Chehri Abdellah et Saeidi Ali. (2021). IoT and Deep Learning Solutions for an Automated Crack Detection for the Inspection of Concrete Bridge Structures. Dans Human Centred Intelligent Systems. (244, p. 110-119). Smart Innovation, Systems and Technologies book series (SIST, volume 244). Singapore : Springer.
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URL officielle: http://dx.doi.org/10.1007/978-981-16-3264-8_11
Résumé
Road infrastructure is subject to increasingly demanding operating conditions. The loads stressing the bridges is increasingly high, which overloads the existing structures, many of which were designed with lower design loads. The high frequency of heavy vehicle traffic accelerates damage to the structure. Furthermore, other environmental causes (ice, de-icing salt, settling, temperature, etc.) make existing structures unsuitable. The security risk of bridges is most frequently raised by crack. The paper explored a digital and smart bridge crack system for improving the efficiency and risk factor for bridge security diagnosis. Artificial intelligence and deep learning will enhance the inspection of concrete bridge structures, and it will become the future trend of development.
Type de document: | Chapitre de livre |
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Date: | 2021 |
Lieu de publication: | Singapore |
Identifiant unique: | 10.1007/978-981-16-3264-8_11 |
Sujets: | Sciences naturelles et génie > Génie Sciences naturelles et génie > Génie > Génie informatique et génie logiciel Sciences naturelles et génie > Sciences appliquées |
Département, module, service et unité de recherche: | Départements et modules > Département des sciences appliquées > Module d'ingénierie |
Mots-clés: | concrete bridge structure, crack bridge, Internet of things, deep learning, neural network, structure de pont en béton, Internet des objets, apprentissage profond, réseau de neurones |
Déposé le: | 28 avr. 2022 12:46 |
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Dernière modification: | 28 avr. 2022 12:46 |
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