A review of physics-based machine learning in civil engineering

• Published in: Results in engineering Vol. 13; p. 100316
• Main Authors: Vadyala, Shashank Reddy, Betgeri, Sai Nethra, Matthews, John C., Matthews, Elizabeth
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2022; Elsevier
• Subjects: Civil engineering; Deep neural network; Machine learning; Physics-based machine learning; Physics-based machine learning; Deep neural network; Civil engineering; Machine learning
• ISSN: 2590-1230; 2590-1230
• DOI: 10.1016/j.rineng.2021.100316

The recent development of machine learning (ML) and Deep Learning (DL) increases the opportunities in all the sectors. ML is a significant tool that can be applied across many disciplines, but its direct application to civil engineering problems can be challenging. ML for civil engineering applications that are simulated in the lab often fail in real-world tests. This is usually attributed to a data mismatch between the data used to train and test the ML model and the data it encounters in the real world, a phenomenon known as data shift. However, a physics-based ML model integrates data, partial differential equations (PDEs), and mathematical models to solve data shift problems. Physics-based ML models are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear equations. Physics-based ML, which takes center stage across many science disciplines, plays an important role in fluid dynamics, quantum mechanics, computational resources, and data storage. This paper reviews the history of physics-based ML and its application in civil engineering. •A detailed explanation of Physics-based machine learning.•A review of recent applications of Physics-based machine learning in Civil Engineering.•Potential research avenues in civil engineering are identified using Physics-based machine learning.•Advantages of physics-based machine learning in civil engineering.