A Study of Artificial Neural Network Model for Bridge Inspection in Chung-Hua County
|關鍵字:||類神經網路;橋樑;橋樑管理系統;Artificial Neural Network;Bridges;Bridge Management System|
Transportation system plays the most important role for the rapidly economic developing in Taiwan. In which, bridges take part in the major function in the system; hence, there are numerous bridges located among plant and mountain area of Taiwan. Therefore, the damage of bridges may result in inconvenient and distress in many points, such as social, civil, and economic concern. The issue is more essential in Taiwan since Taiwan locates in frequently natural disaster region. Hence, how to maintain these numerous bridges health in well function with low cost and less manpower is vital. Currently, a bridge management system based on the evaluation of DERU has been developed and employed in various bridge management bureaus in Taiwan. However, the locations and types of bridges are deviation county to county. This work aims to develop an artificial neural network (ANN) bridge damage detection model based on the investigation data by established DERU approach. The target of this study focus on plate or beam type bridges in plant area, says Chang-Hua County. There are 463 investigated data used in this work and divided into training and verify instances. The optimal topology of ANN model is studied first. The verified results illustrate that ANN model can accurately detect the degree of damage for both training and verification cases. The performance of ANN model is better than conventional DERU approach by comparing with detecting accuracy. The work also confirms that the feasibility of applying ANN bridge damage detection models in different locations and types of bridges.
|Appears in Collections:||Thesis|
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