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Damage Identification of Arch Bridge Based on Artificial Neural Network

Author: GeLinRui
Tutor: ZhaoRenDa
School: Southwest Jiaotong University
Course: Bridge and Tunnel Engineering
Keywords: Damage Identification Radial basis function neural network Zhen die state Curvature Mode
CLC: U441.4
Type: Master's thesis
Year: 2008
Downloads: 152
Quote: 4
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Abstract


With the rapid development of China's transport undertakings , large bridge structures are emerging, the increasing number of old and new bridges . To ensure that people's lives and property safety, quickly and efficiently identify the bridge structure may occur and damage the site of injury , to grasp the health status of a bridge operator , bridge engineering research field is the current hot issues. Arch -specific mechanical properties and structural superiority , so that in the construction of large-span bridges has broad prospects for development, however , the domestic large-span arch bridge safety evaluation problem has not been studied in depth . Therefore, how to allow economic and technical conditions, a reasonable assessment of the safety of large-span arch bridge , is an urgent and significant issue. Based on neural network in structural damage identification existing achievements , and BP neural network, RBF neural network performance analysis , proposed RBF neural network is used to bridge damage identification research, application methods are given step . Based on the study abroad lot about structural damage identification and neural network based on the data , according to the bridge structure damage identification and neural network development prospects, the ball River Bridge in the background , using the finite element program ANSYS and MATLAB Modal Analysis theory and RBF neural network used in combination arch bridge damage identification , while achieving the location of the injury and damage identification , focus on the single components of the bridge structure damage, injury two members , three members injury damage cases into three categories , namely, using the vibration frequency , vibration -die state , curvature mode three kinds of indicators as a neural network input parameters , collecting samples of each damage state data , RBF neural network model to establish a bridge damage identification studies . Studies show that RBF neural network can be used to identify structural damage location and arch injury .

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CLC: > Transportation > Road transport > Bridges and Culverts > Structural principles, structural mechanics > Bridge strength and fatigue damage
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