Applications of Artificial Intelligence in Structural Engineering: A Review
G. A. Suryawanshi1, L. S. Mahajan2, S. R. Bhagat3
1G. A. Suryawanshi, Assistant Professor, Dr. Babasaheb Ambedkar Technological University, Lonere, Raigad, India.
2L. S. Mahajan, Research Scholar, Dr. Babasaheb Ambedkar Technological University, Lonere, Raigad, India.
3S. R. Bhagat, Professor & HoD, Dr. Babasaheb Ambedkar Technological University, Lonere, Raigad, India.
Manuscript received on 01 August 2025 | First Revised Manuscript received on 15 August 2025 | Second Revised Manuscript received on 02 September 2025 | Manuscript Accepted on 15 September 2025 | Manuscript published on 30 September 2025 | PP: 7-11 | Volume-12 Issue-9, September 2025 | Retrieval Number: 100.1/ijies.B35380111222 | DOI: 10.35940/ijies.B3538.12090925
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Artificial intelligence (AI) is a computational approach that aims to mimic human-like thinking/cognitive abilities to tackle complicated engineering issues. AI is appropriate for engineering contexts with a large set of inputs. AI is a feasible alternative to traditional modelling and statistical techniques. Experimentation is a herculean task in the domain of structural engineering, so AI-based techniques are viable alternatives for the prediction of various engineering design parameters, such as structural response, compressive strength, etc. The goal of this research is to outline numerous applications of artificial intelligence in structural engineering that have emerged in recent years. Initially, a broad introduction to AI is provided, followed by a discussion of the relevance of AI in the field of structural engineering. Thereafter, a review of recent applications of AI techniques such as deep learning (DL), pattern recognition (PR), and machine learning (ML) in structural engineering is presented, and the ability of such techniques to meet the constraints of conventional models is explored. Furthermore, the benefits of adopting such algorithmic approaches are thoroughly addressed. Finally, future research areas and latest innovations by using deep learning, pattern recognition, and machine learning are given, along with their shortcomings.
Keywords: Structural Engineering, Artificial Intelligence, Machine Learning, Deep Learning.
Scope of the Article: Artificial Intelligence and Methods