| 摘要: |
| 为及时发现排水管道安全隐患,准确掌握管道状况,结合极限学习机（extreme learning machine,ELM）神经网络和管道闭路电视（closed circuit television,CCTV）检测,建立一个数据驱动的排水管道缺陷诊断模型.采用遗传算法（genetic algorithm,GA）优化ELM神经网络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ELM模型输出不稳定、分类精度偏低的问题.以上海市洋山保税港区排水管道破裂、渗漏等主要结构性缺陷的诊断为例,对GA-ELM模型进行仿真分析,并与ELM模型诊断结果进行对比.结果表明,GA-ELM模型能够更好地识别管道缺陷,获得更佳的分类性能,参数优化提高ELM模型的拟合能力和泛化能力,可应用于城市排水管道状况评价,为排水管网养护计划和修复计划的制订提供技术依据. |
| 关键词: 排水管道 缺陷诊断 极限学习机 遗传算法 神经网络 CCTV检测 |
| DOI：10.11918/201908056 |
| 分类号:TU992 |
| 文献标识码:A |
| 基金项目:国家重点研发计划项目(2016YFC0,7YFC0803300) |
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| Defect diagnosis of urban drainage pipelines based on GA optimized ELM neural network |
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ZHENG Maohui1,LIU Shaofei1,2
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(1.Shanghai Institute of Disaster Prevention and Relief, Tongji University, Shanghai 200092, China; 2.College of Civil Engineering, Tongji University, Shanghai 200092, China)
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| Abstract: |
| To detect the potential risks of drainage pipelines and accurately grasp the pipeline conditions, a data-driven defect diagnosis model was established by combining optimized extreme learning machine (ELM) neural network and closed circuit television (CCTV) inspection. Genetic algorithm (GA) was adopted to optimize the input weight matrix and the hidden layer offset of ELM neural network, which helps to solve the problems of unstable output and low classification accuracy of ELM neural network caused by random generation of network parameters. Taking the drainage pipeline dataset from Yangshan Free Trade Port Area in Shanghai as an example, the proposed GA-ELM model was conducted to identify and diagnose major structural defects, such as pipe rupture, disconnect, and leakage. The results of the GA-ELM model were compared with those of ELM model on the same dataset. It shows that the GA-ELM model achieved better classification performance by utilizing same neuron nodes in the hidden layer, and the optimization of parameters improved the fitting capability and generalization ability of the ELM model. Therefore, the proposed method is applicable to defect diagnosis and evaluation of urban drainage pipes and can provide a technical basis for the formulation of drainage network maintenance plan and repair plan. |
| Key words: drainage pipeline defect diagnosis extreme learning machine (ELM) genetic algorithm (GA) neural network CCTV inspection |
