Abstract
This study aims to compare the impact of Construction 4.0 technologies on different organizational core values, focusing on sustainability and resiliency, well-being, productivity, safety, and integrity. To achieve that aim, the study objectives are the following: (i) identify the critical Construction 4.0 technologies between core values; (ii) appraise overlapping critical Construction 4.0 technologies between core values; (iii) examine the ranking performance of Construction 4.0 technologies between core values; and (iv) analyze the interrelationships between Construction 4.0 technologies and core values. First, twelve Construction 4.0 technologies were identified from a national strategic plan. Then, the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) that incorporates subjective and objective weights was used to evaluate the impact of the Construction 4.0 technologies on the five core values. Finally, the collected data was analyzed using the following techniques: fuzzy TOPSIS, normalization, overlap analysis, agreement analysis, sensitivity analysis, ranking comparison, and Spearman correlation. The study findings reveal four critical Construction 4.0 technologies that enhance all five core values: building information modeling (BIM), Internet of Things (IoT), big data and predictive analytics, and autonomous construction. Also, there is a high agreement on the Construction 4.0 technologies that enhance well-being and productivity. Lastly, artificial intelligence (AI) has the highest number of very strong relationships among the core values. The originality of this paper lies in its comprehensive comparison of the impact of Construction 4.0 technologies on multiple organizational core values. The study findings provide valuable insights in making strategic decisions in adopting Construction 4.0 technologies.
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Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions (e.g., anonymized data).
References
Abdullah AG, Shafii MA, Pramuditya S, Setiadipura T, Anzhar K (2023) Multi-criteria decision making for nuclear power plant selection using fuzzy AHP: evidence from Indonesia. Energy and AI 14(100263):1–13. https://doi.org/10.1016/j.egyai.2023.100263
Ajayi A, Oyedele L, Davila Delgado JM, Akanbi L, Bilal M, Akinade O, Olawale O (2019) Big data platform for health and safety accident prediction. World J Sci Technol Sustain Dev 16(1):2–21. https://doi.org/10.1108/wjstsd-05-2018-0042
Al Mohamed AA, Al Mohamed S (2023) Application of fuzzy group decision-making selecting green supplier: a case study of the manufacture of natural laurel soap. Future Bus J 9(35):1–20. https://doi.org/10.1186/s43093-023-00212-5
Al Mohamed AA, Jeblak A (2021) Use of fuzzy time series to predict the numbers of students enrolled in the Private University of Ebla ( case study at the Faculty of Engineering in Aleppo ). J Sci Comput Eng Res (JSCER) 2(4):184–189. https://doi.org/10.46379/jscer.2021.020401
Al Mohamed AA, Al Mohamed S, Zino M (2023) Application of fuzzy multicriteria decision-making model in selecting pandemic hospital site. Future Bus J 9(14):1–22. https://doi.org/10.1186/s43093-023-00185-5
Al-Mohammad MS, Haron AT, Esa M, Aloko MN, Alhammadi Y, Anandh KS, Rahman RA (2022) Factors affecting BIM implementation : evidence from countries with different income levels. Constr Innov. https://doi.org/10.1108/CI-11-2021-0217
Aslan Ö, Altan A, Hacıo˘glu R (2022) Level control of blast furnace gas cleaning tank system with fuzzy based gain regulation for model reference adaptive controller. Processes 10(2503):1–23
Aslan Ö, Altan A, Hacıoğlu R (2017) The control of blast furnace top gas pressure by using fuzzy PID. Proceedings of the fifth international conference on advances in mechanical and robotics Engineering–AMRE, 22–26. https://doi.org/10.15224/978-1-63248-123-8-18
Awolusi I, Marks E, Hallowell M (2018) Automation in construction wearable technology for personalized construction safety monitoring and trending : review of applicable devices. Autom Constr 85:96–106. https://doi.org/10.1016/j.autcon.2017.10.010
Badger L, Grance T, Patt-Corner R, Voas J (2012) Draft cloud computing synopsis and recommendations: recommendations of the National Institute of Standards and Technology. NIST Spec Publ 800:146
Badraddin AK, Radzi AR, Almutairi S, Rahman RA (2022) Critical success factors for concrete recycling in construction projects. Sustainability 14:3102
Basílio MP, Pereira V, Costa HG, Santos M, Ghosh A (2022) A systematic review of the applications of multi-criteria decision aid methods (1977–2022). Electronics 11(11):1720. https://doi.org/10.3390/electronics11111720
Bello SA, Oyedele LO, Akinade OO, Bilal M, Davila Delgado JM, Akanbi LA, Ajayi AO, Owolabi HA (2021) Cloud computing in construction industry: use cases, benefits and challenges. Autom Constr 122:103441. https://doi.org/10.1016/j.autcon.2020.103441
Bouraima MB, Qiu Y, Stević Ž, Marinković D, Deveci M (2023) Integrated intelligent decision support model for ranking regional transport infrastructure programmes based on performance assessment. Expert Syst Appl 222:119852. https://doi.org/10.1016/j.eswa.2023.119852
CalışDuman M, Akdemir B (2021) A study to determine the effects of industry 4.0 technology components on organizational performance. Technol Forecast Soc Chang 167:120615. https://doi.org/10.1016/j.techfore.2021.120615
Calvetti D, Mêda P, Gonçalves MC, Sousa H (2020) Worker 4.0: the future of sensored construction sites. Buildings 10(169):1–22. https://doi.org/10.3390/BUILDINGS10100169
Chang V, Wang Z, Xu Q, Golightly L, Liu B, Arami M (2021) Smart home based on internet of things and ethical issues. Proceedings of the 3rd international conference on finance, economics, management and IT business (FEMIB), 57–64. https://doi.org/10.5220/0010178100570064
Chen CT (2000) Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst 114(1):1–9. https://doi.org/10.1016/j.asoc.2014.10.013
Chou C (2018) International Journal of Industrial Ergonomics Application of ANP to the selection of shipping registry : the case of Taiwanese maritime industry. Int J Ind Ergon 67:89–97. https://doi.org/10.1016/j.ergon.2018.04.009
Chowdhury P, Paul SK (2020) Applications of MCDM methods in research on corporate sustainability. Manag Environ Qual: Int J 31(2):385–405. https://doi.org/10.1108/MEQ-12-2019-0284
CIDB. (2020) Construction 4.0 Strategic Plan (2021-2025): Next Revolution of the Malaysian Construction Industry, Kuala Lumpur, Malaysia. https://www.cream.my/construction-4-0-strategic-plan-2021-2025
Cooke R, Goossens LHJ (1999) Procedures guide for structured expert judgment. In Project Report to the European Commission, EUR 18820, Luxembourg/Brussels. https://filelist.tudelft.nl/EWI/Overdefaculteit/Afdelingen/AppliedMathematics/AppliedProbability/Risk/Download/eur18820.pdf
Damaševiˇ R, Bacanin N, Misra S (2023) From sensors to safety : Internet of Emergency Services ( IoES ) for emergency response and disaster management. J Sens Actuator Netw 12(41):1–45
Delgado JMD, Oyedele L, Demian P, Beach T (2020) A research agenda for augmented and virtual reality in architecture, engineering and construction. Adv Eng Inform 45:101122. https://doi.org/10.1016/j.aei.2020.101122
Demirkesen S, Tezel A (2022) Investigating major challenges for industry 4.0 adoption among construction companies. Eng Constr Archit Manag 29(3):1470–1503. https://doi.org/10.1108/ECAM-12-2020-1059
Deng H, Yeh CH, Willis RJ (2000) Inter-company comparison using modified TOPSIS with objective weights. Comput Oper Res 27:963–973. https://doi.org/10.1016/S0305-0548(99)00069-6
Deveci M, Gokasar I, Pamucar D, Chen Y, Coffman DM (2023a) Sustainable E-scooter parking operation in urban areas using fuzzy Dombi based RAFSI model. Sustain Cities Soc 91:104426. https://doi.org/10.1016/j.scs.2023.104426
Deveci M, Varouchakis EA, Brito-Parada PR, Mishra AR, Rani P, Bolgkoranou M, Galetakis M (2023b) Evaluation of risks impeding sustainable mining using Fermatean fuzzy score function based SWARA method. Appl Soft Comput 139:110220. https://doi.org/10.1016/j.asoc.2023.110220
Dobrucali E, Demirkesen S, Sadikoglu E, Zhang C, Damci A (2022) Investigating the impact of emerging technologies on construction safety performance. Eng Constr Archit Manag. https://doi.org/10.1108/ECAM-07-2022-0668
Du Plessis D and Simpson A (2021) The mental health and wellbeing of small and medium-sized construction firms in New Zealand. BRANZ Study Report SR459. Judgeford, New Zealand: BRANZ Ltd.
Duckworth J, Hasan A, Kamardeen I (2022) Mental health challenges of manual and trade workers in the construction industry : a systematic review of causes, effects and interventions. Eng Constr Archit Manag. https://doi.org/10.1108/ECAM-11-2021-1022
Elias A, Ben J, Hiruy K (2023) Re-imagining anti-racism as a core organisational value. Aust J Manag. https://doi.org/10.1177/03128962231151579
Fagbenro RK, Sunindijo RY, Illankoon C, Frimpong S (2023) Influence of prefabricated construction on the mental health of workers: systematic review. Eur J Investig Health Psychol Educ 13:345–363. https://doi.org/10.3390/ejihpe13020026
Feldmann FG (2022) Towards Lean Automation in Construction—Exploring Barriers to Implementing Automation in Prefabrication. Sustain 14(12944):1–22. https://doi.org/10.3390/su141912944
Feng F, Xu Z, Fujita H, Liang M (2020) Enhancing PROMETHEE method with intuitionistic fuzzy soft sets. Int J Intell Syst 35(7):1071–1104. https://doi.org/10.1002/int.22235
Ganah AA, John GA (2017) BIM and project planning integration for on-site safety induction. J Eng Des Technol 15(3):341–354. https://doi.org/10.1108/JEDT-02-2016-0012
García de Soto B, Agustí-Juan I, Joss S, Hunhevicz J (2022) Implications of Construction 4.0 to the workforce and organizational structures. Int J Constr Manag 22(2):205–217. https://doi.org/10.1080/15623599.2019.1616414
Glass J, Bygballe LE, Hall D (2022) Transforming construction: the multi-scale challenges of changing and innovating in construction. Constr Manag Econ 40(11–12):855–864. https://doi.org/10.1080/01446193.2022.2141432
Gurgun AP, Koc K, Kunkcu H (2022) Exploring the adoption of technology against delays in construction projects. Eng Constr Archit Manag. https://doi.org/10.1108/ECAM-06-2022-0566
Hajj CE, Montes GM, Jawad D (2021) Analysis of BIM functionalities diffusion in the construction industry : the case of the MENA region. Eng Constr Archit Manag 30(2):415–435. https://doi.org/10.1108/ECAM-03-2021-0269
Hao J, Chen Z, Zhang Z, Loehlein G (2021) Quantifying construction waste reduction through the application of prefabrication: a case study in Anhui, China. Environ Sci Pollut Res 28(19):24499–24510. https://doi.org/10.1007/s11356-020-09026-2
Jud D, Kerscher S, Wermelinger M, Jelavic E, Egli P, Leemann P, Hottiger G, Hutter M (2021) HEAP - the autonomous walking excavator. Autom Constr 129:103783. https://doi.org/10.1016/j.autcon.2021.103783
Kacprzak D (2021) A novel extension of the technique for order preference by similarity to ideal solution method with objective criteria weights for group decision making with interval numbers. Entropy 23(1460):1–20. https://doi.org/10.3390/e23111460
Khan IS, Ahmad MO, Majava J (2023) Industry 4.0 innovations and their implications: an evaluation from sustainable development perspective. J Clean Prod 405:137006. https://doi.org/10.1016/j.jclepro.2023.137006
King SS, Rahman RA, Fauzi MA, Haron AT (2022) Critical analysis of pandemic impact on AEC organizations: the COVID-19 case. J Eng Des Technol 20(1):358–383. https://doi.org/10.1108/JEDT-04-2021-0225
Kor M, Yitmen I, Alizadehsalehi S (2023) An investigation for integration of deep learning and digital twins towards Construction 4.0. Smart Sustain Built Environ 12(3):461–487. https://doi.org/10.1108/SASBE-08-2021-0148
Kumar P, Brar PS, Singh D, Bhamu J (2021) Fuzzy AHP approach for barriers to implement LSS in the context of. Int J Product Perform Manag. https://doi.org/10.1108/IJPPM-12-2021-0715
Lai H, Xu W (2019) Statistical properties of average Kendall ’ s Tau under multivariate contaminated Gaussian model. IEEE Access 7:159177–159189. https://doi.org/10.1109/ACCESS.2019.2950566
Lazar N, Chithra K (2021) Evaluation of sustainability criteria for residential buildings of tropical climate: the stakeholder perspective. Energy Build 232:110654. https://doi.org/10.1016/j.enbuild.2020.110654
Lee ZP, Rahman RA, Doh SI (2021) Critical success factors for implementing design-build: analysing Malaysian public projects. J Eng Des Technol 20(5):1041–1056. https://doi.org/10.1108/JEDT-08-2020-0321
Lokuge S, Sedera D, Grover V, Xu D (2019) Organizational readiness for digital innovation: development and empirical calibration of a construct. Inf Manag 56:445–461
Magalhaes RM, Mello LCBdeB, Hippert MAS (2023) Organizational readiness for building information modeling. Front Eng Built Environ 3(2):137–152. https://doi.org/10.1108/FEBE-07-2022-0028
Mahpour A (2018) Prioritizing barriers to adopt circular economy in construction and demolition waste management. Resour Conserv Recycl 134:216–227. https://doi.org/10.1016/j.resconrec.2018.01.026
Melenbrink N, Werfel J, Menges A (2020) On-site autonomous construction robots: Towards unsupervised building. Autom Constr 119:103312. https://doi.org/10.1016/j.autcon.2020.103312
Mahamadu A-M, Manu P, Aigbavboa C, Abanda FH (2020) The importance of BIM capability assessment: an evaluation of post-selection performance of organisations on construction projects. Eng Constr Archit Manag 27(1):24–48. https://doi.org/10.1108/ECAM-09-2018-0357
Malomane R, Musonda I, Okoro CS (2022) The Opportunities and Challenges Associated with the Implementation of Fourth Industrial Revolution Technologies to Manage Health and Safety. Int J Environ Res Public Health 19(846):1–22. https://doi.org/10.3390/ijerph19020846
Maqbool R, Saiba MR, Ashfaq S (2022) Emerging industry 4.0 and Internet of Things (IoT) technologies in the Ghanaian construction industry: sustainability, implementation challenges, and benefits. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-022-24764-1
Melo-Acosta GE, Duitama-Munoz F, Arias-Londono JD (2017) Fraud detection in big data using supervised and semi-supervised learning techniques. IEEE Colombian Conf Commun Comput. https://doi.org/10.1109/ColComCon.2017.8088206
Miranda SLC, Del Rey Castillo E, Gonzalez V, Adafin J (2022) Predictive analytics for early-stage construction costs estimation. Buildings 12(7):1–21. https://doi.org/10.3390/buildings12071043
Musarat MA, Alaloul WS, Liew MS (2022) Inflation rate and labours ’ wages in construction projects : economic relation investigation. Eng Constr Archit Manag 29(6):2461–2494. https://doi.org/10.1108/ECAM-07-2020-0478
Musarat MA, Alaloul WS, Hameed N, Dhinaharan R, Qureshi AH, Wahab MMA (2023) Efficient construction waste management: a solution through Industrial Revolution (IR) 4.0 evaluated by AHP. Sustainability (Switzerland) 15(1):1–16. https://doi.org/10.3390/su15010274
Nagy O, Papp I, Szabó RZ (2021) Construction 4.0 organisational level challenges and solutions. Sustainability (Switzerland) 13(21):1–18. https://doi.org/10.3390/su132112321
Oke AE, Arowoiya VA (2021) Evaluation of Internet of Things ( IoT ) application areas for sustainable construction. Smart Sustain Built Environ 10(3):387–402. https://doi.org/10.1108/SASBE-11-2020-0167
Oke AE, Kineber AF, Al-Bukhari I, Famakin I, Kingsley C (2021) Exploring the benefits of cloud computing for sustainable construction in Nigeria. J Eng Des Technol 21(4):973–990. https://doi.org/10.1108/JEDT-04-2021-0189
Oke AE, Arowoiya VA, Temitope O (2022) Influence of the Internet of Things ’ application on construction project performance performance. Int J Constr Manag 22(13):2517–2527. https://doi.org/10.1080/15623599.2020.1807731
Okedara K, Ejohwomu O, Chan P (2020) Ethics and stakeholder engagement for Industry/Construction 4.0: a systematic review. Constr Ind Fourth Ind Revolution. https://doi.org/10.1007/978-3-030-26528-1. (Springer International Publishing)
Olatunde NA, Gento AM, Okorie VN, Oyewo OW, Mewomo MC, Awodele IA (2023) Construction 4.0 technologies in a developing economy: awareness, adoption readiness and challenges. Front Eng Built Environ 3(2):108–121. https://doi.org/10.1108/febe-08-2022-0037
Onososen AO, Musonda I (2022) Research focus for construction robotics and human-robot teams towards resilience in construction: scientometric review. J Eng Des Technol 21(2):502–526. https://doi.org/10.1108/JEDT-10-2021-0590
Osunsanmi TO, Aigbavboa CO, Emmanuel Oke A, Liphadzi M (2020) Appraisal of stakeholders’ willingness to adopt construction 4.0 technologies for construction projects. Built Environ Proj Asset Manag 10(4):547–565. https://doi.org/10.1108/BEPAM-12-2018-0159
Osunsanmi TO, Aigbavboa CO, Thwala WDD, Molusiwa R (2022) Modelling construction 4.0 as a vaccine for ensuring construction supply chain resilience amid COVID-19 pandemic. J Eng Des Technol 20(1):132–158. https://doi.org/10.1108/JEDT-07-2021-0384
Oubahman L, Duleba S (2021) Review of PROMETHEE method in transportation. Prod Eng Arch 27(1):69–74. https://doi.org/10.30657/pea.2021.27.9
Pamidimukkala A, Kermanshachi S (2021) Impact of COVID-19 on field and office workforce in construction industry. Proj Leadersh Soc 2:100018. https://doi.org/10.1016/j.plas.2021.100018
Papathanasiou J (2021) An example on the use and limitations of MCDA: the case of fuzzy VIKOR. Examples Counterexamples 1:100001. https://doi.org/10.1016/j.exco.2020.100001
Qahtan S, Alsattar HA, Zaidan AA, Deveci M, Pamucar D, Delen D (2023) Performance assessment of sustainable transportation in the shipping industry using a q-rung orthopair fuzzy rough sets-based decision making methodology. Expert Syst Appl 223:119958. https://doi.org/10.1016/j.eswa.2023.119958
Queiroz MM, FossoWamba S, ChiappettaJabbour CJ, de Sousa Lopes, Jabbour AB, Machado MC (2022) Adoption of Industry 4.0 technologies by organizations: a maturity levels perspective. Ann Oper Res. https://doi.org/10.1007/s10479-022-05006-6
Rani HA, Radzi AR, Alias AR, Almutairi S, Rahman RA (2022) Factors affecting workplace well-being: building construction projects. Buildings 12(910):1–18. https://doi.org/10.3390/buildings12070910
Ren D, Choi JK, Schneider K (2022) A multicriteria decision-making method for additive manufacturing process selection. Rapid Prototyp J 28(11):77–91. https://doi.org/10.1108/RPJ-11-2021-0302
Veras PR, Suresh S, Renukappa S (2018) The adoption of big data concepts for sustainable practices implementation in the construction industry. 2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion), 349–352. https://doi.org/10.1109/UCC-Companion.2018.00079
João Ribeirinho M, Jan M, Gernot S, Erik S, Jose Luis B, Rob P, Jonas B, David R, Andersson T (2020) The next Normal in Construction. How disruption is reshaping the world’s largest ecosystem. In McKinsey & Company. https://www.mckinsey.com/business-functions/operations/our-insights/the-next-normal-inconstruction-how-disruption-is-reshaping-the-worlds-largest-ecosystem
Ribeiro DB, Coutinho AR, Satyro WC, Campos FC, Lima CRC, Contador JC, Gonçalves RF (2022) The DAWN readiness model to assess the level of use of Industry 4.0 technologies in the construction industry in Brazil. Constr Innov. https://doi.org/10.1108/CI-05-2022-0114
Rodrigues F, Antunes F, Matos R (2021) Safety plugins for risks prevention through design resourcing BIM. Constr Innov 21(2):244–258. https://doi.org/10.1108/CI-12-2019-0147
Roy V, Desjardins D, Ouellet-Plamondon C, Fertel C (2021) Reflection on Integrity Management While Engaging with Third Parties in the Construction and Civil Engineering Industry. J Leg Aff Dispute Resolut Eng Constr 13(1):03720005. https://doi.org/10.1061/(asce)la.1943-4170.0000435
Sadeghi M, Mahmoudi A, Deng X (2022) Adopting distributed ledger technology for the sustainable construction industry: evaluating the barriers using ordinal priority approach. Environ Sci Pollut Res 29(7):10495–10520. https://doi.org/10.1007/s11356-021-16376-y
Saha A, Simic V, Dabic-Miletic S, Senapati T, Yager RR, Deveci M (2023) Evaluation of propulsion technologies for sustainable road freight distribution using a dual probabilistic linguistic group decision-making approach. IEEE Tran Eng Manag. https://doi.org/10.1109/TEM.2023.3253300
Sarker MNI, Yang B, Lv Y, Huq ME, Kamruzzaman MM (2020) Climate change adaptation and resilience through big data. Int J Adv Comput Sci Appl 11(3):533–539. https://doi.org/10.14569/ijacsa.2020.0110368
Sen DK, Datta S, Patel SK, Mahapatra SS (2015) Multi-criteria decision making towards selection of industrial robot exploration of PROMETHEE II method. Benchmark: Int J 22(3):465–487. https://doi.org/10.1108/BIJ-05-2014-0046
Sertyesilisik B (2017) Building information modeling as a tool for enhancing disaster resilience of the construction industry. Trans VSB - Tech Univ Ostrava Saf Eng Ser 12:9–18. https://doi.org/10.1515/tvsbses-2017-0002
Shafei H, Radzi AR, Algahtany M, Rahman RA (2022) Construction 4.0 technologies and decision-making: a systematic review and gap analysis. Buildings 12(2206):1–19. https://doi.org/10.3390/buildings12122206
Shamsuzzoha A, Piya S, Shamsuzzaman M (2021) Application of fuzzy TOPSIS framework for selecting complex project in a case company. J Glob Oper Strateg Sourcing 14(3):528–566. https://doi.org/10.1108/JGOSS-07-2020-0040
Shannon CE, Weaver W (1949) The Theory of Mathematical Communication. International Business, 131
Singh A, Misra SC, Kumar V, Kumar U (2022a) Identification and ordering of safety performance indicators using fuzzy TOPSIS: a case study in Indian construction company. Int J Qual Reliab Manag 39(1):77–114. https://doi.org/10.1108/IJQRM-02-2020-0051
Singh R, Sharma P, Foropon C, Belal HM (2022b) The role of big data and predictive analytics in the employee retention: a resource-based view. In Int J Manpow 43(2):411–447. https://doi.org/10.1108/IJM-03-2021-0197
Tam NV, Toan NQ, Phong VV, Durdyev S (2021) Impact of BIM-related factors affecting construction project performance. Int J Build Pathol Adapt 41(2):454–475. https://doi.org/10.1108/IJBPA-05-2021-0068
Tanko BL, Zakka WP, Heng WN (2022) BIM in the Malaysian construction industry : a scientometric review and case study industry. Eng Constr Archit Manag. https://doi.org/10.1108/ECAM-04-2021-0324
Tao X, Liu Y, Wong PKY, Chen K, Das M, Cheng JCP (2022) Confidentiality-minded framework for blockchain-based BIM design collaboration. Autom Constr 136:104172. https://doi.org/10.1016/j.autcon.2022.104172
Tijani B, Xiaohua J, Osei-Kyei R (2021) Critical analysis of mental health research among construction project professionals. J Eng Des Technol 19(2):467–496. https://doi.org/10.1108/JEDT-04-2020-0119
Toyin JO, Mewomo MC (2022) Critical Review of the Impacts of Successful BIM Technology Application on Construction Projects. Construction in 5D: Deconstruction, Digitalization, Disruption, Disaster, Development: Proceedings of the 15th Built Environment Conference 65–77. https://doi.org/10.1007/978-3-030-97748-1
Turner CJ, Oyekan J, Stergioulas L, Griffin D (2021) Utilizing Industry 4.0 on the Construction site: challenges and opportunities. IEEE Trans Ind Inform 17(2):746–756. https://doi.org/10.1109/TII.2020.3002197
Uzun B, Taiwo M, Syidanova A, Uzun Ozsahin D (2021) The technique for order of preference by similarity to ideal solution (TOPSIS). In Application of multi-criteria decision analysis in environmental and civil engineering 25–30. https://doi.org/10.1007/978-3-030-64765-0
Wang T-C, Lee H-D (2009) Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert Syst Appl 36:8980–8985
Wang Y, Ren J, Zhang L, Liu D (2023) Research on resilience evaluation of green building supply chain based on ANP-fuzzy model. Sustainability 15(285):1–21
Wen Z, Liao H, Kazimieras Zavadskas E, Al-Barakati A (2019) Selection third-party logistics service providers in supply chain finance by a hesitant fuzzy linguistic combined compromise solution method. Econ Res-Ekonomska Istrazivanja 32(1):4033–4058. https://doi.org/10.1080/1331677X.2019.1678502
Winge S, Albrechtsen E, Mostue BA (2019) Causal factors and connections in construction accidents. Saf Sci 112:130–141. https://doi.org/10.1016/j.ssci.2018.10.015
Xu X, de Soto BG (2020) On-site autonomous construction robots: a review of research areas, technologies, and suggestions for advancement. Proc 37th Int Symp Autom Robot Constr ISARC 385–392. https://doi.org/10.22260/isarc2020/0055
Yang M, Wu C, Guo Y, Jiang R, Zhou F, Zhang J, Yang Z (2023) Transformer-based deep learning model and video dataset for unsafe action identification in construction projects. Autom Constr 146:104703. https://doi.org/10.1016/j.autcon.2022.104703
Yap JBH, Lee KPH, Wang C (2023) Safety enablers using emerging technologies in construction projects: empirical study in Malaysia. J Eng Des Technol 21(5):1414–1440. https://doi.org/10.1108/JEDT-07-2021-0379
Yoo M, Ham N (2020) Productivity analysis of documentation based on 3D model in plant facility construction project. Appl Sci (Switzerland) 10(1126):1–15. https://doi.org/10.3390/app10031126
Zhang X (2005) Critical success factors for public – private partnerships in infrastructure development. J Constr Eng Manag 131(1):3–14
Zhao S, Wang Q, Fang X, Liang W, Cao Y, Zhao C, … Wang K (2022) Application and Development of Autonomous Robots in Concrete Construction: Challenges and Opportunities. Drones 6(424):1–26. https://doi.org/10.3390/drones6120424
Zimonjić S, Đekić M, Kastratović E (2018) Application of Vikor method in ranking the investment projects. J Econ Law 8:125–134
Zoraghi N, Amiri M, Talebi G, Zowghi M (2013) A fuzzy MCDM model with objective and subjective weights for evaluating service quality in hotel industries. J Ind Eng Int 9(38):1–13. https://doi.org/10.1186/2251-712X-9-38
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This study was funded by the Universiti Malaysia Pahang Al-Sultan Abdullah (PGRS220342).
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by HS, RAR, and YSL. The first draft of the manuscript was written by HS, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Shafei, H., Rahman, R. . & Lee, Y.S. Construction 4.0 technology evaluation using fuzzy TOPSIS: comparison between sustainability and resiliency, well-being, productivity, safety, and integrity. Environ Sci Pollut Res 31, 14858–14893 (2024). https://doi.org/10.1007/s11356-024-31862-9
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DOI: https://doi.org/10.1007/s11356-024-31862-9