神经网络在结构损伤模拟中的应用

Application of Neural Networks in Structure Damage Simulation

识别一个结构在震动状态下的变化，在结构监测中是十分重要的，神经网络就非常适用于这种目的。本文研究了使用可分析的学习样本来训练神经网络的可行性问题。神经网络从损伤状态中训练产生，然后用于诊断一个五层钢框架在一系列震动模拟中的状态。结果表明，使用神经网络可使在线结构诊断更加可行。

Identifying changes in the vibrational signatures of a structure is very important.Neural networks can be used for this purpose.This paper investigates the feasibility of using analytically generated training samples to train neural networks.These networks,trained with analytically generated states of damage,were used to diagnose damage states obtained experimentally from a series of shaking table tests of a five story steel frame.The results show that neural networks have a strang potential for making on line structural monitoring a practical reality.