Emerging artificial intelligence methods in structural engineering

By Hadi Salehi, Rigoberto Burgueño. Engineering structures, 2018

910 citations recorded by the source corpus.

Abstract 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. Compared to traditional methods, AI offers advantages to deal with problems associated with uncertainties and is an effective aid 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 recently 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 techniques concerning applications of the noted AI methods in structural engineering developed over the last decade. First, a general introduction to AI is presented and the importance of AI in structural engineering is described. Thereafter, a review of recent applications of ML, PR, and DL in the field is provided, and the capability of such methods to address the restrictions of conventional models are discussed. Further, the advantages of employing such algorithmic methods are discussed in detail. Finally, potential research avenues and emerging trends for employing ML, PR, and DL are presented, and their limitations are discussed.

Subfields: Civil and Structural Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/c29ad537c20bbf57d1c1de75e6069ca3e157958a

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Citations in the corpus

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, background): “Various papers have shown the different cases of these ML algorithms in their reviews.(36,37) However, a clear cut between the utilization of different learners is somewhat hidden in the core of their review strategy.”
The promise of implementing machine learning in earthquake engineering: A state-of-the-art review (2020, background): “For instance, the IoT for smart cities can use real-time connected sensors to generate a large amount of spatiotemporal data about bridge networks or pipeline systems for the entire region (Salehi and Burgueño, 2018).”
Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review (2020, methodology): “are examples of clustering methods [22].”
Future Trends and Current State of Smart City Concepts: A Survey (2020, background): “On the other hand, the application domain has also been rapidly growing and studied in different scientiﬁc works [88]–[90].”
A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications (2020, background): “A general overview of ML utilization in structural engineering applications is presented in [64].”
A systematic review of convolutional neural network-based structural condition assessment techniques (2021, background): “Salehi and Burgueno (2018) 89 reviewed a suite of various artificial intelligence (AI) methods that have recently been used in 90 structural engineering.”