A binary classifier with applications to poorly defined engineering problems
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Modeling dynamic engineering processes using radial-Gaussian neural networks
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
| Hi-index | 0.00 |
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.Several recommendations are made for future work, aimed at further improving the performance of the system. Primarily, the work here focused on simply supported steel bridges with negligible skew. It is recommended that the technique be applied to other bridge configurations, such as skewed and pre-stressed concrete structures.