Revealing the nature of concrete materials using soft computing models

Abstract

The durability of materials has long been a hot topic in civil engineering. This paper used a new approach to foster current research in two aspects: identifying important properties of concrete and simulating the transport of chloride in concrete. Machine learning methods have proven to have remarkable abilities in approximating complex functions, which provides a potential tool for realizing the above ideas. This paper used deep neural networks to identify important parameters of concrete via datasets and simulate the transport process by integrating physical constraints into models. Results showed that physical constraints could be well combined with deep neural networks to accurately identify materials’ properties (relative error < 1%) and simulate the aggression of chloride. This study also demonstrated that machine learning models could be interpretable. In addition, this paper also explored the influence of time intervals of detection datasets, the size of datasets, and the noise in datasets (-5%~5%) on the convergence of identification. Results showed that short time intervals and big datasets can accelerate convergence. This method can resist the influence of noise in datasets. In addition, with exact physical constraints, this method can predict the performance of materials without the assistance of any dataset.

Duan, Kangkang

PhD Student of Civil Engineering

Kangkang is a PhD student at the University of British Columbia. Previously, he received his BSc degree in Highway and Bridge Engineering from Southeast University (Mao Yisheng Class) in 2019. Then, he received his MASc degree in Civil Engineering from Southeast University in 2022. His research interests include robotics, artificial intelligence, AR/VR, and physical machine learning.