基于粒子群优化极限学习机的排水管结构状况评价

基于极限学习机(ELM)和粒子群优化(PSO)算法,建立一个新型排水管道结构性状况评价模型。采用PSO算法优化ELM中的输入权值矩阵和隐含层偏置,改善网络参数随机生成带来的分类精度偏低的问题。以上海市洋山保税港区排水管网为例,对分类器模型进行训练测试,并与ELM分类结果进行对比分析。结果表明,PSO-ELM算法以较少的隐含层神经元节点获得更高的分类精度,参数优化提高了模型拟合能力,对于城市排水管道结构性状况分类、判断具有可行性和有效性。

Structural Condition Assessment of Urban Drainage Pipes Based on Particle Swarm Optimization-Extreme Learning Machine

Structural condition assessment of drainage pipes has been a major concern for asset managers in maintaining the required performance of urban drainage systems. This paper proposed a neural network model combing extreme learning machine (ELM) and particle swarm optimization (PSO) to classify the structural condition status of urban drainage pipes. Besides, in an attempt to look for better classification performance, it used the PSO algorithm to optimize the input weight matrix and the hidden layer offset of ELM. Moreover, it validated the PSO-ELM model by using the dataset supplied from the Yangshan Bonded Port Area in Shanghai. Furthermore, it compared the predictive performance of PSO-ELM with ELM on the same dataset. The result shows that the PSO-ELM can achieve a higher classification accuracy by utilizing less neuron nodes in the hidden layer, and improve the fitting capability of ELM. The method proposed has feasibility and effectiveness for structural condition assessment of urban drainage pipes.