Shear strength prediction of reinforced concrete beams using machine learning. (January 2023)

• Record Type: Journal Article
• Authors: Sandeep, M.S.; Tiprak, Koravith; Kaewunruen, Sakdirat; Pheinsusom, Phoonsak; Pansuk, Withit
• Is Part Of: Structures. Volume 47(2023)
• Page Start: 1196
• Page End: 1211
• Publication Date: 2023-01
• Subjects: Machine learning -- Shear strength prediction -- ANN -- Artificial neural network -- Shear strength; Structural engineering -- Periodicals; 624.1
• DOI: 10.1016/j.istruc.2022.11.140
• Languages: English
• ISSNs: 2352-0124

Abstract

Abstract: Recent years have witnessed a surge in the application of machine learning techniques for solving hard to solve structural engineering problems. The application of machine learning can replace the use of empirical and semi-empirical prediction models currently used in practice with highly accurate models. This paper provides a detailed discussion on the basic terminologies and concepts of commonly used machine learning algorithms for solving structural engineering problems. To provide confidence to use this method and show the potential of machine learning in accurately predicting the results of complex civil engineering problems, a comprehensive literature review on the application of machine learning in shear strength prediction is also presented. The literature review covers the application of different machine learning algorithms in predicting the shear strength of conventional concrete beams, steel fibre reinforced concrete beams, beams reinforced with FRP bars as well as high strength concrete beams. Major observations, challenges and future scope in this field are also discussed in detail. This article will be a valuable resource for individuals who are unfamiliar with machine learning yet aspire to learn more about it.