Applications of Machine Learning to Foundation Design for Transportation Structures

Abstract

This paper is part of a collection looking at the history of and future perspectives on foundation design for transportation structures. The objective of this article is to summarize relevant applications of machine learning (ML) to transportation-related matters with a focus on transportation geotechnics and, specifically, foundation design for transportation structures. Furthermore, this paper is intended to serve as an introduction to ML for readers who may not have experience in these subjects. A review of the most significant ML techniques that are likely to be of interest to the geotechnical community is presented and relevant available databases are discussed. While ML approaches have begun to be applied to geotechnical problems, their application within transportation geotechnics and foundation design is still limited. This paper emphasizes the potential of ML for solving problems in geotechnical engineering, foundation design, and other related fields. Successful applications from several sources are discussed. Furthering the use of ML within foundation design will likely require collaboration between the Standing Committees on Foundations of Bridges and Other Structures and on Artificial Intelligence and Advanced Computing. These collaborations between geotechnical engineers and experts in artificial intelligence and ML will be essential to maximize the benefits of these tools for use in foundation design.