Machine learning in structural design: an opinionated review
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Published version
Author(s)
Malaga Chuquitaype, Christian
Type
Journal Article
Abstract
The prominence gained by Artificial Intelligence (AI) over all aspects of human activity today cannot be overstated. This technology is no newcomer to structural engineering, with logic-based AI systems used to carry out design explorations as early as the 1980’s. Nevertheless, the advent of low-cost data collection and processing capabilities have imprinted new impetus and a degree of ubiquity to AI-based engineering solutions. This review paper ends by posing the question of how long will the human engineer be needed in structural design. However, the paper does not aim to address this question, not least because all such predictions have a history of going wrong. Instead, the paper assumes throughout as valid the claim that the need for human engineers in conventional design practice has its days numbered. In order to build the case towards the final question, the paper starts with a general description of the currently available AI frameworks and their Machine Learning (ML) sub-classes. The paper then proceeds to review a selected number of studies on the application of AI in structural engineering design. A discussion of specific challenges and future needs is presented with emphasis on the much exalted roles of ’engineering intuition’ and ’creativity’. Finally, the conclusion section of the paper compiles the findings and outlines the challenges and future research directions.
Date Issued
2022-02-09
Date Acceptance
2022-01-13
Citation
Frontiers in Built Environment, 2022, 8
ISSN
2297-3362
Publisher
Frontiers Media
Journal / Book Title
Frontiers in Built Environment
Volume
8
Copyright Statement
© 2022 Málaga-Chuquitaype. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
License URL
Publication Status
Published
Article Number
ARTN 815717