利用B-TBU模型评估桥梁状态的神经网络法

鉴于目前常用的桥梁状态评估方法存在较大的人为主观性和随意性,且无法考虑历史评估数据对当前状态的影响,因此不能准确地反映出桥梁当前的真实状态,依据贝叶斯推断中考虑先验信息影响的特点,提出了一种B-TBU模型的方法,在对当前状态的评估中,考虑历史评估数据的影响,对某座桥梁近20年的状态进行重新评估.评估结果表明：采用B-TBU模型方法可大幅度提高状态评估的准确性,使桥梁各年份状态评估的准确度均提高到90%以上;同时将BP神经网络、ELM神经网络等算法初步引入B-TBU模型,对该B-TBU模型方法进行训练学习.其结果表明,采用神经网络类方法,各年份状态评估的准确度也保持在80%左右....

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Bibliographic Details
Published in江苏大学学报（自然科学版） Vol. 38; no. 4; pp. 466 - 471
Main Author 吴多 刘来君 苗如松
Format Journal Article
LanguageChinese
Published 长安大学公路学院,陕西西安,710064%西南交通大学土木工程学院,四川成都,610031 2017
Subjects
B-TBU模型
桥梁状态
状态评估
神经网络
贝叶斯推断
condition assessment
贝叶斯推断
B-TBU model
B-TBU模型
状态评估
bridge condition
神经网络
桥梁状态
Bayesian inference
neural network
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ISSN1671-7775
DOI10.3969/j.issn.1671-7775.2017.04.016

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Summary:鉴于目前常用的桥梁状态评估方法存在较大的人为主观性和随意性,且无法考虑历史评估数据对当前状态的影响,因此不能准确地反映出桥梁当前的真实状态,依据贝叶斯推断中考虑先验信息影响的特点,提出了一种B-TBU模型的方法,在对当前状态的评估中,考虑历史评估数据的影响,对某座桥梁近20年的状态进行重新评估.评估结果表明：采用B-TBU模型方法可大幅度提高状态评估的准确性,使桥梁各年份状态评估的准确度均提高到90%以上;同时将BP神经网络、ELM神经网络等算法初步引入B-TBU模型,对该B-TBU模型方法进行训练学习.其结果表明,采用神经网络类方法,各年份状态评估的准确度也保持在80%左右.
Bibliography:32-1668/N
bridge condition; Bayesian inference; B-TBU model; neural network; condition assessment
WU Duo1, LIU Laijun1, MIAO Rusong2 （1. School of Highway, Chang＇ an University, Xi＇ an, Shaanxi 710064, China; 2. School of Civil Engineering, Southwest Jiaotong University, Chengdu, Sichuan 610031, China）
The present commonly-used bridge condition assessment methods were easily influenced by human subjectivity and arbitrariness, and the impact of historical evaluation data on the current state was also neglected by these methods. To solve the problem that the current true state of bridges could not be accurately reflected, the B-TBU model method was proposed based on Bayesian inference characteristics of taking prior information influence into account. The influence of historical evaluation data on the assessment of current state was considered, and the bridge state over the past twenty years was reevaluated. The evaluation results show that the state evaluation accuracy can be significantly improved with the bridg
ISSN:1671-7775
DOI:10.3969/j.issn.1671-7775.2017.04.016