Emerging artificial intelligence methods in Structural Engineering

EOI: 10.11242/viva-tech.01.04.062

Download Full Text here


Ms. Akshay Mistry "Emerging artificial intelligence methods in Structural Engineering", VIVA-IJRI Volume 1, Issue 4, Article 62, pp. 1-2, 2021. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.


Artificial intelligence (AI) is proving to be an efficient alternative approach to classical modeling techniques. AI refers to the branch of computer science that develops machines and software with human-like intelligence. Many problems in civil and structural engineering are affected by uncertainties that cannot be solved with traditional methods. AI aids to solve such complex problems. In addition, AI-based solutions are good alternatives to determine engineering design parameters when testing is not possible, thus resulting in significant savings in terms of human time and effort spent in experiments AI is also able to make the process of decision making faster, decrease error rates, and increase computational efficiency. Among the different AI techniques, machine learning (ML), pattern recognition (PR), and deep learning (DL) have acquired considerable attention and are establishing themselves as a new class of intelligent methods for use in structural engineering. The objective of this review paper is to summarize recently developed techniques with regards to the applications of the noted AI methods in structural engineering over the last decade. First, a general introduction to AI is presented and the importance of AI in the field is described. Thereafter, a review of recent applications of ML, PR, and DL in structural engineering is provided, and the capability of such methods to address the restrictions of conventional models are discussed. Further, the advantages of employing such intelligent methods are discussed in detail. Finally, potential research avenues and emerging trends for employing ML, PR, and DL are presented.


structural engineering, artificial intelligence, machine learning, pattern recognition, deep learning, soft computing


  1. Russell SJ, Norvig P, Canny JF, Malik JM, Edwards DD. Artificial intelligence: a modern approach.vol. 2. Prentice hall Upper Saddle River; 2003.
  2. Back T. Evolutionary computation: Toward a new philosophy of machine intelligence. Complexity1997;2:28–30. doi:10.1002/(SICI)1099-0526(199703/04)2:4<28::AID-CPLX7>3.0.CO;2-2
  3. Fadlullah ZM, Tang F, Mao B, Kato N, Akashi O, Inoue T, et al. State-of-the-Art Deep Learning:Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems. IEEE Commun Surv Tutor 2017;19:2432–55. doi:10.1109/COMST.2017.2707140.
  4. Modha DS, Ananthanarayanan R, Esser SK, Ndirango A, Sherbondy AJ, Singh R. Cognitive computing. Commun ACM 2011;54:62–71.
  5. Noor AK. Potential of cognitive computing and cognitive systems. Open Eng 2015;5:75–88.
  6. Shahin MA. Artificial intelligence in geotechnical engineering: applications, modeling aspects, and future directions. Metaheuristics Water Geotech. Transp. Eng., Elsevier; 2013, p. 169–204.
  7. Kicinger R, Arciszewski T, Jong KD. Evolutionary computation and structural design: A survey of the state-of-the-art. Comput Struct 2005;83:1943–78. doi:10.1016/j.compstruc.2005.03.002.
  8. Lu P, Chen S, Zheng Y. Artificial Intelligence in Civil Engineering. Math Probl Eng 2012;2012:1–22. doi:10.1155/2012/145974.
  9. Penades-Pla V, Garcia-Segura T, Marti JV, Yepes V. A review of multi-criteria decision- making methods applied to the sustainable bridge design. Sustainability 2016;8:1295.