Forum
May 17, 2022

A Faculty’s Perspective on Infusing Artificial Intelligence into Civil Engineering Education

Publication: Journal of Civil Engineering Education
Volume 148, Issue 4

Abstract

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

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Data Availability Statement

No data, models, or code were generated or used during the study.

Acknowledgments

I thank the editors and reviewers for their support of this work and constructive comments that enhanced the quality of this manuscript.

References

ACI (American Concrete Institute). 2019. Building code requirements for structural concrete and commentary. ACI 318-19. Farmington Hills, MI: ACI.
ASCE. 2020. “Improving efficiency in making better decisions with AI and data science.” Accessed March 6, 2022. https://www.youtube.com/watch?v=gtE7YG5kXIE.
Barbosa, F., J. Mischke, and M. Parsons. 2021. “Improving construction productivity.” Accessed April 13, 2021. https://www.mckinsey.com/business-functions/operations/our-insights/improving-construction-productivity.
Borah, D., K. Malik, and S. Massini. 2019. “Are engineering graduates ready for R&D jobs in emerging countries? Teaching-focused industry-academia collaboration strategies.” Res. Policy 48 (9): 103837. https://doi.org/10.1016/j.respol.2019.103837.
Cox, M. F., J. S. London, B. Ahn, J. Zhu, A. T. Torres-Ayala, S. Frazier, and O. Cekic. 2011. “Attributes of success for engineering Ph.D.s: Perspectives from academia and industry.” In Proc., ASEE Annual Conf. and Exposition. Washington, DC: American Society for Engineering Education. https://peer.asee.org/attributes-of-success-for-engineering-ph-d-s-perspectives-from-academia-and-industry.
Digital Science. 2021. “Dimensions.” Accessed January 30, 2022. https://www.dimensions.ai/.
Dosilovic, F. K., M. Brcic, and N. Hlupic. 2018. “Explainable artificial intelligence: A survey.” In Proc., 41st Int. Convention on Information and Communication Technology, Electronics and Microelectronics. New York: IEEE. https://ieeexplore.ieee.org/document/8400040.
Dunn, R., and M. Carbo. 1981. “Modalities: An open letter to Walter Barbe, Michael Milone, and Raymond Swassing.” Educ. Leadership 38 (5): 381–382.
Felder, R., and L. Silverman. 1988. “Learning and teaching styles in engineering education.” Eng. Educ. 78 (7): 674–681.
Feng, D.-C., W.-J. Wang, S. Mangalathu, and E. Taciroglu. 2021. “Interpretable XGBoost-SHAP machine-learning model for shear strength prediction of squat RC walls.” J. Struct. Eng. 147 (11): 04021173. https://doi.org/10.1061/(ASCE)ST.1943-541X.0003115.
Foster, R. 1986. Innovation: The attacker’s advantage. New York: Summit Books.
Gullion, S. 2021. “Uses of artificial intelligence in civil engineering.” Accessed March 6, 2022. https://keckwood.com/news-updates/uses-of-artificial-intelligence-in-civil-engineering/.
Harvey, D., C. Ling, and R. Shehab. 2010. “Comparison of student’s learning style in STEM disciplines.” In Proc., IIE Annual Conf. and Expo 2010. Norcross, GA: Institute of Industrial and Systems Engineers.
Hearns, A. 2019. “Giatec unveils first artificial intelligence solution for concrete.” Accessed March 6, 2022. https://www.giatecscientific.com/press/giatec-unveils-first-artificial-intelligence-solution-for-concrete/.
Hiriyur, B. 2020. “Thornton Tomasetti launches T2D2, an AI solution company to detect, classify and monitor deterioration.” Accessed March 6, 2022. https://www.thorntontomasetti.com/news/thornton-tomasetti-launches-t2d2-ai-solution-company-detect-classify-and-monitor-deterioration.
Jarrahi, M. H. 2018. “Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making.” Bus. Horiz. 61 (4): 577–586. https://doi.org/10.1016/j.bushor.2018.03.007.
Matos, G. R. 2017. Use of raw materials in the United States from 1900 through 2014. geological survey fact sheet 2017–3062. Reston, VA: USGS.
Naser, M. K., and K. Mueller. 2021. SP-350: The concrete industry in the era of artificial intelligence. Farmington Hills, MI: American Concrete Institute.
Naser, M. Z. 2021a. “An engineer’s guide to eXplainable Artificial Intelligence and Interpretable Machine Learning: Navigating causality, forced goodness, and the false perception of inference.” Autom. Constr. 129 (Sep): 103821. https://doi.org/10.1016/j.autcon.2021.103821.
Naser, M. Z. 2021b. “Mapping functions: A physics-guided, data-driven and algorithm-agnostic machine learning approach to discover causal and descriptive expressions of engineering phenomena.” Measurement 185 (Nov): 110098. https://doi.org/10.1016/j.measurement.2021.110098.
OSHA (Occupational Safety and Health Administration). 2022. OSHA laws and regulations. Washington, DC: OSHA.
Otto, E. R. 1987. “Innovation: The attacker’s advantage.” Acad. Manage. Rev. 12 (3): 571–573. https://doi.org/10.5465/amr.1987.4306576.
Rudin, C. 2019. “Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.” Nat. Mach. Intell. 1 (5): 206–215. https://doi.org/10.1038/s42256-019-0048-x.
Russell, S., and P. Norvig. 2010. Artificial intelligence a modern approach. 3rd ed. London: Pearson.
Ryu, M. G., K. He, D.-H. Lee, S.-I. Park, G. Thomas, and J. K. Paik. 2021. “Finite element modeling for the progressive collapse analysis of steel stiffened-plate structures in fires.” Thin-Walled Struct. 159 (Feb): 107262. https://doi.org/10.1016/j.tws.2020.107262.
Sacks, R., and R. Barak. 2010. “Teaching building information modeling as an integral part of freshman year civil engineering education.” J. Civ. Eng. Educ. 136 (1): 30–38. https://doi.org/10.1061/(ASCE)EI.1943-5541.0000003.
Shaeiwitz, J. A. 1996. “Outcomes assessment in engineering education.” J. Eng. Educ. 85 (3): 239–246. https://doi.org/10.1002/j.2168-9830.1996.tb00239.x.
Tao, F., J. Cheng, Q. Qi, M. Zhang, H. Zhang, and F. Sui. 2018. “Digital twin-driven product design, manufacturing and service with big data.” Int. J. Adv. Manufacturing Technol. 94 (9): 3563–3576. https://doi.org/10.1007/s00170-017-0233-1.
TRB (Transportation Research Board). 2020. Highway capacity manual. 6th edition: A guide for multimodal mobility analysis. Washington, DC: TRB.
Zaker Esteghamati, M., and M. M. Flint. 2021. “Developing data-driven surrogate models for holistic performance-based assessment of mid-rise RC frame buildings at early design.” Eng. Struct. 245 (Oct): 112971. https://doi.org/10.1016/j.engstruct.2021.112971.

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Go to Journal of Civil Engineering Education
Journal of Civil Engineering Education
Volume 148Issue 4October 2022

History

Received: Aug 17, 2021
Accepted: Mar 21, 2022
Published online: May 17, 2022
Published in print: Oct 1, 2022
Discussion open until: Oct 17, 2022

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Authors

Affiliations

Assistant Professor, School of Civil and Environmental Engineering & Earth Sciences, Clemson Univ., Clemson, SC 29634; Artificial Intelligence Research Institute for Science and Engineering (AIRISE), Clemson Univ., Clemson, SC 29634. ORCID: https://orcid.org/0000-0003-1350-3654. Email: [email protected]

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