Artificial Intelligence in Construction Projects: An Explorative Study of Professionals’ Expectations



Abstract Views
2696

Downloads
3600

Citations

Share

##plugins.themes.bootstrap3.article.sidebar##

Submitted May 6, 2022
Published May 28, 2022
10.24018/ejbmr.2022.7.3.1432

Abstract viewed = 2696 times

Table of Contents

##plugins.themes.bootstrap3.article.main##

have a huge impact on projects and project management practices in the forthcoming years. The purpose of this paper is to contribute to project management theory and practice in the construction industry by analyzing the expectations of project professionals. A mixed method based on an international survey and semi-structured interviews was applied. The results show that construction project practitioners are looking for AI solutions to support the quantitative processes mainly related to scope, schedule, cost, quality, and risk management. However, the human-related processes, such as communication and stakeholder management, are not expected to be directly enhanced by AI, although might benefit from it indirectly. The findings also demonstrate a difference between amplifying and accelerating countries, where somewhat surprisingly the latter are more ready to adopt AI in their projects.

Keywords: Artificial Intelligence Construction Digital Readiness Project Management

Downloads

Download data is not yet available.

References

  1. Aarseth, W., Rolstadås, A., & Andersen, B. (2014). Managing organizational challenges in global projects. International Journal of Managing Projects in Business, 7(1), 103–132.
     Google Scholar
  2. Agarwal, A., Colak, S., & Erenguc, S. (2011). A neurogenetic approach for the resource-constrained project scheduling problem. Computers & Operations Research, 38(1), 44–50.
     Google Scholar
  3. Aidan, I. A., Al-Jeznawi, D., & Al-Zwainy, F. M. S. (2020). Predicting Earned Value Indexes in Residential Complexes’ Construction Projects Using Artificial Neural Network Model. https://doi.org/10.20944/preprints202003.0363.v1.
     Google Scholar
  4. Akinci, B., Boukamp, F., Gordon, C., Huber, D., Lyons, C., & Park, K. (2006). A formalism for utilization of sensor systems and integrated project models for active construction quality control. Automation in Construction, 15(2), 124–138.
     Google Scholar
  5. Alfonseca, M., Cebrian, M., Anta, A. F., Coviello, L., Abeliuk, A., & Rahwan, I. (2021). Superintelligence cannot be contained: Lessons from Computability Theory. Journal of Artificial Intelligence Research, 70, 65–76.
     Google Scholar
  6. Amade, B. (2017). The impact of social media in achieving effective communication in construction project delivery. European Project Management Journal, 7(1), 24–35.
     Google Scholar
  7. Anantatmula, V. S. (2008). The role of technology in the project manager performance model. Project Management Journal, 39(1), 34–48.
     Google Scholar
  8. Arafa, M., & Alqedra, M. (2011). Early stage cost estimation of buildings construction projects using artificial neural networks. Journal of Artificial Intelligence, 4(1).
     Google Scholar
  9. Attar, A. M., Khanzadi, M., Dabirian, S., & Kalhor, E. (2013). Forecasting contractor’s deviation from the client objectives in prequalification model using support vector regression. International Journal of Project Management, 31(6), 924–936.
     Google Scholar
  10. Auth, G., Jokisch, O., & Dürk, C. (2019). Revisiting automated project management in the digital age-a survey of AI approaches. Online Journal of Applied Knowledge Management (OJAKM), 7(1), 27–39.
     Google Scholar
  11. Ayodele, T. O., & Kajimo-Shakantu, K. (2021). The fourth industrial revolution (4thIR) and the construction industry-the role of data sharing and assemblage. IOP Conference Series: Earth and Environmental Science, 654(1), 12013.
     Google Scholar
  12. Aziz, R. F., Hafez, S. M., & Abuel-Magd, Y. R. (2014). Smart optimization for mega construction projects using artificial intelligence. Alexandria Engineering Journal, 53(3), 591–606.
     Google Scholar
  13. Banihashemi, S., Hosseini, M. R., Golizadeh, H., & Sankaran, S. (2017). Critical success factors (CSFs) for integration of sustainability into construction project management practices in developing countries. International Journal of Project Management, 35(6), 1103–1119.
     Google Scholar
  14. Betts, M., & Lansley, P. (1995). International Journal of Project Management: a review of the first ten years. International Journal of Project Management, 13(4), 207–217.
     Google Scholar
  15. Borana, J. (2016). Applications of Artificial Intelligence & Associated Technologies. Proceeding of International Conference on Emerging Technologies in Engineering, Biomedical, Management and Science [ETEBMS-2016], March, 25–30.
     Google Scholar
  16. Boström, N. (2014). Superintelligence: Paths, dangers, strategies. In Superintelligence: Paths, dangers, strategies. Oxford University Press. Oxford, UK.
     Google Scholar
  17. Boström, N., & Yudkowsky, E. (2014). The ethics of artificial intelligence. In & R. W. M. Frankish K. (Ed.), The Cambridge handbook of artificial intelligence (Vol. 1, pp. 316–334). Cambridge University Press. New York, NY.
     Google Scholar
  18. Cahyadi, A., & Magda, R. (2021). Digital Leadership in the Economies of the G20 Countries: A Secondary Research. Economies, 9(1), 32.
     Google Scholar
  19. Carden, L., & Egan, T. (2008). Does our literature support sectors newer to project management? The search for quality publications relevant to nontraditional industries. Project Management Journal, 39(3), 6–27.
     Google Scholar
  20. Cárdenas, I. C., Al‐Jibouri, S. S. H., Halman, J. I. M., & van Tol, F. A. (2014). Modeling risk‐related knowledge in tunneling projects. Risk Analysis, 34(2), 323–339.
     Google Scholar
  21. Chao, L.-C., & Chien, C.-F. (2010). A model for updating project S-curve by using neural networks and matching progress. Automation in Construction, 19(1), 84–91.
     Google Scholar
  22. Charniak, E., & McDermott, D. (1985). Introduction to Artificial Intelligence. Addison Wesley.
     Google Scholar
  23. Chen, S.-M., Chen, P.-H., & Chang, L.-M. (2012). Simulation and analytical techniques for construction resource planning and scheduling. Automation in Construction, 21, 99–113.
     Google Scholar
  24. Cheng, M.-Y., Hoang, N.-D., Roy, A. F. V, & Wu, Y.-W. (2012). A novel time-depended evolutionary fuzzy SVM inference model for estimating construction project at completion. Engineering Applications of Artificial Intelligence, 25(4), 744–752.
     Google Scholar
  25. Cheng, M.-Y., Hoang, N.-D., & Wu, Y.-W. (2015). Cash flow prediction for construction project using a novel adaptive time-dependent least squares support vector machine inference model. Journal of Civil Engineering and Management, 21(6), 679–688.
     Google Scholar
  26. Cheng, M.-Y., Peng, H.-S., Wu, Y.-W., & Chen, T.-L. (2010). Estimate at completion for construction projects using evolutionary support vector machine inference model. Automation in Construction, 19(5), 619–629.
     Google Scholar
  27. Cheng, M.-Y., & Roy, A. F. V. (2011). Evolutionary fuzzy decision model for cash flow prediction using time-dependent support vector machines. International Journal of Project Management, 29(1), 56–65.
     Google Scholar
  28. Cheng, M.-Y., Tsai, H.-C., & Sudjono, E. (2010). Conceptual cost estimates using evolutionary fuzzy hybrid neural network for projects in construction industry. Expert Systems with Applications, 37(6), 4224–4231.
     Google Scholar
  29. Cheng, M.-Y., Tsai, H.-C., & Sudjono, E. (2011). Evaluating subcontractor performance using evolutionary fuzzy hybrid neural network. International Journal of Project Management, 29(3), 349–356.
     Google Scholar
  30. Cho, J., & Lee, G. (2019). A chatbot system for construction daily report information management. ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, 36, 429–437.
     Google Scholar
  31. Chou, J.-S., Lin, C.-W., Pham, A.-D., & Shao, J.-Y. (2015). Optimized artificial intelligence models for predicting project award price. Automation in Construction, 54, 106–115.
     Google Scholar
  32. Chyung, S. Y. (Yonnie), Roberts, K., Swanson, I., & Hankinson, A. (2017). Evidence-Based Survey Design: The Use of a Midpoint on the Likert Scale. Performance Improvement, 56(10), 15–23.
     Google Scholar
  33. Cisco. (2019). Cisco Global Digital Readiness Index 2019 (White Paper Cisco Public).
     Google Scholar
  34. Coughlan, J., & Macredie, R. D. (2002). Effective communication in requirements elicitation: a comparison of methodologies. Requirements Engineering, 7(2), 47–60.
     Google Scholar
  35. Crawford, L., Pollack, J., & England, D. (2006). Uncovering the trends in project management: Journal emphases over the last 10 years. International Journal of Project Management, 24(2), 175–184. https://doi.org/https://doi.org/10.1016/j.ijproman.2005.10.005.
     Google Scholar
  36. Creswell, J. W., Plano Clark, V. L., Gutmann, M. L., & Hanson, W. E. (2003). An expanded typology for classifying mixed methods research into designs. A. Tashakkori y C. Teddlie, Handbook of Mixed Methods in Social and Behavioral Research, 209–240.
     Google Scholar
  37. Dawes, J. (2008). Do Data Characteristics Change According to the Number of Scale Points Used? An Experiment Using 5-Point, 7-Point and 10-Point Scales. International Journal of Market Research, 50(1), 61–104.
     Google Scholar
  38. de Soto, B. G., & Adey, B. T. (2016). Preliminary resource-based estimates combining artificial intelligence approaches and traditional techniques. Procedia Engineering, 164, 261–268.
     Google Scholar
  39. Deloitte. (2018). Artificial Intelligence (Issue March).
     Google Scholar
  40. Di Thommazo, A., Rovina, R., de Oliveira, T. R., Olivatto, G., Hernandes, E. M., Werneck, V., & Fabbri, S. C. P. F. (2014). Using Artificial Intelligence Techniques to Enhance Traceability Links. ICEIS (2), 26–38.
     Google Scholar
  41. EC. (2018). Artificial Intelligence for Europe: COMMUNICATION FROM THE COMMISSION (SWD(2018) 137 final).
     Google Scholar
  42. Faghihi, V., Nejat, A., Reinschmidt, K. F., & Kang, J. H. (2015). Automation in construction scheduling: a review of the literature. The International Journal of Advanced Manufacturing Technology, 81(9–12), 1845–1856.
     Google Scholar
  43. Faghihi, V., Reinschmidt, K. F., & Kang, J. H. (2014). Construction scheduling using genetic algorithm based on building information model. Expert Systems with Applications, 41(16), 7565–7578.
     Google Scholar
  44. Fang, C., Marle, F., Xie, M., & Zio, E. (2013). An integrated framework for risk response planning under resource constraints in large engineering projects. IEEE Transactions on Engineering Management, 60(3), 627–639.
     Google Scholar
  45. Fridgeirsson, T. V, Ingason, H. T., Jonasson, H. I., & Jonsdottir, H. (2021). An Authoritative Study on the Near Future Effect of Artificial Intelligence on Project Management Knowledge Areas. In Sustainability (Vol. 13, Issue 4).
     Google Scholar
  46. Gartner. (2020). 2020 CIO Agenda: Resilience During Disruption. In CIO Agenda.
     Google Scholar
  47. Gfrerer, A., Hutter, K., Füller, J., & Ströhle, T. (2020). Ready or Not: Managers’ and Employees’ Different Perceptions of Digital Readiness. California Management Review, 63(2), 23–48.
     Google Scholar
  48. Golpayegani, S. A. H., & Parvaresh, F. (2011). The logical precedence network planning of projects, considering the finish-to-start (FS) relations, using neural networks. The International Journal of Advanced Manufacturing Technology, 55(9–12), 1123–1133.
     Google Scholar
  49. Haenlein, M., & Kaplan, A. (2019). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review, 61(4), 5–14.
     Google Scholar
  50. Hester, P. (2015). Analyzing Stakeholders Using Fuzzy Cognitive Mapping. Procedia Computer Science, 61, 92–97.
     Google Scholar
  51. Ho, S.-P., Tserng, H.-P., & Jan, S.-H. (2013). Enhancing knowledge sharing management using BIM technology in construction. The Scientific World Journal, 170498.
     Google Scholar
  52. Hofmann, P., Jöhnk, J., Protschky, D., & Urbach, N. (2020). Developing purposeful AI use cases–a structured method and its application in project management. The 15th International Conference on Wirtschaftsinformatik (WI), Potsdam, Germany.
     Google Scholar
  53. Holzmann, V., Zitter, D., & Peshkess, S. (2022). The Expectations of Project Managers from Artificial Intelligence: A Delphi Study. Project Management Journal, 87569728211061780.
     Google Scholar
  54. Hyväri, I. (2006). Success of projects in different organizational conditions. Project Management Journal, 37(4), 31–41.
     Google Scholar
  55. Iansiti, M., & Lakhani, K. R. (2020). Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World. Harvard Business Press. Boston, MA.
     Google Scholar
  56. Ibrahim, Y. M., Lukins, T. C., Zhang, X., Trucco, E., & Kaka, A. P. (2009). Towards automated progress assessment of workpackage components in construction projects using computer vision. Advanced Engineering Informatics, 23(1), 93–103.
     Google Scholar
  57. Islam, M. S., & Nepal, M. (2016). A fuzzy-Bayesian model for risk assessment in power plant projects. Procedia Computer Science, 100, 963–970.
     Google Scholar
  58. Ivankova, N. V, Creswell, J. W., & Stick, S. L. (2006). Using mixed-methods sequential explanatory design: From theory to practice. Field Methods, 18(1), 3–20.
     Google Scholar
  59. Jebari, K., & Lundborg, J. (2020). Artificial superintelligence and its limits: why AlphaZero cannot become a general agent. AI & SOCIETY, 1–9.
     Google Scholar
  60. Kamoona, K. R. K., & Budayan, C. (2019). Implementation of Genetic Algorithm Integrated with the Deep Neural Network for Estimating at Completion Simulation. Advances in Civil Engineering, ID 7081073.
     Google Scholar
  61. Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25.
     Google Scholar
  62. Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2018). A dynamic fuzzy approach based on the EDAS method for multi-criteria subcontractor evaluation. Information, 9(3), 68.
     Google Scholar
  63. Khodakarami, V., & Abdi, A. (2014). Project cost risk analysis: A Bayesian networks approach for modeling dependencies between cost items. International Journal of Project Management, 32(7), 1233–1245.
     Google Scholar
  64. Kurzweil, R. (1990). The Age of Intelligent Machines. MIT Press.
     Google Scholar
  65. Lahmann, M. (2019). AI will transform project management. Are you ready?
     Google Scholar
  66. Lewis, J. A., Odeyinka, H., & Eadie, R. (2011). Innovative construction procurement selection through an artificial intelligence approach. Management, 745, 754.
     Google Scholar
  67. Lhee, S. C., Flood, I., & Issa, R. R. A. (2014). Development of a two-step neural network-based model to predict construction cost contingency. Journal of Information Technology in Construction (ITcon), 19(24), 399–411.
     Google Scholar
  68. Li, H., Xiong, L., Liu, Y., & Li, H. (2018). An effective genetic algorithm for the resource levelling problem with generalised precedence relations. International Journal of Production Research, 56(5), 2054–2075.
     Google Scholar
  69. Liu, L., Silva, E. A., Wu, C., & Wang, H. (2017). A machine learning-based method for the large-scale evaluation of the qualities of the urban environment. Computers, Environment and Urban Systems, 65, 113–125.
     Google Scholar
  70. Luger, G. F. (2005). Artificial intelligence: structures and strategies for complex problem solving. Addison-Wesley. New York, NY.
     Google Scholar
  71. Lv, X., & El-Gohary, N. (2016). Text Analytics for Supporting Stakeholder Opinion Mining for Large-scale Highway Projects. Procedia Engineering, 145, 518–524. https://doi.org/https://doi.org/10.1016/j.proeng.2016.04.039.
     Google Scholar
  72. Martínez-Rojas, M., Marín, N., & Miranda, M. A. V. (2016). An intelligent system for the acquisition and management of information from bill of quantities in building projects. Expert Systems with Applications, 63, 284–294.
     Google Scholar
  73. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI Magazine, 27(4), 12.
     Google Scholar
  74. McKinsey Analytics. (2019). Global AI Survey: AI proves its worth, but few scale impact.
     Google Scholar
  75. Meisels, M. (2020). 2020 engineering and construction industry outlook - Deloitte.
     Google Scholar
  76. Müller, V. C., & Boström, N. (2016). Future progress in artificial intelligence: A survey of expert opinion. In M. V. (Ed.), Fundamental issues of artificial intelligence (pp. 555–572). Springer.
     Google Scholar
  77. Munir, M. (2019). How Artificial Intelligence can help Project Managers. Global Journal of Management and Business Research, 19(4-A).
     Google Scholar
  78. Odeyinka, H. A., Lowe, J., & Kaka, A. P. (2013). Artificial neural network cost flow risk assessment model. Construction Management and Economics, 31(5), 423–439.
     Google Scholar
  79. Pan, Y., & Zhang, L. (2021). Roles of artificial intelligence in construction engineering and management: A critical review and future trends. Automation in Construction, 122, 103517.
     Google Scholar
  80. Parsi, N. (2019). Brain Power. PM Network, 33(9), 38–43.
     Google Scholar
  81. Patton, M. Q. (2014). Qualitative research & evaluation methods: Integrating theory and practice. Sage publications.
     Google Scholar
  82. PMI. (2017). A guide to the project management body of knowledge (PMBOK guide), 6th ed. (Vol. 2). Project Management Institute.
     Google Scholar
  83. PMI. (2019). Pulse of the Profession, AI Innovators: Cracking the Code on Project Performance.
     Google Scholar
  84. Polat, G., Kaplan, B., & Bingol, B. N. (2015). Subcontractor selection using genetic algorithm. Procedia Engineering, 123, 432–440.
     Google Scholar
  85. Pozin, M. A., Nawi, M. M., Mydin, A. O., Riazi, S. M., & Imran, A. (2019). An ability of WhatsApp usage in Industrialised Building System (IBS) construction project. International Association of Online Engineering.
     Google Scholar
  86. Qazi, A., Quigley, J., Dickson, A., & Kirytopoulos, K. (2016). Project Complexity and Risk Management (ProCRiM): Towards modelling project complexity driven risk paths in construction projects. International Journal of Project Management, 34(7), 1183–1198.
     Google Scholar
  87. Rafiei, M. H., & Adeli, H. (2018). Novel machine-learning model for estimating construction costs considering economic variables and indexes. Journal of Construction Engineering and Management, 144(12), 4018106.
     Google Scholar
  88. Raisch, S., & Krakowski, S. (2020). Artificial Intelligence and Management: The Automation-Augmentation Paradox. Academy of Management Review, 46(1), 192–210.
     Google Scholar
  89. Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017). Reshaping business with artificial intelligence: Closing the gap between ambition and action. MIT Sloan Management Review, 59(1).
     Google Scholar
  90. Rashidi, A., Sigari, M. H., Maghiar, M., & Citrin, D. (2016). An analogy between various machine-learning techniques for detecting construction materials in digital images. KSCE Journal of Civil Engineering, 20(4), 1178–1188.
     Google Scholar
  91. Remidez, H., & Jones, N. B. (2012). Developing a model for social media in project management communications. International Journal of Business and Social Science, 3(3), 33–36.
     Google Scholar
  92. Rich, E., & Knight, K. (1991). Introduction to Artificial Networks. Mac Graw-Hill.
     Google Scholar
  93. Rowley, J. (2012). Conducting research interviews. Management Research Review, 35(3/4), 260–271.
     Google Scholar
  94. Sacks, R., Girolami, M., & Brilakis, I. (2020). Building information modelling, artificial intelligence and construction tech. Developments in the Built Environment, 4, 100011.
     Google Scholar
  95. Shen, W., Tang, W., Wang, S., Duffield, C. F., Hui, F. K. P., & You, R. (2017). Enhancing trust-based interface management in international engineering-procurement-construction projects. Journal of Construction Engineering and Management, 143(9), 4017061.
     Google Scholar
  96. Siami-Irdemoosa, E., Dindarloo, S. R., & Sharifzadeh, M. (2015). Work breakdown structure (WBS) development for underground construction. Automation in Construction, 58, 85–94.
     Google Scholar
  97. Singer, D., Bügler, M., Borrmann, A., & Center, L. O. (2016). Knowledge based Bridge Engineering-Artificial Intelligence meets Building Information Modeling. Proc. of the EG-ICE Workshop on Intelligent Computing in Engineering.
     Google Scholar
  98. Sousa, R. L., & Einstein, H. H. (2012). Risk analysis during tunnel construction using Bayesian Networks: Porto Metro case study. Tunnelling and Underground Space Technology, 27(1), 86–100.
     Google Scholar
  99. Sperry, R. C., & Jetter, A. J. (2019). A Systems Approach to Project Stakeholder Management: Fuzzy Cognitive Map Modeling. Project Management Journal, 50(6), 699–715.
     Google Scholar
  100. Strnad, D., & Guid, N. (2010). A fuzzy-genetic decision support system for project team formation. Applied Soft Computing, 10(4), 1178–1187.
     Google Scholar
  101. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 23–65.
     Google Scholar
  102. Turner, C. J., Oyekan, J., Stergioulas, L., & Griffin, D. (2021). Utilizing Industry 4.0 on the Construction Site: Challenges and Opportunities. IEEE Transactions on Industrial Informatics, 17(2), 746–756.
     Google Scholar
  103. Ulubeyli, S., & Kazaz, A. (2016). Fuzzy multi-criteria decision making model for subcontractor selection in international construction projects. Technological and Economic Development of Economy, 22(2), 210–234.
     Google Scholar
  104. Ulubeyli, S., Kazaz, A., & Arslan, V. (2017). Decision criteria for subcontractor selection in international construction projects. Proceedings of the International Conference on Civil and Environmental Engineering (ICOCEE), May, 8–10.
     Google Scholar
  105. Utama, W. P., Chan, A. P. C., Zahoor, H., & Gao, R. (2016). Review of research trend in international construction projects: a bibliometric analysis. Construction Economics and Building, 16(2), 71–82.
     Google Scholar
  106. Wang, H., & Meng, X. (2019). Transformation from IT-based knowledge management into BIM-supported knowledge management: A literature review. Expert Systems with Applications, 121, 170–187.
     Google Scholar
  107. Wang, Q. (2019). How to apply AI technology in Project Management? PM World Journal, VIII(III).
     Google Scholar
  108. Wauters, M., & Vanhoucke, M. (2016). A comparative study of Artificial Intelligence methods for project duration forecasting. Expert Systems with Applications, 46, 249–261.
     Google Scholar
  109. Wauters, M., & Vanhoucke, M. (2017). A Nearest Neighbour extension to project duration forecasting with Artificial Intelligence. European Journal of Operational Research, 259(3), 1097–1111.
     Google Scholar
  110. Webber, S. S., Detjen, J., MacLean, T. L., & Thomas, D. (2019). Team challenges: Is artificial intelligence the solution? Business Horizons, 62(6), 741–750.
     Google Scholar
  111. Williams, C. (2007). Research methods. Journal of Business & Economics Research (JBER), 5(3).
     Google Scholar
  112. Winston, H. P. (1993). Artificial Intelligence (3rd ed.). Addison-Wesley.
     Google Scholar
  113. Yalcinkaya, M., & Arditi, D. (2013). Building information modeling (BIM) and the construction management body of knowledge. IFIP International Conference on Product Lifecycle Management, 619–629.
     Google Scholar
  114. Yazdani-Chamzini, A. (2014). Proposing a new methodology based on fuzzy logic for tunnelling risk assessment. Journal of Civil Engineering and Management, 20(1), 82–94.
     Google Scholar
  115. Zhang, J., & El-Gohary, N. M. (2016). Semantic NLP-based information extraction from construction regulatory documents for automated compliance checking. Journal of Computing in Civil Engineering, 30(2), 4015014.
     Google Scholar