Evaluating the Use of Artificial Intelligence in the Field of Civil Engineering: Basis for a Proposed Comprehensive Professional Organizational Support Program

Orlando D. Davin Jr.^1, , Genesis S. Jose² and Wilfredo B. Baniqued³

¹Graduate School, University of la Salette Inc. Philippines

²Computer Engineering and Electronics Engineering Department, University of la Salette Inc. Philippines

³Civil Engineering Department, Quirino State University, Cabarroguis, Philippines

Cite this paper:
Orlando D. Davin Jr., Genesis S. Jose and Wilfredo B. Baniqued. Evaluating the Use of Artificial Intelligence in the Field of Civil Engineering: Basis for a Proposed Comprehensive Professional Organizational Support Program. American Journal of Civil Engineering and Architecture. 2026; 14(3):128-136. doi: 10.12691/ajcea-14-3-6

Abstract

Artificial Intelligence (AI) is increasingly embedded in civil engineering work, yet empirical evidence on how practicing engineers actually use and experience AI remains limited, particularly in emergingeconomy settings [1]. This study examined AI utilization in civil engineering practice in Quirino Province, Philippines, and developed a comprehensive professional organizational support program based on the findings. A descriptivecomparative crosssectional survey was conducted among 150 practicing civil engineers using a structured 24item questionnaire covering four dimensions: technological, organizational, knowledge and skills, and ethical, cultural, and regulatory aspects. Descriptive statistics, independentsamples ttests, and oneway analysis of variance with post hoc testing were used to analyze the data. The overall mean score for AIrelated conditions was 2.89 on a fourpoint scale, indicating moderately favorable conditions. Knowledge and skills obtained the highest mean (3.03), followed by technological (2.96) and organizational aspects (2.88), while ethical, cultural, and regulatory aspects obtained the lowest mean (2.69), although they remained in the positive range. Significant differences were observed across demographic and sectoral groups, with younger engineers, those with 10 years or less of experience, and privatesector practitioners reporting more favorable AIrelated conditions than older, more experienced, and governmentsector counterparts; female respondents also perceived more favorable organizational support than male respondents. The findings indicate an emerging but not yet mature level of AI readiness in civil engineering practice and support the need for multidimensional interventions. A comprehensive professional organizational support program is proposed to strengthen AI adoption through capacitybuilding, stronger institutional backing, improved digital infrastructure, and clearer ethical and regulatory guidance, which may be useful for similar developingcountry contexts [2,3].

Keywords:
artificial intelligence civil engineering practice technology adoption organizational support professional development Philippines

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	Algharairi, M. S., & Fahim, A. R. (2023). The role of artificial intelligence in civil engineering applications and programs. Asian Journal of Civil Engineering, 7(3), 54–67.
[2]	Reyes, A. L., Cruz, A. C., & Yalung, M. R. (2021). The role of AI in infrastructure development: A study of its applications in the Philippines. Journal of Philippine Construction Management, 5(2), 45–56.
[3]	Santos, R. M., Cruz, S. V., & Garcia, A. L. (2021). AI adoption in Philippine civil engineering: Challenges and opportunities. Philippine Engineering Journal, 10(1), 67–75.
[4]	Hosseini, M. R., Khosravi, A., & Sadeghpour, F. (2021). Machine learning for predicting construction project risks: A systematic review. Journal of Civil Engineering and Management, 27(4), 220–232.
[5]	Hassanein, M., Shehab, E., & ElMaraghy, H. (2020). Artificial intelligence in construction project management: A review. Advanced Engineering Informatics, 45, 101076.
[6]	Huang, X., Wang, L., & Zhao, Y. (2022). Reinforcement learning for automated construction planning. Automation in Construction, 130, 104374.
[7]	Goh, Y. M., Anwar, M. S., & Han, S. H. (2019). Artificial intelligence in construction project management: A review of applications. Journal of Construction Engineering and Management, 145(5), 04019034.
[8]	Kim, J., & Choi, S. (2023). AI-enhanced safety monitoring in construction: Wearable technology applications. Safety Science, 168, 106389.
[9]	Aquino, R. S., Bautista, M. T., & Salazar, A. E. (2022). Application of artificial intelligence in construction project management: A case study in Metro Manila. Journal of Engineering and Technology, 8(1), 45–58.
[10]	Sacks, R., Shtub, A., & Barak, R. (2020). AI in construction: Automating construction project management processes. Engineering, Construction and Architectural Management, 27(7), 1482–1500.
[11]	Alghazzawi, D., & Elghandour, R. (2023). AI-driven construction monitoring: Applications and future trends. Construction Science Journal, 12(3), 245–260.
[12]	Zhou, M., Fang, W., & Chen, T. (2023). Ethical and regulatory challenges of AI in construction management. Construction Law Review, 8(2), 78–94.
[13]	Tariq, M. S., Khokhar, R. I., & Aziz, A. (2022). Challenges and opportunities in adopting AI for project management in construction. Journal of Construction Engineering and Management, 148(6), 04022049.
[14]	Ogunlana, S. O., Smit, E., & Khandekar, A. (2021). Artificial intelligence in risk management in construction projects. Construction Management and Economics, 39(4), 359–371.
[15]	Diaz, J. P., & Reyes, P. M. (2023). Enhancing flood management systems with artificial intelligence: A study of Metro Manila. Philippine Journal of Civil Engineering, 34(2), 102–118.
[16]	Wang, P., Li, X., & Zhu, H. (2023). Generative AI for sustainable construction design. Journal of Sustainable Engineering, 25(1), 112–129.
[17]	Raiffa, H. (1968). Decision analysis: Introductory lectures on choices under uncertainty. Addison-Wesley.
[18]	Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291.
[19]	Miller, J. H., & Page, S. E. (2007). Complex adaptive systems: An introduction to computational models of social life. Princeton University Press.
[20]	Turner, J. R., & Cochrane, R. A. (1993). Goals-and-methods matrix: Coping with projects with ill-defined goals and/or methods of achieving them. International Journal of Project Management, 11(2), 93–102.
[21]	Vassallo, J., Finkelstein, D., & Serpell, A. (2020). Artificial intelligence and decision support in construction project management. Journal of Management in Engineering, 36(5), 04020033.
[22]	Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
[23]	Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.
[24]	Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
[25]	Stevenson, W. J. (2019). Operations management (14th ed.). McGraw-Hill.
[26]	Kerzner, H. (2017). Project management: A systems approach to planning, scheduling, and controlling (12th ed.). Wiley.
[27]	Simon, H. A. (1957). Administrative behavior: A study of decision-making processes in administrative organizations (2nd ed.). Free Press.
[28]	Kizito, S., Kasim, B., & Al-Baik, B. (2020). AI in infrastructure asset management: Current applications and future trends. Journal of Infrastructure Systems, 26(4), 04020048.
[29]	Ghimire, P., Kim, K., & Acharya, M. (2023). Generative AI in the construction industry: Opportunities & challenges. arXiv.
[30]	Zhang, Y., Sun, J., & Liu, H. (2023). Predictive maintenance in civil infrastructure using deep learning. IEEE Transactions on Smart Infrastructure, 10(4), 1672–1685.