, R. Comparison of MCDM methods with Users’ Evaluation. In Proceedings of the 2016 11th Iberian Conference on Information Systems and Technologies, Gran Canaria, Spain, 15–18 June 2016. [Google Scholar]
- Kittur, J.; Vijaykumar, S.; Bellubbi, V.P.; Vishal, P.; Shankara, M.G. Comparison of Different MCDM Techniques Used to Evaluate Optimal Generation; Institute of Electrical and Electronics Engineers (IEEE): New York, NY, USA, 2015; pp. 172–177. [Google Scholar]
- Sojda, R.S. Empirical evaluation of decision support systems: Needs, definitions, potential methods, and an example pertaining to waterfowl management. Environ. Model. Softw. 2007, 22, 269–277. [Google Scholar] [CrossRef]
- Myšiak, J. Consistency of the Results of Different MCA Methods: A Critical Review. Environ. Plan. C Gov. Policy 2006, 24, 257–277. [Google Scholar] [CrossRef]
- Navarro, I.J.; Penadés-Plà, V.; Martínez-Muñoz, D.; Rempling, R.; Yepes, V. Life cycle sustainability assessment for multi-criteria decision making in bridge design: A review. J. Civ. Eng. Manag. 2020, 26, 690–704. [Google Scholar] [CrossRef]
- Aria, M.; Cuccurullo, C. bibliometrix: An R-tool for comprehensive science mapping analysis. J. Informetr. 2017, 11, 959–975. [Google Scholar] [CrossRef]
- Chen, J.; Konstan, J.A. Conference paper selectivity and impact. Commun. Acm 2010, 53, 79–83. [Google Scholar] [CrossRef]
- Navarro Martinez, I.; Yepes Piqueras, V.; Marti Albinana, J.B. Multi-criteria decision making in engineering eduation for sustainability. In Proceedings of the ICERI2018 Conference, Seville, Spain, 12–14 November 2018. [Google Scholar]
- Dadpour, M.; Shakeri, E. A Hybrid Model Based on Fuzzy Approach Type II to Select Private Sector in Partnership Projects. Iran. J. Sci. Technol. Trans. Civ. Eng. 2017, 41, 175–186. [Google Scholar] [CrossRef]
- Roy, B.; Présent, M.; Silhol, D. A programming method for determining which Paris metro stations should be renovated. Eur. J. Oper. Res. 1986, 24, 318–334. [Google Scholar] [CrossRef]
- Ellis, K.V.; Tang, S.L. Wastewater Treatment Optimization Model for Developing World. I: Model Development. J. Environ. Eng. 1991, 117, 501–518. [Google Scholar] [CrossRef]
- Bouyssou, D. Outranking methods. In Encyclopedia of Optimization; Floudas, C.A., Pardalos, P.M., Eds.; Springer: Boston, MA, USA, 2009; pp. 2887–2893. [Google Scholar] [CrossRef]
- Zadeh, L.A. Fuzzy Sets. Inf. Control 1965, 8, 338–353. [Google Scholar] [CrossRef] [Green Version]
- Saaty, T.L. Decision Making with the Analytic Hierarchy Process. Intern. J. Serv. Sci. 2008, 1, 83–98. [Google Scholar] [CrossRef] [Green Version]
- Saaty, T.L. How to make a decision: The analytic hierarchy process. Eur. J. Oper. Res. 1990, 48, 9–26. [Google Scholar] [CrossRef]
- Jato-Espino, D.; 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. [Google Scholar] [CrossRef]
- Buckley, J.J. Fuzzy hierarchical analysis. Fuzzy Sets Syst. 1985, 17, 233–247. [Google Scholar] [CrossRef]
- Chang, D.-Y. Applications of the extent analysis method on fuzzy AHP. Eur. J. Oper. Res. 1996, 95, 649–655. [Google Scholar] [CrossRef]
- Behzadian, M.; Kazemzadeh, R.B.; Albadvi, A.; Aghdasi, M. PROMETHEE: A comprehensive literature review on methodologies and applications. Eur. J. Oper. Res. 2010, 200, 198–215. [Google Scholar] [CrossRef]
- Ek, K.; Mathern, A.; Rempling, R.; Brinkhoff, P.; Karlsson, M.; Norin, M. Life Cycle Sustainability Performance Assessment Method for Comparison of Civil Engineering Works Design Concepts: Case Study of a Bridge. Int. J. Environ. Res. Public Health 2020, 17, 7909. [Google Scholar] [CrossRef]
- Rempling, R.; Mathern, A.; Tarazona Ramos, D.; Luis Fernández, S. Automatic structural design by a set-based parametric design method. Autom. Constr. 2019, 108, 102936. [Google Scholar] [CrossRef]
- Kripka, M.; Yepes, V.; Milani, C.J. Selection of Sustainable Short-Span Bridge Design in Brazil. Sustainability 2019, 11, 1307. [Google Scholar] [CrossRef] [Green Version]
- Markogiannaki, O.G.; Tegos, N.I. Towards accelerated construction and cost reduction of monolithical bridges facing earthquake hazard. In COMPDYN Proceedings—Crete; Institute of Structural Analysis and Antiseismic Research, NTU Athens: Athens, Greece, 2019; pp. 3747–3760. [Google Scholar]
- García-Segura, T.; Penadés-Plà, V.; Yepes, V. Sustainable bridge design by metamodel-assisted multi-objective optimization and decision-making under uncertainty. J. Clean. Prod. 2018, 202, 904–915. [Google Scholar] [CrossRef]
- Harirchian, E.; Jadhav, K.; Mohammad, K.; Aghakouchaki Hosseini, S.E.; Lahmer, T. A Comparative Study of MCDM Methods Integrated with Rapid Visual Seismic Vulnerability Assessment of Existing RC Structures. Appl. Sci. 2020, 10, 6411. [Google Scholar] [CrossRef]
- Barkhordari, M.S.; Tehranizadeh, M. Ranking passive seismic control systems by their effectiveness in reducing responses of high-Rise buildings with concrete shear walls using multiple-Criteria decision making. Int. J. Eng. Trans. B Appl. 2020, 33, 1479–1490. [Google Scholar] [CrossRef]
- Dwivedi, V.K.; Dubey, R.K.; Pancholi, V.; Rout, M.M.; Singh, P.; Sairam, B.; Chopra, S.; Rastogi, B.K. Multi criteria study for seismic hazard assessment of UNESCO world heritage Ahmedabad City, Gujarat, Western India. Bull. Eng. Geol. Environ. 2020, 79, 1721–1733. [Google Scholar] [CrossRef]
- Georgescu, E.; Gociman, C.; Craifaleanu, I.; Florescu, T.; Georgescu, M.; Moscu, C. Architectural vs. structural constraints in urban multi-hazard safety assessment. In Proceedings of the 3rd International Conference on Structures and Architecture, ICSA 2016, Atlanta, GA, USA, 12–15 June 2016; pp. 1302–1309. [Google Scholar]
- Vona, M.; Murgante, B. Seismic retrofitting of strategic buildings based on multi-criteria decision-making analysis. In Proceedings of the 4th International Symposium on Life-Cycle Civil Engineering, IALCCE 2014, Tokyo, Japan, 16–19 November 2014; pp. 1846–1851. [Google Scholar]
- Karaman, H.; Erden, T. Net earthquake hazard and elements at risk (NEaR) map creation for city of Istanbul via spatial multi-criteria decision analysis. Nat. Hazards 2014, 73, 685–709. [Google Scholar] [CrossRef]
- Akin, M.K.; Topal, T.; Kramer, S.L. Seismic microzonation of Erbaa, Tokat Province, Turkey, based on analytical hierarchical process. Environ. Eng. Geosci. 2012, 18, 191–207. [Google Scholar] [CrossRef]
- Ilter, E.; Celik, O.C.; Unlu, A. Multi-criteria performance evaluation of a glass panel system using full-scale experimental data. Archit. Sci. Rev. 2020. [Google Scholar] [CrossRef]
- Roshan, P.; Pal, S.; Kumar, R. Performance Assessment Indexing of Buildings Through Fuzzy AHP Methodology. Lect. Notes Civil Eng. 2020, 58, 503–519. [Google Scholar]
- Bocchini, P.; Frangopol, D.M. Restoration of bridge networks after an earthquake: Multicriteria intervention optimization. Earthq. Spectra 2012, 28, 427–455. [Google Scholar] [CrossRef]
- Zamanifar, M.; Hartmann, T. Decision attributes for disaster recovery planning of transportation networks; A case study. Transp. Res. Part D Transp. Environ. 2021, 93, 102771. [Google Scholar] [CrossRef]
- Kilanitis, I.; Sextos, A. Integrated seismic risk and resilience assessment of roadway networks in earthquake prone areas. Bull. Earthq. Eng. 2019, 17, 181–210. [Google Scholar] [CrossRef] [Green Version]
- Sextos, A.G.; Kilanitis, I. Methodology, software and policy for optimum seismic resilience of highway networks. In Proceedings of the 11th National Conference on Earthquake Engineering 2018, Los Angeles, CA, USA, 25–29 June 2018; pp. 2939–2949. [Google Scholar]
- Bostenaru Dan, M.D. Multi-criteria decision model for retrofitting existing buildings. Nat. Hazards Earth Syst. Sci. 2004, 4, 485–499. [Google Scholar] [CrossRef] [Green Version]
- Bostenaru Dan, M.D. Review of retrofit strategies decision system in historic perspective. Nat. Hazards Earth Syst. Sci. 2004, 4, 449–462. [Google Scholar] [CrossRef]
- Gentile, R.; Galasso, C. Optimal retrofit selection for seismically-deficient RC buildings based on simplified performance assessment. In COMPDYN Proceedings Crete; Institute of Structural Analysis and Antiseismic Research, NTU Athens: Athens, Greece, 2019; pp. 1146–1160. [Google Scholar]
- Vona, M.; Harabaglia, P.; Murgante, B. Thinking about resilient cities: Studying Italian earthquakes. Proc. Inst. Civ. Eng. Urban Des. Plan. 2016, 169, 185–199. [Google Scholar] [CrossRef]
- Caterino, N.; Cosenza, E. A multi-criteria approach for selecting the seismic retrofit intervention for an existing structure accounting for expected losses and tax incentives in Italy. Eng. Struct. 2018, 174, 840–850. [Google Scholar] [CrossRef]
- Santa-Cruz, S.; Brioso, X.; Córdova-Arias, C. Selection of seismic retrofitting techniques through a multi-criteria methodology and BIM tools to improve transparency. In Proceedings of the 11th National Conference on Earthquake Engineering 2018, Los Angeles, CA, USA, 25–29 June 2018; pp. 2517–2526. [Google Scholar]
- Ćosić, M.; Folić, R.; Folić, B. Multidisciplinary Approach to the Assessment of Seismic Performances and Rehabilitation of Bridges: Nonlinear Analyses, Probability Theory and Optimization Theory. Procedia Eng. 2016, 156, 83–90. [Google Scholar] [CrossRef] [Green Version]
- Maddaloni, G.; Caterino, N.; Nestovito, G.; Occhiuzzi, A. Exploring New Boundaries to Mitigate Structural Vibrations of Bridges in Seismic Regions: A Smart Passive Strategy. Shock Vib. 2016, 2016, 4528168. [Google Scholar] [CrossRef]
- Hedayati Dezfuli, F.; Alam, M.S. Multi-criteria optimization and seismic performance assessment of carbon FRP-based elastomeric isolator. Eng. Struct. 2013, 49, 525–540. [Google Scholar] [CrossRef]
- Jain, R.K.; Purandare, A.S. Validation of the proposed liquefaction criterion of sand with fines by static tri-axial shear testing. Int. J. Civ. Eng. Technol. 2018, 9, 31–40. [Google Scholar]
- Takano, A.; Hughes, M.; Winter, S. A multidisciplinary approach to sustainable building material selection: A case study in a Finnish context. Build. Environ. 2014, 82, 526–535. [Google Scholar] [CrossRef]
- Ferreira, C.; Dias, I.S.; Silva, A.; de Brito, J.; Flores-Colen, I. Criteria for selection of cladding systems based on their maintainability. J. Build. Eng. 2021, 39, 102260. [Google Scholar] [CrossRef]
- Ferreira, C.; Silva, A.; de Brito, J.; Dias, I.S.; Flores-Colen, I. Definition of a condition-based model for natural stone claddings. J. Build. Eng. 2021, 33, 101643. [Google Scholar] [CrossRef]
- Ferreira, C.; Silva, A.; de Brito, J.; Dias, I.S.; Flores-Colen, I. Maintenance modelling of ceramic claddings in pitched roofs based on the evaluation of their in situ degradation condition. Infrastructures 2020, 5, 77. [Google Scholar] [CrossRef]
- Dodgson, J.; Spackman, M.; Pearman, A.; Phillips, L. Multi-Criteria Analysis: A Manual. 2009. Available online: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/191506/Mult-crisis_analysis_a_manual.pdf (accessed on 24 March 2021).
- Bapat, H.; Sarkar, D.; Gujar, R. Application of integrated fuzzy FCM-BIM-IoT for sustainable material selection and energy management of metro rail station box project in western India. Innov. Infrastruct. Solut. 2021, 6, 73. [Google Scholar] [CrossRef]
- Helmer-Hirschberg, O. Analysis of the Future: The Delphi Method; RAND Corporation: Santa Monica, CA, USA, 1967. [Google Scholar]
- Moussavi Nadoushani, Z.S.; Akbarnezhad, A.; Ferre Jornet, J.; Xiao, J. Multi-criteria selection of façade systems based on sustainability criteria. Build. Environ. 2017, 121, 67–78. [Google Scholar] [CrossRef]
- Friedrich, D.; Luible, A. Assessment of standard compliance of Central European plastics-based wall cladding using multi-criteria decision making (MCDM). Case Stud. Struct. Eng. 2016, 5, 27–37. [Google Scholar] [CrossRef] [Green Version]
- Cengiz, A.E.; Aytekin, O.; Ozdemir, I.; Kusan, H.; Cabuk, A. A Multi-criteria Decision Model for Construction Material Supplier Selection. In Procedia Engineering; Elsevier Ltd.: Amsterdam, The Netherlands, 2017; pp. 294–301. [Google Scholar] [CrossRef]
- Hamida, H.; Alshibani, A. A multi-criteria decision-making model for selecting curtain wall systems in office buildings. J. Eng. Des. Technol. 2020. [Google Scholar] [CrossRef]
- Guarini, M.R.; Morano, P.; Sica, F. Integrated ecosystem design: An evaluation model to support the choice of eco-compatible technological solutions for residential building. Energies 2019, 12, 2659. [Google Scholar] [CrossRef] [Green Version]
- Mannina, G.; Rebouças, T.F.; Cosenza, A.; Sànchez-Marrè, M.; Gibert, K. Decision support systems (DSS) for wastewater treatment plants—A review of the state of the art. Bioresour. Technol. 2019, 290. [Google Scholar] [CrossRef] [PubMed]
- Castillo, A.; Porro, J.; Garrido-Baserba, M.; Rosso, D.; Renzi, D.; Fatone, F.; Gómez, V.; Comas, J.; Poch, M. Validation of a decision support tool for wastewater treatment selection. J. Environ. Manag. 2016, 184, 409–418. [Google Scholar] [CrossRef] [PubMed]
- Liu, B.; Tang, J.; Li, Z.; Yan, Y.; Chen, J. Optimal Selection of Sewage Treatment Technologies in Town Areas: A Coupled Multi-Criteria Decision-Making Model. Environ. Manag. 2020, 66, 709–721. [Google Scholar] [CrossRef]
- Munasinghe-Arachchige, S.P.; Abeysiriwardana-Arachchige, I.S.A.; Delanka-Pedige, H.M.K.; Nirmalakhandan, N. Sewage treatment process refinement and intensification using multi-criteria decision making approach: A case study. J. Water Process Eng. 2020, 37. [Google Scholar] [CrossRef]
- Ren, J.; Liang, H. Multi-criteria group decision-making based sustainability measurement of wastewater treatment processes. Environ. Impact Assess. Rev. 2017, 65, 91–99. [Google Scholar] [CrossRef]
- Garrido-Baserba, M.; Reif, R.; Molinos-Senante, M.; Larrea, L.; Castillo, A.; Verdaguer, M.; Poch, M. Application of a multi-criteria decision model to select of design choices for WWTPs. Clean Technol. Environ. Policy 2016, 18, 1097–1109. [Google Scholar] [CrossRef]
- Tjandraatmadja, G.; Sharma, A.K.; Grant, T.; Pamminger, F. A Decision Support Methodology for Integrated Urban Water Management in Remote Settlements. Water Resour. Manag. 2013, 27, 433–449. [Google Scholar] [CrossRef]
- Diaper, C.; Sharma, A. Innovative sewerage solutions for small rural towns. In Water Science and Technology; International Water Association: London, UK, 2007; Volume 56, pp. 97–103. [Google Scholar]
- Vashi, A.N.; Shah, N.C. Impacts of a participatory approach to assess sustainable sewage treatment technologies for urban fringe of Surat city in India. In Water Science and Technology; International Water Association: London, UK, 2008; Volume 57, pp. 1957–1962. [Google Scholar]
- Tang, C.; Xu, D.; Chen, N. Sustainability prioritization of sewage sludge to energy scenarios with hybrid-data consideration: A fuzzy decision-making framework based on full consistency method and fusion ranking model. Environ. Sci. Pollut. Res. 2021, 28, 5548–5565. [Google Scholar] [CrossRef] [PubMed]
- Gomes, L.A.; Santos, A.F.; Pinheiro, C.T.; Góis, J.C.; Quina, M.J. Screening of waste materials as adjuvants for drying sewage sludge based on environmental, technical and economic criteria. J. Clean. Prod. 2020, 259. [Google Scholar] [CrossRef]
- Passuello, A.; Cadiach, O.; Perez, Y.; Schuhmacher, M. A spatial multicriteria decision making tool to define the best agricultural areas for sewage sludge amendment. Environ. Int. 2012, 38, 1–9. [Google Scholar] [CrossRef] [PubMed]
- Carroll, S.; Goonetilleke, A.; Dawes, L. Framework for soil suitability evaluation for sewage effluent renovation. Environ. Geol. 2004, 46, 195–208. [Google Scholar] [CrossRef] [Green Version]
- Vaseghi, E.; Zare Mehrjerdi, M.R.; Boshrabadi, H.M.; Nikouei, A. Prioritizing potential use of urban treated wastewater using expert-oriented and multi-criteria decision-making approaches: A case study in Iran. Water Sci. Technol. 2020, 82, 81–96. [Google Scholar] [CrossRef] [PubMed]
- Zolfaghary, P.; Zakerinia, M.; Kazemi, H. A model for the use of urban treated wastewater in agriculture using multiple criteria decision making (MCDM) and geographic information system (GIS). Agric. Water Manag. 2021, 243. [Google Scholar] [CrossRef]
- Aldababseh, A.; Temimi, M.; Maghelal, P.; Branch, O.; Wulfmeyer, V. Multi-criteria evaluation of irrigated agriculture suitability to achieve food security in an arid environment. Sustainability 2018, 10, 803. [Google Scholar] [CrossRef] [Green Version]
- Hama, A.R.; Al-Suhili, R.H.; Ghafour, Z.J. A multi-criteria GIS model for suitability analysis of locations of decentralized wastewater treatment units: Case study in Sulaimania, Iraq. Heliyon 2019, 5. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- De Gisi, S.; Pica, R.; Casella, P.; Notarnicola, M. Dealing with a cluster of large centralized municipal wastewater treatment plants: A case study. Process Saf. Environ. Prot. 2018, 118, 268–278. [Google Scholar] [CrossRef]
- Vasiloglou, V.; Lokkas, F.; Gravanis, G. New tool for wastewater treatment units location. Desalination 2009, 248, 1039–1048. [Google Scholar] [CrossRef]
- Rodríguez-Sinobas, L.; Zubelzu, S.; Perales-Momparler, S.; Canogar, S. Techniques and criteria for sustainable urban stormwater management. The case study of Valdebebas (Madrid, Spain). J. Clean. Prod. 2018, 172, 402–416. [Google Scholar] [CrossRef]
- Roghanian, E.; Kebria, Z.S. The combination of TOPSIS method and Dijkstra’s algorithm in multi-attribute routing. Sci. Iran. 2017, 24, 2540–2549. [Google Scholar] [CrossRef] [Green Version]
- Jang, J.R. ANFIS: Adaptive-network-based fuzzy inference system. Ieee Trans. Syst. ManCybern. 1993, 23, 665–685. [Google Scholar] [CrossRef]
- Qu, L.; Chen, Y. A hybrid MCDM method for route selection of multimodal transportation network. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Springer: Berlin/Heidelberg, Germany, 2008; Volume 5263, pp. 374–383. [Google Scholar]
- Nappi, M.M.L.; Nappi, V.; Souza, J.C. Multi-criteria decision model for the selection and location of temporary shelters in disaster management. J. Int. Humanit. Action 2019, 4, 16. [Google Scholar] [CrossRef] [Green Version]
- Jamalul Shamsudin, N.L.; Abdul Khanan, M.F.; Umar, H.A.; Atan, S.N.; Din, A.H.M. Integrating network concept into multi criteria analysis for suggesting bus rapid transit routes. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences—ISPRS Archives; Leibniz University Hannover: Hannover, Germany, 2019; pp. 309–317. [Google Scholar]
- Song, Q.; Zilecky, P.; Jakob, M.; Hrncir, J. Exploring pareto routes in multi-criteria urban bicycle routing. In Proceedings of the 2014 17th IEEE International Conference on Intelligent Transportation Systems, Qingdao, China, 8–11 October 2014; pp. 1781–1787. [Google Scholar]
- Derek, J.; Sikora, M. Bicycle route planning using multiple criteria GIS analysis. In Proceedings of the 2019 27th International Conference on Software, Telecommunications and Computer Networks, Split, Croatia, 19–21 September 2019. [Google Scholar]
- Pahlavani, P.; Delavar, M.R. Multi-criteria route planning based on a driver’s preferences in multi-criteria route selection. Transp. Res. Part C: Emerg. Technol. 2014, 40, 14–35. [Google Scholar] [CrossRef]
- Pesce, M.; Terzi, S.; Al-Jawasreh, R.I.M.; Bommarito, C.; Calgaro, L.; Fogarin, S.; Russo, E.; Marcomini, A.; Linkov, I. Selecting sustainable alternatives for cruise ships in Venice using multi-criteria decision analysis. Sci. Total Environ. 2018, 642, 668–678. [Google Scholar] [CrossRef]
- Deveci, M.; Demirel, N.Ç.; Ahmetoğlu, E. Airline new route selection based on interval type-2 fuzzy MCDM: A case study of new route between Turkey- North American region destinations. J. Air Transp. Manag. 2017, 59, 83–99. [Google Scholar] [CrossRef]
- Hamid-Mosaku, I.A.; Oguntade, O.F.; Ifeanyi, V.I.; Balogun, A.L.; Jimoh, O.A. Evolving a comprehensive geomatics multi-criteria evaluation index model for optimal pipeline route selection. Struct. Infrastruct. Eng. 2020, 16, 1382–1396. [Google Scholar] [CrossRef]
- Balogun, A.L.; Matori, A.N.; Hamid-Mosaku, A.I.; Umar Lawal, D.; Ahmed Chandio, I. Fuzzy MCDM-based GIS model for subsea oil pipeline route optimization: An integrated approach. Mar. Georesources Geotechnol. 2017, 35, 961–969. [Google Scholar] [CrossRef]
- Hamurcu, M.; Eren, T. An application of multicriteria decision-making for the evaluation of alternative monorail routes. Mathematics 2018, 7, 16. [Google Scholar] [CrossRef] [Green Version]
- Saplıoğlu, M.; Aydın, M.M. Choosing safe and suitable bicycle routes to integrate cycling and public transport systems. J. Transp. Health 2018, 10, 236–252. [Google Scholar] [CrossRef]
- Haial, A.; Berrado, A.; Benabbou, L. Reviewing the use of multi-criteria group decision making methods for transportation problems: Case of transport mode selection problem. In Proceedings of the International Conference on Industrial Engineering and Operations Management, Pilsen, Czech Republic, 23–26 July 2019; pp. 1275–1285. [Google Scholar]
- Martin, J.C.; Román, C.; Moreira, P.; Moreno, R.; Oyarce, F. Does the access transport mode affect visitors’ satisfaction in a World Heritage City? The case of Valparaiso, Chile. J. Transp. Geogr. 2021, 91. [Google Scholar] [CrossRef]
- Pamucar, D.; Deveci, M.; Canıtez, F.; Lukovac, V. Selecting an airport ground access mode using novel fuzzy LBWA-WASPAS-H decision making model. Eng. Appl. Artif. Intell. 2020, 93. [Google Scholar] [CrossRef]
- Fonseca, F.; Ribeiro, P.; Jabbari, M.; Petrova, E.; Papageorgiou, G.; Conticelli, E.; Tondelli, S.; Ramos, R. Smart Pedestrian Network: An Integrated Conceptual Model for Improving Walkability. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Springer: Berlin/Heidelberg, Germany, 2020; Volume 318, pp. 125–142. [Google Scholar]
- Vermote, L.; Macharis, C.; Hollevoet, J.; Putman, K. Participatory evaluation of regional light rail scenarios: A Flemish case on sustainable mobility and land-use. Environ. Sci. Policy 2014, 37, 101–120. [Google Scholar] [CrossRef]
- Lee, D.J. A multi-criteria approach for prioritizing advanced public transport modes (APTM) considering urban types in Korea. Transp. Res. Part A Policy Pract. 2018, 111, 148–161. [Google Scholar] [CrossRef]
- Ghosh, A.; Ghorui, N.; Mondal, S.P.; Kumari, S.; Mondal, B.K.; Das, A.; Gupta, M.S. Application of Hexagonal Fuzzy MCDM Methodology for Site Selection of Electric Vehicle Charging Station. Mathematics 2021, 9, 393. [Google Scholar] [CrossRef]
- Bac, U.; Alaloosi, K.A.M.S.; Turhan, C. A comprehensive evaluation of the most suitable HVAC system for an industrial building by using a hybrid building energy simulation and multi criteria decision making framework. J. Build. Eng. 2021, 37. [Google Scholar] [CrossRef]
- Alhashmi, M.; Chhipi-Shrestha, G.; Ruparathna, R.; Nahiduzzaman, K.M.; Hewage, K.; Sadiq, R. Energy performance assessment framework for residential buildings in Saudi Arabia. Sustainability 2021, 13, 2232. [Google Scholar] [CrossRef]
- Mukhamet, T.; Kobeyev, S.; Nadeem, A.; Memon, S.A. Ranking PCMs for building façade applications using multi-criteria decision-making tools combined with energy simulations. Energy 2021, 215. [Google Scholar] [CrossRef]
- Moghtadernejad, S.; Chouinard, L.E.; Mirza, M.S. Design strategies using multi-criteria decision-making tools to enhance the performance of building façades. J. Build. Eng. 2020, 30. [Google Scholar] [CrossRef]
- Chen, X.; Qu, K.; Calautit, J.; Ekambaram, A.; Lu, W.; Fox, C.; Gan, G.; Riffat, S. Multi-criteria assessment approach for a residential building retrofit in Norway. Energy Build. 2020, 215. [Google Scholar] [CrossRef]
- Torabi Moghadam, S.; Lombardi, P. An interactive multi-criteria spatial decision support system for energy retrofitting of building stocks using CommuntiyVIZ to support urban energy planning. Build. Environ. 2019, 163. [Google Scholar] [CrossRef]
- Figueira, J.; Roy, B. Determining the weights of criteria in the ELECTRE type methods with a revised Simos’ procedure. Eur. J. Oper. Res. 2002, 139, 317–326. [Google Scholar] [CrossRef] [Green Version]
- Lassandro, P.; Di Turi, S. Multi-criteria and multiscale assessment of building envelope response-ability to rising heat waves. Sustain. Cities Soc. 2019, 51. [Google Scholar] [CrossRef]
- Wang, L.; Ma, G.; Zhou, F.; Liu, Y.; Tian, T. Multicriteria decision-making approach for selecting ventilation heat recovery devices based on the attributes of buildings and the preferences of decision makers. Sustain. Cities Soc. 2019, 51. [Google Scholar] [CrossRef]
- Noori, A.; Bonakdari, H.; Morovati, K.; Gharabaghi, B. Development of optimal water supply plan using integrated fuzzy Delphi and fuzzy ELECTRE III methods—Case study of the Gamasiab basin. Expert Syst. 2020, 37. [Google Scholar] [CrossRef]
- Gebre, S.L.; Cattrysse, D.; Van Orshoven, J. Multi-criteria decision-making methods to address water allocation problems: A systematic review. Water 2021, 13, 125. [Google Scholar] [CrossRef]
- Whitley, D. A genetic algorithm tutorial. Stat. Comput. 1994, 4, 65–85. [Google Scholar] [CrossRef]
- Savun-Hekimoğlu, B.; Erbay, B.; Hekimoğlu, M.; Burak, S. Evaluation of water supply alternatives for Istanbul using forecasting and multi-criteria decision making methods. J. Clean. Prod. 2021, 287. [Google Scholar] [CrossRef]
- Noori, A.; Bonakdari, H.; Salimi, A.H.; Gharabaghi, B. A group Multi-Criteria Decision-Making method for water supply choice optimization. Socio-Econ. Plan. Sci. 2021. [Google Scholar] [CrossRef]
- Singh, L.K.; Jha, M.K.; Chowdary, V.M. Planning rainwater conservation measures using geospatial and multi-criteria decision making tools. Environ. Sci. Pollut. Res. 2021, 28, 1734–1751. [Google Scholar] [CrossRef]
- Quinn, R.; Rougé, C.; Stovin, V. Quantifying the performance of dual-use rainwater harvesting systems. Water Res. X 2021, 10. [Google Scholar] [CrossRef]
- Machado, V.C.; Lafuente, J.; Baeza, J.A. Systematic comparison framework for selecting the best retrofitting alternative for an existing water resource recovery facility. Water Environ. Res. 2020, 92, 2072–2085. [Google Scholar] [CrossRef]
- Forgy, E. Cluster analysis of multivariate data: Efficiency versus interpretability of classifications. Biometrics 1965, 21, 768–769. [Google Scholar]
- Brentan, B.M.; Carpitella, S.; Izquierdo, J.; Luvizotto, E.; Meirelles, G. District metered area design through multicriteria and multiobjective optimization. Math. Methods Appl. Sci. 2021. [Google Scholar] [CrossRef]
- Ashofteh, P.S.; Golfam, P.; Loáiciga, H.A. Evaluation of River Water Transfer Alternatives with the TODIM Multi-Criteria Decision Making Method. Water Resour. Manag. 2020, 34, 4847–4863. [Google Scholar] [CrossRef]
- Narayanamoorthy, S.; Annapoorani, V.; Kalaiselvan, S.; Kang, D. Hybrid hesitant fuzzy multi-criteria decision making method: A symmetric analysis of the selection of the best water distribution system. Symmetry 2020, 12, 2096. [Google Scholar] [CrossRef]
- Cunha, M.; Marques, J.; Savić, D. A Flexible Approach for the Reinforcement of Water Networks Using Multi-Criteria Decision Analysis. Water Resour. Manag. 2020, 34, 4469–4490. [Google Scholar] [CrossRef]
- Fathi, S.; Hagen, J.S.; Haidari, A.H. Synthesizing existing frameworks to identify the potential for Managed Aquifer Recharge in a karstic and semi-arid region using GIS Multi Criteria Decision Analysis. Groundw. Sustain. Dev. 2020, 11. [Google Scholar] [CrossRef]
- Rubio-Aliaga, A.; García-Cascales, M.S.; Sánchez-Lozano, J.M.; Molina-Garcia, A. MCDM-based multidimensional approach for selection of optimal groundwater pumping systems: Design and case example. Renew. Energy 2021, 163, 213–224. [Google Scholar] [CrossRef]
- Sadr, S.M.K.; Johns, M.B.; Memon, F.A.; Duncan, A.P.; Gordon, J.; Gibson, R.; Chang, H.J.F.; Morley, M.S.; Savic, D.; Butler, D. Development and application of a multi-objective-optimization and multi-criteria-based decision support tool for selecting optimal water treatment technologies in india. Water 2020, 12, 2836. [Google Scholar] [CrossRef]
- Maleki, H.; Zahir, S. A Comprehensive Literature Review of the Rank Reversal Phenomenon in the Analytic Hierarchy Process. J. Multi-Criteria Decis. Anal. 2013, 20, 141–155. [Google Scholar] [CrossRef]
- Aires, R.F.d.F.; Ferreira, L. The rank reversal problem in multi-criteria decision making: A literature review. Pesqui. Oper. 2018, 38, 331–362. [Google Scholar] [CrossRef]
- Chi, H.-L.; Wang, X.; Jiao, Y. BIM-Enabled Structural Design: Impacts and Future Developments in Structural Modelling, Analysis and Optimisation Processes. Arch. Comput. Methods Eng. 2015, 22, 135–151. [Google Scholar] [CrossRef]
- Mardani, A.; Jusoh, A.; Md Nor, K.; Khalifah, Z.; Zakwan, N.; Valipour, A. Multiple criteria decision-making techniques and their applications—A review of the literature from 2000 to 2014. Econ. Res. Ekon. Istraživanja 2015, 28, 516–571. [Google Scholar] [CrossRef]
- Mustajoki, J.; Hämäläinen, R.P. Web-Hipre: Global Decision Support By Value Tree And AHP Analysis. Infor: Inf. Syst. Oper. Res. 2000, 38, 208–220. [Google Scholar] [CrossRef]
- Cinelli, M.; Spada, M.; Kim, W.; Zhang, Y.; Burgherr, P. MCDA Index Tool: An interactive software to develop indices and rankings. Environ. Syst. Decis. 2020. [Google Scholar] [CrossRef] [PubMed]
- Yadav, V.; Karmakar, S.; Kalbar, P.P.; Dikshit, A.K. PyTOPS: A Python based tool for TOPSIS. SoftwareX 2019, 9, 217–222. [Google Scholar] [CrossRef]
- Rezaei, J. A Concentration Ratio for Nonlinear Best Worst Method. Int. J. Inf. Technol. Decis. Mak. 2020, 19, 891–907. [Google Scholar] [CrossRef]
Abbreviation | Name | Citation | Category |
---|---|---|---|
AHP | Analytic Hierarchy Process | [6] | compensatory |
ANP | Analytic Network Process | [14] | compensatory |
CBA | Cost Benefit Analysis | [15] | compensatory |
COPRAS | Complex Proportional Assessment | [16] | compensatory |
CRITIC | Objective assessment of weights for MCDA | [17] | weights only |
ELECTRE | ELimination Et Choix Traduisant la REalité | [10] | non-compensatory outranking |
MACBETH | Measuring Attractiveness by a Categorical Based Evaluation Technique | [18] | compensatory |
MAUT | Multi-Attribute Utility Theory | [5] | multi-dimensional function |
MAVT | Multi-Attribute Value Theory | [19] | multi-dimensional function |
MOORA | Multi-Objective optimization on the basis of ratio analysis | [20] | compensatory |
PROMETHEE | Preference Ranking Organization Method for Enrichment Evaluation | [11] | non-compensatory outranking |
SAW or WAS | Simple Additive Weighting | [4] | compensatory |
Simos’ cards | Method for eliciting cardinal information on preferences from stakeholders | [21] | weights only |
TOPSIS | Technique for Order Preference by similarity to Ideal Solution | [7] | compensatory |
VIKOR | Visekriterijumsko Kompromisino Rangiranje (Serbian) | [22] | compensatory |
Rank | Keywords | Number of Articles |
---|---|---|
1 | Decision making (or decision-making) | 62 |
2 | Civil engineer | 46 |
3 | Model | 42 |
4 | Construction or (construction industry) | 36 |
5 | Optimization | 35 |
6 | System (or systems) | 32 |
7 | Selection | 32 |
8 | AHP (or Analytic Hierarchy Process) | 32 |
9 | Management | 30 |
10 | Water resources or (water management) | 30 |
11 | Sustainable development | 24 |
12 | Design | 22 |
13 | Performance | 21 |
14 | Framework | 16 |
15 | Risk Assessment | 16 |
16 | Engineering education | 14 |
17 | Uncertainty analysis | 14 |
18 | Climate change | 13 |
19 | Criteria | 13 |
20 | Environment | 12 |
21 | Human resource management | 12 |
22 | TOPSIS | 12 |
23 | GIS | 11 |
24 | Project management | 11 |
25 | Safety engineering | 11 |
Rank | Main Author | Citation Link | Method Used |
---|---|---|---|
1 | Saaty, T.L. | [6] | AHP |
2 | Zadeh, L.A. | [45] | Fuzzy sets approach—used in some MCDA implementations |
3 | Saaty, T.L. | [46] | AHP |
4 | Hwang, C. | [7] | TOPSIS |
5 | Saaty, T.L. | [47] | AHP |
6 | Jato-Espino, D. | [48] | Review of MCDA in construction industry |
7 | Belton, V. | [8] | Book—mainly on ELECTRE (an outranking method) |
8 | Buckley, J.J. | [49] | Application of Fuzzy analysis to MCDA |
9 | Chang, D.Y. | [50] | Fuzzy—AHP |
10 | Behzadian, M. | [51] | Review of applications of PROMETHEE method (an outranking method) |
Title of Journal or Proceedings (Publisher) | Number of Documents in Database |
---|---|
Journal of Civil Engineering and Management (Publisher Vilnius Gediminas Technical University) | 51 |
KSCE Journal of Civil Engineering (Korean Society of Civil Engineers) | 17 |
World Multidisciplinary Civil Engineering-Architecture-Urban Planning Symposium—WMCAUS (Conference series—Prague) | 17 |
Proceedings of the Annual Conference of the Canadian Society for Civil Engineering—(Canadian Society for Civil Engineering) | 12 |
Proceedings of the ASEE Annual Conference and Exposition (American Society for Engineering Education) | 11 |
Civil Engineering and Environmental Systems (Taylor Francis) | 10 |
Archives of Civil Engineering (De Gruyter Open) [Poland] | 8 |
Canadian Journal of Civil Engineering (Canadian Science Publishing) | 8 |
Civil Engineering Journal-TEHRAN | 8 |
CIVIL-COMP Proceedings | 5 |
Journal of Applied Water Engineering and Research (Taylor and Francis) | 5 |
Structural Engineering and Mechanics (Techno Press) | 5 |
Sustainability (MDPI) | 5 |
Search Criteria: All the Multi-Criteria Various Listed in Section 4 Plus Topics Listed below | Number of Publications Found | Number of Publications in Common with Broad Survey | % of Publications in Common with Broad Survey |
---|---|---|---|
Bridge Design | 15 | 3 | 20.0 |
Earthquake Engineering | 26 | 3 | 11.5 |
Cladding | 21 | 1 | 5.0 |
Sewage Treatment | 38 | 1 | 2.6 |
Foundation design * | 0 * | ||
Truss design | 3 | 0 | 0 |
Water Supply | 535 | 11 | 2.1 |
Building Energy | 86 | 11 | 12.8 |
Route selection | 64 | 2 | 3.1 |
Transport mode | 14 | 1 | 7.1 |
Topic | Method | No. Criteria | Notes |
---|---|---|---|
[54] | AHP, VIKOR | 5 | Included qualitative criteria. Both methods gave the same results |
[55] | PROMETHEE | 7 | Includes earthquake resistance |
[56] | AHP, VIKOR | 3 | Links MCDA with an optimization technique |
Citation | MCDA Method | Number of Criteria | Notes |
---|---|---|---|
[62] | AHP | - | Urban seismic risk map for Istanbul |
[63] | AHP | 8 | Regional risk map Erbaa, Turkey |
[64] | AHP, TOPSIS and COPRAS | - | AHP and TOPSIS gave similar rankings |
[65] | AHP (fuzzy) | - | Building performance index |
[72] | AHP and TOPSIS | 7 | Retrofitting reinforced concrete buildings for seismic resilience |
[74] | TOPSIS | 7 | Retrofitting buildings for seismic risk in Italy. Includes tax incentive refunds affecting costs |
[55] | PROMETHEE | 7 | Cost not included in the evaluation. Weights from questionnaire sent to 7 experts. |
[79] | AHP | 5 | AHP used for initial selection of criteria. |
[75] | AHP and TOPSIS | Used BIM for estimating cost and duration of works for each option. | |
[78] | AHP and TOPSIS | 3 | Design and materials of elastomeric isolators |
Citation | Method | No. Criteria | Notes |
---|---|---|---|
[81,82,83] | SAW | 4 | Noted SAW method is sensitive to number of alternatives considered. |
[85] | Factor comparison | ||
[87] | AHP and Delphi | 6 (major groups) | Interesting use of the Delphi technique to elicit information from stakeholder workshops. |
[88] | SAW | 7 | Includes standards codes in consideration of criteria |
[89] | ANP | 10 | Stakeholder questionnaires used Likert scale for responses. |
[90] | AHP and MAUT | Up to 32 | Uses questionnaire surveys of office space designers and users. |
[91] | Not specified | n.a. | Interactions of urban ecological elements with building cladding- energy and noise. |
Citation | Method | No. Criteria | Notes |
---|---|---|---|
[94] | Fuzzy AHP and TOPSIS | 10 | - |
[95] | PROMETHEE | 15 | Included odour as a criterion |
[96] | AHP based | 10 | Method adopted for use with groups |
[98] | PROMETHEE | 28 | See Table 1 in [98] for list of criteria. Large number of criteria demonstrate power of MCDA methods. |
[99] | PROMETHEE | 17 | Developed a simple generic project schema—see Figure 1 in [99] |
[100] | SAW | 18 | Wide range of stakeholder groups determining weights in a participatory approach |
[101] | Fuzzy methods based on TOPSIS | 12 | Tested sensitivity to result to removal of a weight. As might be expected, rank change can occur when some criteria with the highest weights are removed. |
[102] | TOPSIS, MOORA and CRITIC | 11 | - |
[103] | Method linked to PROMETHEE | 12 | Used method with a GIS system |
[106] | AHP and GIS | 14 | Areas suitable for irrigation using treated wastewater |
[107] | AHP | 16 | - |
[108] | AHP | 5 | Used in a GIS with a constrain to lie within a 50 m buffer of an existing sewer. |
[109] | SAW | 5 | - |
[110] | PROMETHEE, ELECTRE | 30 | The tool is also suitable for landfill site selection. |
[111] | SAW | 4 | Especially applicable in older cities with extensive combined sewer systems. |
Citation | Method | No. Criteria | Notes |
---|---|---|---|
[122] | AHP | - | Oil and gas pipeline routes. Uses Landsat imagery and GIS |
[116] | AHP | 6 | |
[118] | AHP, fuzzy | 5 | Bicycle routes (Imotske, Croatia) |
[120] | SAW with MAVT | 30 | Cruise ships in Venice (Italy). Did a sensitivity analysis of the results to changes in weights |
[124] | ANP, TOPSIS | 15 | Monorail system (Ankara, Turkey) |
[123] | AHP, Fuzzy | 7 | Subsea hydrocarbon pipelines |
[121] | TOPSIS, fuzzy | 12 | Selection of new destination city in the USA for Turkish airlines. |
Citation | Method | No. Criteria | Notes |
---|---|---|---|
[127] | TOPSIS (fuzzy) | 16 | Choice of transport mode by tourists (Valparaiso) |
[128] | SAW | 14 | Choice of transport mode to airport (Istanbul) |
[129] | Unspecified | 24 | Factors influencing choice to walk in a city (Bologna and Porto) |
[131] | AHP | 13 | Reviewed 47 possible criteria from the literature and reduced to the 13 used in their study. Interestingly, they also distinguished between values for the provider and values for the user of the service. |
[130] | AHP | 13 | Distinguished between preferences of transport provider and regional and local government stakeholders. (various cities in Belgium) |
[132] | AHP, TOPSIS and COPRAS | 13 | Choice of location for electric vehicle charging stations in Howrah (India) with sensitivity analysis. |
Citation | Method | No. Criteria | Notes |
---|---|---|---|
[133] | SAW related | 27 | HVAC selection for buildings. Additionally, gives a list of energy related articles. |
[134] | AHP | 3 | Residential buildings in Saudi Arabia. |
[135] | AHP, TOPSIS, fuzzy | 9 | Façade design. Combined energy simulation with MCDA method. |
[136] | AHP | 15 | Facade design. |
[138] | Weights from Simos’ method [139] | 11 | Uses CommunityViz spatial planning tool to involve stakeholders in urban planning. |
[137] | SAW | 7 | Took account of the different weightings from different groups of stakeholders. |
[140] | AHP | 7 | Heat waves and buildings. |
[141] | AHP | 4 | Ventilation heat recovery in buildings. |
Citation | Method | No. Criteria | Notes |
---|---|---|---|
[145] | PROMETHEE, TOPSIS | 7 | Istanbul (Turkey) water supply expansion |
[146] | ELECTRE III | 8 | Uses group decision making, applied in Iran. |
[147] | AHP | 3 | Rainwater harvesting rural options (Bengal, India) |
[148] | Visualisation | 7 | Urban rainwater harvesting and flood reduction |
[149] | SAW | 3 | Retrofitting water recovery plants |
[151] | AHP, TOPSIS with k-means clustering | 4 | Optimal design of district metering regions |
[152] | AHP | 14 | River water transfer selection in Iran |
[153] | MOORA, TOPSIS, VIKOR | 5 | Water distribution network design. |
[154] | PROMETHEE, SAW | 4 | Water distribution system upgrade. |
[155] | SAW | 9 | Finding suitable areas for aquifer recharge. This study explicitly took account of interactions between criteria. |
[156] | TOPSIS/AHP | 9 | Groundwater pumping and storage solutions |
[157] | AHP | 30 | WETSUIT tool for design of water treatment works. Applied in India, the paper has a detailed presentation and analysis of its results. (for project information see https://cordis.europa.eu/article/id/169860-water-treatment-solutions-for-india (accessed on 24 March 2021)) |
Name | Description | Availability |
---|---|---|
DEFINITE 3.1 | A toolbox of evaluation methods including Weighted summation, SMART, AHP, ELECTRE 2, Regime Method, Graphical Analysis and extensive sensitivity analysis. | (https://spinlab.vu.nl/support/tools/definite-bosda/) (accessed on 24 March 2021)[commercial but with reductions for academic institutions] |
Decision Deck (linked to Diviz) | A software workbench based on XML which helps to design, execute, and share complex, open source, MCDA/M algorithms and experiments | http://www.decision-deck.org/project/ (accessed on 24 March 2021) (looks like a good resource but needs considerable skill to use) Additionally, links to an R-based MCDA package (see below) Cf. Mayer & Bigaret (2012) |
R package for MCDA | R tools supporting the multi-criteria decision aiding process: | Free but requires knowledge of the R language. https://github.com/paterijk/MCDA (accessed on 24 March 2021) |
ChemDecide | A software package containing a decision structuring tool and three analysis tools that utilise AHP, ELECTRE III and MARE | Developed by Dr. R.E. Hodgett, University of Leeds and available through Britest Ltd. Although originally developed with Chemical storage and transport options selection in mind is quite general. |
Decisionarium | Web-based interface linking to tools for decision support (for academic use) both web-based (web-Hipre—java based AHP tool) and some software for windows. Has an online public participation facility. | http://decisionarium.tkk.fi/ (accessed on 24 March 2021) free for academic use. Note: webpages lasted updated 2013. [162] |
IDSS software | Collection of MCDM software of the Laboratory of Intelligent Decision Support Systems (University of Poznan, Poland). | http://idss.cs.put.poznan.pl/site/software.html (accessed on 24 March 2021) |
Name | Description | Availability |
---|---|---|
SuperDecisions | Implementation of the Analytic Hierarchy Process and Analytic Network Process. Supported by the Creative Decisions Foundation, started by Thomas Saaty. | Free educational software and resources, see http://www.superdecisions.com/ (accessed on 24 March 2021) |
Expert Choice | Implements the Analytic Hierarchy Process and supports group decision making. | www.expertchoice.com (accessed on 24 March 2021) |
Visual PROMETHEE | Implements the PROMETHEE method | www.promethee-gaia.net/ (accessed on 24 March 2021) academic license available |
Entscheidungsnavi (decision navigation) | A decision front end supporting the ideas, concepts, and methods of value-focused thinking and a decision back end based on Multi-Attribute Utility Theory (MAUT). | A free decision support tool, available in German and English, see https://entscheidungsnavi.de/ (accessed on 24 March 2021) |
Mcda index | Web-based index tool | [163] (https://www.mcdaindex.net/) (accessed on 24 March 2021) |
FITradeoff | A Flexible and Interactive Tradeoff elicitation procedure for multi-criteria additive models. | Commercial. (de Almeida, de Almeida et al. 2016, Frej, de Almeida et al. 2019). |
1000 Minds | Software for Multi-Criteria Decision-Making, prioritisation and resource allocation. | Commercial Uses the PAPRIKA method (Hansen and Ombler 2008). |
DEXi | A program for qualitative multi-attribute decision modelling, developed at the Jožef Stefan Institute, Ljubljana, Slovenia. | Support through a single person’s web-site. http://kt.ijs.si/MarkoBohanec/dexi.html (accessed on 24 March 2021) |
D-Sight | A visual and interactive collaborative decision-making tool for multi-criteria decision aid problems based on the PROMETHEE methods and Multi-Attribute Utility Theory. | http://www.d-sight.com/ (accessed on 24 March 2021) |
AHP-Online System | Implements the AHP method in a web browser using Java | Free, https://bpmsg.com/ahp/ahp.php (accessed on 24 March 2021) |
MACBETH | Measuring Attractiveness by a Categorical Based Evaluation TecHnique in MultiCriteria Decision Aid. | (commercial with academic pricing) http://m-macbeth.com/ (accessed on 24 March 2021) |
IRIS and VIP | IRIS—Interactive Robustness analysis and parameters’ Inference software for multi-criteria Sorting problems using ELECTRE Tri and VIP—Variable Interdependent Parameters Analysis software | https://www.uc.pt/en/feuc/ldias/software (accessed on 24 March 2021) |
Transparent Choice | AHP based decision software | (https://www.transparentchoice.com/ahp-software) (accessed on 24 March 2021) [formerly called MakeItRational] |
Decision-Radar | A Python based TOPSIS and ELECTRE tool | (Statistical Design Institute, 2016), see (https://decision-radar.com/) and [164] (accessed on 24 March 2021) |
ELECTRE I, II, III, IV and Tri | Software implementing the various ELECTRE group of methods. | https://sourceforge.net/projects/j-electre/files/ https://github.com/Valdecy/J-Electre, also https://www.lamsade.dauphine.fr/~mayag/links.html ( all three accessed 24 March 2021) |
Best Worst Method | A structured pairwise comparison system that involves comparisons with best and worst options. | https://bestworstmethod.com/ [165] (accessed on 24 March 2021) |
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Bruen, M. Uptake and Dissemination of Multi-Criteria Decision Support Methods in Civil Engineering—Lessons from the Literature. Appl. Sci. 2021, 11, 2940. https://doi.org/10.3390/app11072940
Bruen M. Uptake and Dissemination of Multi-Criteria Decision Support Methods in Civil Engineering—Lessons from the Literature. Applied Sciences. 2021; 11(7):2940. https://doi.org/10.3390/app11072940
Chicago/Turabian StyleBruen, Michael. 2021. "Uptake and Dissemination of Multi-Criteria Decision Support Methods in Civil Engineering—Lessons from the Literature" Applied Sciences 11, no. 7: 2940. https://doi.org/10.3390/app11072940
APA StyleBruen, M. (2021). Uptake and Dissemination of Multi-Criteria Decision Support Methods in Civil Engineering—Lessons from the Literature. Applied Sciences, 11(7), 2940. https://doi.org/10.3390/app11072940