01893014

Component

http://www.trb.org/Main/Blurbs/182304.aspx

The objective of this article is to summarize relevant applications of machine learning to transportation-related matters with a focus on geotechnical engineering. Furthermore, this paper is intended to serve as an introduction to AI and machine learning for readers who may not have experience in these subjects. A review of the most significant machine learning techniques that could be of interest to the geotechnical community is presented. Relevant available databases are addressed, and applications of machine learning to transportation engineering within the scope of the TRB are highlighted. Applications to geotechnical issues were not found within the Transportation Research Records, nor NCHRP libraries. Nevertheless, the 2019 TRB annual meeting included a session on the topic showing the growing interest in this area. This paper emphasizes the potential of machine learning for solving problems in geotechnical engineering, foundation design, and other related fields. Successful applications from several sources are discussed. This document aims to contribute to the understanding of machine learning techniques and its use for geotechnical engineering. With the same purpose, collaboration between the Standing Committees on Foundations of Bridges and Other Structures and on Artificial Intelligence and Advanced Computing is strongly recommended.

01776554

English

pp 103-118

2021-7

Transportation Research Circular

Digital/other

Figures; References; Tables

Artificial intelligence; Bridge design; Foundation engineering; Geotechnical engineering; Machine learning

Bridges and other structures; Design; Geotechnology; Highways

TRIS, TRB, ATRI

Jun 28 2023 2:33PM

• A Compatible Swarm Intelligence Model and Construction Method to Satisfy Bridge Design and Load Rating Requirements and Considering Geotechnical Variability
• Application of Unmanned Aerial Technologies for Inspecting Pavement and Bridge Infrastructure Asset Condition
• Compendium of Service Limit State Development for Geotechnical Elements Designed by AASHTO LRFD
• Development of Deep Foundation Design for Highway Bridges in United States Considering Earthquake-Induced Liquefaction and Lateral Spread: A Synopsis
• Performance of Rocking Shallow Foundations and the Evolution of Beam-on-Nonlinear-Winkler Foundation Modeling
• Seismic Ground Motion Design Criteria for Highway Bridges from the Perspective of Design Engineers
• Structural Implications Related to Foundation Movements in AASHTO LRFD