Journal article 489 views 66 downloads
Introduction to ‘Artificial intelligence in failure analysis of transportation infrastructure and materials'
,
Qiao Dong ,
Dawei Wang ,
Jenny Liu
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Volume: 381, Issue: 2254
Swansea University Author:
Yue Hou
-
PDF | Version of Record
© 2023 The Authors. Published by the Royal Society. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0).
Download (283.89KB)
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...
| Published in: | Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences |
|---|---|
| ISSN: | 1364-503X 1471-2962 |
| Published: |
The Royal Society
2023
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa63916 |
| 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 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. |
|---|---|
| Keywords: |
Transportation infrastructure, failure analysis, artificial intelligence |
| College: |
Faculty of Science and Engineering |
| Funders: |
Swansea University |
| Issue: |
2254 |