Application of artificial neural network in bridge deck condition rating

Currently bridges are evaluated either a visual inspection process or structural analysis. When bridge evaluation is conducted by visual inspection a subjective rating is assigned to the bridge components with analytical evaluation. The rating is computed based on the load applied and the resistance...

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Main Author:	Bakhary, Norhisham
Format:	Thesis
Language:	English
Published:	2001
Subjects:	TG Bridge engineering
Online Access:	http://eprints.utm.my/id/eprint/12244/1/NorhishamBakharyMFKA2001.pdf
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id	my-utm-ep.12244
record_format	uketd_dc
spelling	my-utm-ep.122442018-09-27T04:06:55Z Application of artificial neural network in bridge deck condition rating 2001-08 Bakhary, Norhisham TG Bridge engineering Currently bridges are evaluated either a visual inspection process or structural analysis. When bridge evaluation is conducted by visual inspection a subjective rating is assigned to the bridge components with analytical evaluation. The rating is computed based on the load applied and the resistance capacity of bridge component visual inspection is subjective and depends primarily on the experience of the inspector in assigning the rating. analytical rating unable to represent the condition of bridge component since the rating is computed based on the load and bridge capacity only. If a relationship between analytical rating and subjective rating can be found. The estimation of bridges condition can be made only by determining the bridge analytical rating. Several attempts to correlate both methods using the conventional statistical analyses. As well as fuzzy logic have not been very succesful in providing the relationship between those rating methods. However, an attempt to utilize Artificial Neural Network (ANN) to correlate the analytical rating for railroad and bridge parameter with bridge subjective rating in Chicago has produced succesful results. This study describes the application of ANN in developing the correlation between load rating and subjective rating as well as bridge parameter for highway bridges. The subjective rating in this study is limited to deck rating only. The data provided by California Department of Transportation (CALTRAN) is utilized for training and testing session. The results obtained in the first part showed that additional variables are needed along with load rating variable to provide acceptable prediction performance. After several rounds of improvement process the best reults obtained exhibits 77% of the data used for testing are predicted within the acceptable range. Generally, this study showed that the ANN has a potential to be used to predict the subjective rating if the proper input variables are applied. 2001-08 Thesis http://eprints.utm.my/id/eprint/12244/ http://eprints.utm.my/id/eprint/12244/1/NorhishamBakharyMFKA2001.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Civil Engineering Faculty of Civil Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TG Bridge engineering
spellingShingle	TG Bridge engineering Bakhary, Norhisham Application of artificial neural network in bridge deck condition rating
description	Currently bridges are evaluated either a visual inspection process or structural analysis. When bridge evaluation is conducted by visual inspection a subjective rating is assigned to the bridge components with analytical evaluation. The rating is computed based on the load applied and the resistance capacity of bridge component visual inspection is subjective and depends primarily on the experience of the inspector in assigning the rating. analytical rating unable to represent the condition of bridge component since the rating is computed based on the load and bridge capacity only. If a relationship between analytical rating and subjective rating can be found. The estimation of bridges condition can be made only by determining the bridge analytical rating. Several attempts to correlate both methods using the conventional statistical analyses. As well as fuzzy logic have not been very succesful in providing the relationship between those rating methods. However, an attempt to utilize Artificial Neural Network (ANN) to correlate the analytical rating for railroad and bridge parameter with bridge subjective rating in Chicago has produced succesful results. This study describes the application of ANN in developing the correlation between load rating and subjective rating as well as bridge parameter for highway bridges. The subjective rating in this study is limited to deck rating only. The data provided by California Department of Transportation (CALTRAN) is utilized for training and testing session. The results obtained in the first part showed that additional variables are needed along with load rating variable to provide acceptable prediction performance. After several rounds of improvement process the best reults obtained exhibits 77% of the data used for testing are predicted within the acceptable range. Generally, this study showed that the ANN has a potential to be used to predict the subjective rating if the proper input variables are applied.
format	Thesis
qualification_level	Master's degree
author	Bakhary, Norhisham
author_facet	Bakhary, Norhisham
author_sort	Bakhary, Norhisham
title	Application of artificial neural network in bridge deck condition rating
title_short	Application of artificial neural network in bridge deck condition rating
title_full	Application of artificial neural network in bridge deck condition rating
title_fullStr	Application of artificial neural network in bridge deck condition rating
title_full_unstemmed	Application of artificial neural network in bridge deck condition rating
title_sort	application of artificial neural network in bridge deck condition rating
granting_institution	Universiti Teknologi Malaysia, Faculty of Civil Engineering
granting_department	Faculty of Civil Engineering
publishDate	2001
url	http://eprints.utm.my/id/eprint/12244/1/NorhishamBakharyMFKA2001.pdf
_version_	1747814913064042496

Application of artificial neural network in bridge deck condition rating

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