A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Developments in Weigh-in-Motion Using Neural Nets
The paper reports on the latest developments of a neural network-based method of accurately estimating truck attributes (such as axle loads) from strain response readings taken from the bridge over which the truck is traveling. The approach is designed to remove the need for intrusive devices (such as tape switches) on the deck of the bridge to obtain such data so as to provide a convenient and viable means of collecting bridge loading statistics. Specifically, this paper compares the performance of three radically different types of neural network used for identifying the class of truck crossing the bridge. Of the methods considered, a binary networking system is found to be the most efficient. The paper concludes with some recommendations for further study.
Developments in Weigh-in-Motion Using Neural Nets
The paper reports on the latest developments of a neural network-based method of accurately estimating truck attributes (such as axle loads) from strain response readings taken from the bridge over which the truck is traveling. The approach is designed to remove the need for intrusive devices (such as tape switches) on the deck of the bridge to obtain such data so as to provide a convenient and viable means of collecting bridge loading statistics. Specifically, this paper compares the performance of three radically different types of neural network used for identifying the class of truck crossing the bridge. Of the methods considered, a binary networking system is found to be the most efficient. The paper concludes with some recommendations for further study.
Developments in Weigh-in-Motion Using Neural Nets
Flood, Ian (author)
Eighth International Conference on Computing in Civil and Building Engineering (ICCCBE-VIII) ; 2000 ; Stanford, California, United States
2000-08-04
Conference paper
Electronic Resource
English
Developments in Weigh-in-Motion Using Neural Nets
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