建筑承压脆弱支撑点定位算法研究与仿真

Building Confined Weak Point Location Algorithms and Simulation

建筑物的承压性检测一直是建筑学领域研究的热点问题之一。传统的建筑承压检测方法都是计算较大的承压脆弱面积,很难精确到点,主要是因为信号分布范围较大,弱化了信号的关联性。为了解决这一问题,提出一种建筑承压脆弱支撑点定位算法,通过对区域进行承压信息的计算,代入神经网络,利用神经网络对小区域搜索的能力,对粒子群进行优化,增强对小区域计算的寻优能力,保证建筑承压脆弱支撑区域进一步缩小,缩小定位范围。仿真结果表明:该方法对建筑承压脆弱点计算的定位效果较好,精度较高。

Building confined sex detection has been the hotspot in research of architecture is one of the problems.Traditional architectural confined detection method is calculation large confined fragile area,it is difficult to accurately to point,mainly because signal distribution range is larger,weakening the signal of relevance.In order to solve this problem,proposed based on particle swarm optimization architecture confined weak point positioning algorithm,based on area of confined information calculation,substituting neural network,using the neural network to small area search ability,to the particle swarm optimization,to increase the calculation of small area optimization ability,ensure the construction confined fragile support area further narrowing and narrow scope of positioning.The simulation results show that the method of building confined weak point calculation orientation effect is good,the accuracy is higher.