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Introduction to ‘Artificial intelligence in failure analysis of transportation infrastructure and materials'

Yue Hou Orcid Logo, Qiao Dong Orcid Logo, Dawei Wang Orcid Logo, Jenny Liu Orcid Logo

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Volume: 381, Issue: 2254

Swansea University Author: Yue Hou Orcid Logo

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DOI (Published version): 10.1098/rsta.2022.0177

Abstract

Transportation infrastructures, including roads, bridges, tunnels, stations, airports and subways, play fundamental roles in modern society. Engineering failures of transportation infrastructures may result in significant damage to the public. The traditional methods are to monitor, store and analys...

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Published in: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
ISSN: 1364-503X 1471-2962
Published: The Royal Society 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa63916
first_indexed 2023-07-19T13:26:48Z
last_indexed 2024-11-25T14:13:06Z
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spelling 2024-03-21T13:54:08.5373573 v2 63916 2023-07-19 Introduction to ‘Artificial intelligence in failure analysis of transportation infrastructure and materials' 92bf566c65343cb3ee04ad963eacf31b 0000-0002-4334-2620 Yue Hou Yue Hou true false 2023-07-19 ACEM Transportation infrastructures, including roads, bridges, tunnels, stations, airports and subways, play fundamental roles in modern society. Engineering failures of transportation infrastructures may result in significant damage to the public. The traditional methods are to monitor, store and analyse the information during the infrastructure and material design, testing, construction, numerical simulations, evaluation, operation, maintenance and preservation, using mechanistic-based, material-based and statistics-based approaches. In recent decades, artificial intelligence (AI) has drawn the attention of many researchers and has been used as a powerful tool to understand and analyse the engineering failures in transportation infrastructure and materials. AI has the advantages of conveniently characterizing infrastructure materials in multi-scale, extracting failure information from images and cloud points, evaluating performance from the signals of sensors, predicting the long-term performance of infrastructure based on big data and optimizing infrastructure maintenance strategies, etc. In the future, AI techniques will be more effective and promising for data collection, transmission, fusion, mining and analysis, which will help engineers quickly detect, analyse and finally prevent the engineering failures of transportation infrastructure and materials. This theme issue presents the latest developments of AI in failure analysis of transportation infrastructure and materials. Journal Article Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 381 2254 The Royal Society 1364-503X 1471-2962 Transportation infrastructure, failure analysis, artificial intelligence 4 9 2023 2023-09-04 10.1098/rsta.2022.0177 http://dx.doi.org/10.1098/rsta.2022.0177 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2024-03-21T13:54:08.5373573 2023-07-19T14:24:28.4344595 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Yue Hou 0000-0002-4334-2620 1 Qiao Dong 0000-0001-7461-9226 2 Dawei Wang 0000-0003-1064-3715 3 Jenny Liu 0000-0002-3840-1438 4 63916__28151__a308cf39ff86452d9a62b806b5688f9c.pdf 63916.pdf 2023-07-19T14:26:42.2821931 Output 290702 application/pdf Version of Record true © 2023 The Authors. Published by the Royal Society. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/
title Introduction to ‘Artificial intelligence in failure analysis of transportation infrastructure and materials'
spellingShingle Introduction to ‘Artificial intelligence in failure analysis of transportation infrastructure and materials'
Yue Hou
title_short Introduction to ‘Artificial intelligence in failure analysis of transportation infrastructure and materials'
title_full Introduction to ‘Artificial intelligence in failure analysis of transportation infrastructure and materials'
title_fullStr Introduction to ‘Artificial intelligence in failure analysis of transportation infrastructure and materials'
title_full_unstemmed Introduction to ‘Artificial intelligence in failure analysis of transportation infrastructure and materials'
title_sort Introduction to ‘Artificial intelligence in failure analysis of transportation infrastructure and materials'
author_id_str_mv 92bf566c65343cb3ee04ad963eacf31b
author_id_fullname_str_mv 92bf566c65343cb3ee04ad963eacf31b_***_Yue Hou
author Yue Hou
author2 Yue Hou
Qiao Dong
Dawei Wang
Jenny Liu
format Journal article
container_title Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
container_volume 381
container_issue 2254
publishDate 2023
institution Swansea University
issn 1364-503X
1471-2962
doi_str_mv 10.1098/rsta.2022.0177
publisher The Royal Society
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
url http://dx.doi.org/10.1098/rsta.2022.0177
document_store_str 1
active_str 0
description Transportation infrastructures, including roads, bridges, tunnels, stations, airports and subways, play fundamental roles in modern society. Engineering failures of transportation infrastructures may result in significant damage to the public. The traditional methods are to monitor, store and analyse the information during the infrastructure and material design, testing, construction, numerical simulations, evaluation, operation, maintenance and preservation, using mechanistic-based, material-based and statistics-based approaches. In recent decades, artificial intelligence (AI) has drawn the attention of many researchers and has been used as a powerful tool to understand and analyse the engineering failures in transportation infrastructure and materials. AI has the advantages of conveniently characterizing infrastructure materials in multi-scale, extracting failure information from images and cloud points, evaluating performance from the signals of sensors, predicting the long-term performance of infrastructure based on big data and optimizing infrastructure maintenance strategies, etc. In the future, AI techniques will be more effective and promising for data collection, transmission, fusion, mining and analysis, which will help engineers quickly detect, analyse and finally prevent the engineering failures of transportation infrastructure and materials. This theme issue presents the latest developments of AI in failure analysis of transportation infrastructure and materials.
published_date 2023-09-04T14:32:06Z
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