OcrV1, Main, Exploration, bibRecord, 000202

Development of hybrid optimisation method for Artificial Intelligence based bridge deterioration model - Feasibility study

Identifieur interne : 000202 ( Main/Exploration ); précédent : 000201; suivant : 000203

Development of hybrid optimisation method for Artificial Intelligence based bridge deterioration model - Feasibility study

Auteurs : Daniel Callow [Australie] ; Jaeho Lee [Australie] ; Michael Blumenstein [Australie] ; HONG GUAN [Australie] ; Yew-Chaye Loo [Australie]

Source :

Automation in construction [ 0926-5805 ] ; 2013.

RBID : Pascal:13-0173352

Descripteurs français

Pascal (Inist)
- Pont, Détérioration, Modélisation, Mode hybride, Intelligence artificielle, Algorithme génétique, Optimisation, Evaluation performance, Long terme, Réseau neuronal, Raisonnement basé sur cas, Faisabilité.
Wicri :
- topic : Intelligence artificielle.

English descriptors

KwdEn :
- Artificial intelligence, Bridges, Case based reasoning, Deterioration, Feasibility, Genetic algorithm, Hybrid mode, Long term, Modeling, Neural network, Optimization, Performance evaluation.

Abstract

Bridge Management Systems (BMSs) are a common tool for bridge management to extend the life cycle of bridge networks. However, the reliability of current BMS outcomes is doubtful. This is because: (1) Overall Condition Rating (OCR) method cannot represent individual bridge elements' condition and is unable to represent condition ratings of bridge elements in lower Condition States and due to (2) insufficient historical bridge records available. A long-term Performance Bridge (LTPB), i.e. deterioration, model is the most crucial component and decides level of reliability of long-term bridge needs. Recent development of an AI-based bridge deterioration model was undertaken to minimise these shortcomings. However, this model is computationally costly due to the process of Neural Network, generating a large data output. To improve the neural network process, optimisation is required. The hybrid optimisation method is proposed in this paper to filter out feasible condition ratings as input for long-term prediction modelling.

Affiliations:

Australie

Links toward previous steps (curation, corpus...)

to stream PascalFrancis, to step Corpus: 000056
to stream PascalFrancis, to step Curation: 000712
to stream PascalFrancis, to step Checkpoint: 000045
to stream Main, to step Merge: 000205
to stream Main, to step Curation: 000202

Le document en format XML

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<front><div type="abstract" xml:lang="en">Bridge Management Systems (BMSs) are a common tool for bridge management to extend the life cycle of bridge networks. However, the reliability of current BMS outcomes is doubtful. This is because: (1) Overall Condition Rating (OCR) method cannot represent individual bridge elements' condition and is unable to represent condition ratings of bridge elements in lower Condition States and due to (2) insufficient historical bridge records available. A long-term Performance Bridge (LTPB), i.e. deterioration, model is the most crucial component and decides level of reliability of long-term bridge needs. Recent development of an AI-based bridge deterioration model was undertaken to minimise these shortcomings. However, this model is computationally costly due to the process of Neural Network, generating a large data output. To improve the neural network process, optimisation is required. The hybrid optimisation method is proposed in this paper to filter out feasible condition ratings as input for long-term prediction modelling.</div>
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Development of hybrid optimisation method for Artificial Intelligence based bridge deterioration model - Feasibility study

Development of hybrid optimisation method for Artificial Intelligence based bridge deterioration model - Feasibility study

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri