Contribution of artificial intelligence (ai) to construction project management processes: State of the art with scoping review method

Yıl 2024, Cilt: 42 Sayı: 5, 1654 - 1669, 04.10.2024

Hande Aladağ , İlkim Güven , Osman Balli

Öz

The Artificial Intelligence (AI) is being considered as a new way to tackle the challenges frequently faced by the Architecture, Engineering and Construction (AEC) industry in terms of its ability to leverage advanced technologies, data analysis techniques, and automation to address specific challenges and improve efficiency, productivity, safety, decision-making, and overall project outcomes. Thus, it is imperative to know the future of AI in construction project management processes. With this background, this study aims to detect application areas of AI in construction project management processes by using scoping review method and create a base for the development of theories that can support future studies. According to the findings of studies, application areas of AI in project management domain are generally clustered under eight main topics: cost, time, quality, contract, dispute, risk, safety, and sustainability. In line with the findings, this study contributes in two-folds. As the theoretical contribution researchers can benefit from this study, which addresses the research trends and current applications of this ground-breaking technology in project management processes, by mapping the current interest in AI studies. The future studies can be directed in line with identified gaps and justifications. As the practical contributions, construction companies that want to be the early movers can benefit from the findings of this study as they reveal crucial areas where they can focus their investment on areas where AI can make the most significant difference to address their company’s specific requirements.

Anahtar Kelimeler

Artificial Intelligence (AI), Construction Industry, Construction Management, Project Management, Scoping Review

Kaynakça

• REFERENCES
• [1] Craveiro F, Duarte JP, Bartolo H, Bartolo PJ. Additive manufacturing as an enabling technology for digital construction: A perspective on Construction 4.0. Sustain Dev. 2019;4:6. [CrossRef]
• [2] Wang X, Zhang Y, Tao Y. Research on system construction and application of Enterprise intelligent finance from the perspective of Artificial intelligence. Proceedings - 2021 International Conference on Computer Information Science and Artificial Intelligence 2021;1:597602. [CrossRef]
• [3] Beier G, Ullrich A, Niehoff S, Reißig M, Habich M. Industry 4.0: How it is defined from a sociotechnical perspective and how much sustainability it includes–A literature review. J Clean Prod 2020;259:120856. [CrossRef]
• [4] Akinosho TD, Oyedele LO, Bilal M, Ajayi AO, Davila M, Akinade OO, Ahmed AA. Deep learning in the construction industry: A review of present status and future innovations. J Build Eng 2020;32:101827. [CrossRef]
• [5] Jiang Z. Land Resource Management Information Platform Based on Artificial Intelligence Technology. 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering 2021;735739. [CrossRef]
• [6] Odeh M. The role of artificial intelligence in project management. IEEE Eng Manag Rev 2023;99:14. [CrossRef]
• [7] Xiao C, Liu Y, Akhnoukh A. Bibliometric review of artificial intelligence (AI) in construction engineering and management. In: International Conference on Construction and Real Estate Management. 2018. p. 3241. [CrossRef]
• [8] Hatami M, Franz B, Paneru S, Flood I. Using Deep Learning Artificial Intelligence to Improve Foresight Method in the Optimization of Planning and Scheduling of Construction Processes. ASCE International Conference on Computing in Civil Engineering 2021. p. 811818. [CrossRef]
• [9] Pan Y, Zhang L. Roles of artificial intelligence in construction engineering and management: A critical review and future trends. Autom Constr 2021;122:103517. [CrossRef]
• [10] Wang K, Guo F, Zhang C, Hao J. Digital Technology in Architecture, Engineering, and Construction (AEC) Industry: Research Trends and Practical Status toward Construction 4.0. March 2022. [CrossRef]
• [11] Ngo J, Hwang B. critical project management knowledge and skills for managing projects with smart technologies. J Manag Eng 2022;38. [CrossRef]
• [12] Xu H, Chang R, Pan M, Li H, Liu S, Webber RJ, Zuo J, Dong N. application of artificial neural networks in construction management: A scientometric review. Buildings 2022;12. [CrossRef]
• [13] Wang XL. Application of artificial intelligence in oil and gas industry. Mod Inf Technol 2017;3:117119.
• [14] Shi T, Wu J. Application of Artificial Intelligence in Water Conservancy Project Management. Proceedings - 2021 2nd International Conference on Big Data and Artificial Intelligence and Software Engineering 2021;556559. [CrossRef]
• [15] Bingöl K, Akan AE, Örmecioglu HT, Er A. Artificial intelligence applications in earthquake resistant architectural design: Determination of irregular structural systems with deep learning and ImageAI method. J Fac Eng Archit Gazi Univ 2020;35:21972209. [CrossRef]
• [16] Lu P, Chen S, Zheng Y. Artificial Intelligence in Civil Engineering. Math Probl Eng 2012;122. [CrossRef]
• [17] Karan E, Asgari S, Mohammadpour A. Applying artificial intelligence within the AEC industry: Collecting and interpreting data. Constr Res Congr 2020;7:809818.
• [18] Abioye SO, Oyedele LO, Akanbi L, Ajayi A, Delgado JM, Bilal M, Ahmed AA. Artificial intelligence in the construction industry: A review of present status, opportunities, and future challenges. J Build Eng 2021;44:103299. [CrossRef]
• [19] Auth G, Jöhnk J, Wiecha DA. A Conceptual Framework for Applying Artificial Intelligence in Project Management. In: 2021 IEEE 23rd Conference on Business Informatics (CBI). 2021;1:161170. [CrossRef]
• [20] Regona M, Yigitcanlar T, Xia B, Li RYM. Opportunities and adoption challenges of AI in the construction industry: a PRISMA review. J Open Innov Technol Mark Complex 2022;8:45. [CrossRef]
• [21] Fulford R, Standing C. Construction industry productivity and the potential for collaborative practice. Int J Proj Manag 2014;32:315326. [CrossRef]
• [22] Kabirifar K, Mojtahedi M. The impact of engineering, procurement and construction (EPC) phases on project performance: A case of large-scale residential construction project. Buildings 2019;9:15. [CrossRef]
• [23] Okudan O, Çevikbaş M, Işık Z. An exploratory study on the critical features of construction project planning software. Sigma J Eng Nat Sci 2022;41:781792. [CrossRef]
• [24] Liu J. Bayesian network inference on risks of construction schedule-cost. Proceedings - 2010 International Conference of Information Science and Management Engineering, ISME. 2010;2:1518. [CrossRef]
• [25] Kong F, Wu X, Cai L. International Symposium on Computational Intelligence and Design. 2008;2124. [CrossRef]
• [26] Huawang S, Wanqing L. The integrated methodology of rough set theory and artificial neural network for construction project cost prediction. Proceedings - 2008 2nd International Symposium on Intelligent Information Technology Application, IITA 2008;2:6064. [CrossRef]
• [27] Ensafi M, Alimoradi S, Gao X, Thabet W. Machine learning and artificial intelligence applications in building construction: present status and future trends. Constr Res Congr 2022;964973. [CrossRef]
• [28] Sravanthi J, Sobti R, Semwal A, Shravan M, Al-Hilali AA, Alazzam MB. AI-Assisted Resource Allocation in Project Management. In: 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). 2023. p. 7074. [CrossRef]
• [29] Afzal F, Yunfei S, Nazir M, Bhatti SM. A review of artificial intelligence-based risk assessment methods for capturing complexity-risk interdependencies: Cost overrun in construction projects. Int J Manag Proj Bus 2021;14:300328. [CrossRef]
• [30] Chenya L, Aminudin E, Mohd S, Yap LS. Intelligent risk management in construction projects: Systematic Literature Review. IEEE Access 2022;10:72936-72954. [CrossRef]
• [31] Arksey H, O’Malley L. Scoping studies: Towards a methodological framework. Int J Soc Res Methodol Theory Pract 2005;8:1932. [CrossRef] [32] Peters MD, Godfrey CM, Khalil H, McInerney P, Parker D, Soares CB. Guidance for conducting systematic scoping reviews. Int J Evid Based Health 2015;13:141146. [CrossRef]
• [33] Jepson R, Blasi ZD, Wright K, Riet GT. Scoping review of the effectiveness of mental health services, CRD Report 21. York: NHS Centre for Reviews and Dissemination, University of York; 2001.
• [34] Mays N, Roberts E, Popay J. Synthesizing research evidence. In: Fulop N, Allen P, Clarke A, Black N, editors. Studying the organization and delivery of health services: Research methods. London: Routledge; 2001.
• [35] Xuan L, Li J. Fusion of Computer Technology and Intelligent Logic Analysis Algorithm in Construction Engineering Cost Management. In: International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2022. 2022. p. 12941297. [CrossRef]
• [36] Fathi H, Dai F, Lourakis M. Automated as-built 3D reconstruction of civil infrastructure using computer vision: Achievements, opportunities, and challenges. Adv Eng Inform 2015;29:149161. [CrossRef]
• [37] Lee HS, Shin JW, Park M, Ryu HG. Probabilistic duration estimation model for high-rise structural work. J Constr Eng Manag 2009;135:12891298. [CrossRef]
• [38] Gondia A, Siam A, El-Dakhakhni W, Nassar AH. Machine learning algorithms for construction projects delay risk prediction. J Constr Eng Manag 2020;146:116. [CrossRef]
• [39] Qiao Y, Labi S, Fricker JD. Hazard-based duration models for predicting actual duration of highway projects using nonparametric and parametric survival analysis. J Manage Eng 2019;35:04019024. [CrossRef]
• [40] Wang Q, Tan Y, Mei Z. Computational methods of acquisition and processing of 3D point cloud data for construction applications. Arch Comput Methods Eng 2020;27:479499. [CrossRef]
• [41] Khallaf R, Khallaf M. Classification and analysis of deep learning applications in construction: A systematic literature review. Autom Constr 2021;129:103760. [CrossRef]
• [42] Mohammadi M, Al-Fuqaha A, Sorour S, Guizani M. Deep learning for IoT big data and streaming analytics: A survey. IEEE Commun Surv Tutor 2018;20:29232960. [CrossRef]
• [43] Jin R, Han S, Hyun C, Cha Y. Application of case-based reasoning for estimating preliminary duration of building projects. J Constr Eng Manag 2016;142:04015082. [CrossRef]
• [44] Al-Zubaidi EDA, Yas AH, Abbas HF. Guess the time of implementation of residential construction projects using neural networks ANN. Period Eng Nat Sci 2019;7:12181227. [CrossRef]
• [45] Da S, Hanbin L, Botao Z. Formal Modeling of Smart Contracts for Quality Acceptance in Construction. 2020;7987. [CrossRef]
• [46] Nikas A, Poulymenakou A, Kriaris P. Investigating antecedents and drivers affecting the adoption of collaboration technologies in the construction industry. Autom Constr 2007;16:632641. [CrossRef]
• [47] Martínez-Rojas M, Marín N, Vila MA. The role of information technologies to address data handling in construction project management. J Comput Civ Eng 2016;30:120[CrossRef]
• [48] Faghihi V, Nejat A, Reinschmidt KF, Kang JH. Automation in construction scheduling: A review of the literature. Int J Adv Manuf Technol 2015;81:18451856. [CrossRef]
• [49] Turban E, Sharda R, Delen D. Decision Support and Business Intelligence Systems. 9th ed. Pearson Education; 2011.
• [50] Zhu H, Hwang BG, Ngo J, Tan JPS. Applications of smart technologies in construction project management. J Constr Eng Manag 2022;148:112. [CrossRef]
• [51] Liu Y, Wang Y, Li X. Computer vision technologies and machine learning algorithms for construction safety management: A critical review. ICCREM 2019. 2019;415424. [CrossRef]
• [52] Liu N, Kang BG, Zheng Y. Current trend in planning and scheduling of construction projects using artificial intelligence. IET Conf Publ. 2018;2018(CP754):16.
• [53] Gao Y, Sun X. Construction of ODR platform of engineering construction laws and regulations based on cloud computing technology. Proc 2021 2nd Int Conf Big Data Artif Intell Softw Eng 2021;722727. [CrossRef]
• [54] Choi SJ, Choi SW, Kim JH, Lee EB. AI and text-mining applications for analyzing contractor’s risk in invitation to bid (ITB) and contracts for engineering procurement and construction (EPC) projects. Energies 2021;14:4632. [CrossRef]
• [55] Chou JS, Cheng MY, Wu YW, Pham AD. Optimizing parameters of support vector machine using fast messy genetic algorithm for dispute classification. Expert Syst Appl 2014;41:39553964. [CrossRef]
• [56] Hatami M, Paneru S, Flood I. Applicability of artificial intelligence (AI) methods to construction manufacturing: A literature review. Constr Res Congr. 2022;3-C:964973. [CrossRef]
• [57] Alheeti KMA, Aldaiyat RM. A new labour safety in construction management based on artificial intelligence. Period Eng Nat Sci 2021;9:685691. [CrossRef]
• [58] Ali TH, Akhund MA, Memon NA, Memon AH, Imad HU, Khahro SH. Application of artificial intelligence in construction waste management. Proc 2019 8th Int Conf Ind Technol Manage ICITM 2019. p. 5055. [CrossRef]
• [59] Rafsanjani HN, Nabizadeh AH. Towards human-centered artificial intelligence (AI) in architecture, engineering, and construction (AEC) industry. Comput Human Behav Rep 2023;11:100319. [CrossRef]
• [60] Németh P. Application possibilities of artificial neural networks in the construction industry. Proc 2014 Int Conf Comput Sci Comput Intell CSCI 2014 2014;1:437439. [CrossRef]
• [61] Karki S, Hadikusumo B. Machine learning for the identification of competent project managers for construction projects in Nepal, 2021. [CrossRef]
• [62] Liu C, Sepasgozar ME, Shirowzhan S, Mohammadi G. Applications of object detection in modular construction based on a comparative evaluation of deep learning algorithms. Constr Innov 2022;22:141159. [CrossRef]
• [63] Song M, Chen X. Construction of enterprise business management analysis framework in the development of artificial intelligence. Proc 2021 Int Conf Comput Inf Sci Artif Intell CISAI. 2021;689692. [CrossRef]
• [64] Xu J. Construction project cost management model based on big data. J Phys Conf Ser 2021;128:022017. [CrossRef]
• [65] Xu D, Xiao X. Influence of the development of VR technology on enterprise human resource management in the era of artificial intelligence. IEEE Access. 2020;11. [CrossRef]
• [66] Yao Y, Hu Q. Research on the new era of construction management mode of using the Internet of Things and big data. Proc 2021 2nd Int Semin Artif Intell Netw Inf Technol AINIT 2021, 2021. p. 683686. [CrossRef]
• [67] Elmousalami HH. Artificial intelligence and parametric construction cost estimate modeling: State-of-the-art review. J Constr Eng Manag 2020;146. [CrossRef]
• [68] Fan SL, Yeh I, Chi W. Improvement in estimating durations for building projects using artificial neural network and sensitivity analysis. J Constr Eng Manag 2021;147. [CrossRef]
• [69] Jacobsen EL, Teizer J. Deep learning in construction: Review of applications and potential avenues. J Comput Civ Eng 2022;36:112. [CrossRef]
• [70] Xiao B, Wang Y, Kang SC. Deep learning image captioning in construction management: A feasibility study. J Constr Eng Manag 2022;148:04022049.
• [71] Eber W. Potentials of artificial intelligence in construction management. Organ Technol Manage Constr 2020;12:20532063. [CrossRef]
• [72] Chen HP, Ying KC. Artificial intelligence in the construction industry: Main development trajectories and future outlook. Appl Sci (Switz) 2022;12. [CrossRef]
• [73] Kumar V, Pandey A, Singh R. Can artificial intelligence be a critical success factor of construction projects? Practitioner perspectives. Technol Innov Manag Rev 2021;11. [CrossRef]
• [74] Okudan O, Budayan C, Dikmen I. A knowledge-based risk management tool for construction projects using case-based reasoning. Expert Syst Appl 2021;173:114776. [CrossRef]