首页|基于三维探地雷达和PSO-ELM的地下病害体识别方法——以成都市金牛区为例

基于三维探地雷达和PSO-ELM的地下病害体识别方法——以成都市金牛区为例

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准确探明城市道路地下病害体类别,是防止道路塌陷的关键.三维探地雷达是目前最常用的道路病害探测技术,但其数据解释还是以人工解释为主,存在工作量大、识别准确率低等缺点.本文以成都市金牛区为例,在地下病害体频谱特征分析的基础上,提出了一种改进粒子群优化算法(PSO)的极限学习机(ELM)病害体自动预测模型.首先,优选最大峰值振幅、最大波谷振幅、振幅方差、峭度因子、均方值、频谱方差及频谱均值7个特征参数作为模型输入,采用改进粒子群算法对极限学习机模型进行参数寻优;然后利用优化后的ELM模型对研究区地下病害体进行识别.结果显示,PSO-ELM模型的病害体识准确率高达92.5%,识别效果明显优于ELM模型和传统的人工图像特征识别法.
Identification method of underground disease body based on 3D ground penetrating radar and PSO-ELM:a case study of Jinniu district of Chengdu
It is the key to prevent road collapse to accurately identify the types of underground diseases in urban roads. 3D ground penetrating radar ( GPR) is the most commonly used road disease detection technology,but its data interpretation is mainly manual interpretation,which has the disadvantages of heavy workload and low recognition accuracy. Taking Jinniu district of Chengdu as an example,this paper proposes a PSO-ELM automatic disease body prediction model based on the analysis of the spectral characteristics of underground disease bodies. Seven characteristic parameters,maximum peak amplitude,maximum trough amplitude,amplitude variance,kurtosis factor,mean square value,spectrum variance and spectrum mean value,are selected as the input of the model. PSO is used to optimize the parameters of the ELM model. The optimized model is used to identify the disease body in the study area. The results show that PSO-ELM model has a disease recognition accuracy of 92.5%,which is significantly better than ELM model and traditional artificial image feature recognition method.

GPRPSOELMdiseases identification

谢小国、罗兵、黄长兵、张玉玲、杨生博

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四川省华地建设工程有限责任公司,四川 成都610036

西南科技大学环境与资源学院,四川 绵阳621010

探地雷达 粒子群优化算法 极限学习机 病害体识别

四川省科技计划四川省科技计划

2023NSFSC07842023YFS0435

2024

测绘通报
测绘出版社

测绘通报

CSTPCD北大核心
影响因子:1.027
ISSN:0494-0911
年,卷(期):2024.(8)