| 灰色-小波神经网络支持下对地铁工程沉降变形的预测 |
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| 引用本文： | 姜刚,李举,陈盟,周佳薇.灰色-小波神经网络支持下对地铁工程沉降变形的预测[J].测绘通报,2019,0(5):60-63. |
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| 作者姓名： | 姜刚 李举 陈盟 周佳薇 |
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| 作者单位： | 长安大学地质工程与测绘学院,陕西 西安710064;西部矿产资源与地质工程教育部重点实验室,陕西 西安710064;长安大学地质工程与测绘学院,陕西 西安,710064;西安科技大学测绘科学与技术学院,陕西 西安,710054 |
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| 基金项目： | 国家自然科学基金（41502277）；矿山地质灾害成灾机理与防控重点实验室开放基金（2017KF06） |
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| 摘 要： | 变形监测是安全化工程施工和管理的重要内容,贯穿于项目的设计、施工和运行,对监测的沉降数据进行处理,并预测沉降量,提前对工程作出安全预警,有很重要的实际意义。本文基于GM(1,1)灰色模型、小波分析和神经网络结合的相关理论,借助Matlab软件编程,建立了灰色-小波神经网络变形预测网络模型。结合工程实例,将建立的变形预测网络模型应用于累积沉降量观测数据,结果表明组合模型具有很稳定的预测效果,比单独的GM(1,1)灰色模型预测准确度高,且训练样本越多,预测越符合实际情况。
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| 关 键 词： | 变形监测 GM(1 1)灰色模型 小波神经网络 变形预测 地铁沉降 |
| 收稿时间： | 2018-12-20 |
Prediction of settlement and deformation of underground based on gray-distributed wavelet neural network model |
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| JIANG Gang,LI Ju,CHEN Meng,ZHOU Jiawei.Prediction of settlement and deformation of underground based on gray-distributed wavelet neural network model[J].Bulletin of Surveying and Mapping,2019,0(5):60-63. |
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| Authors: | JIANG Gang LI Ju CHEN Meng ZHOU Jiawei |
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| Institution: | 1. Institute of geological engineering and surveying, Chang'an University, Xi'an 710064, China;
2. Key Laboratory of Western China's Mineral Resources and Geological Engineering, Ministry of Education, Xi'an 710064, China;
3. College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China |
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| Abstract: | Deformation monitoring is an important part of the safety engineering construction and management, and it runs through the design, construction and operation of the project. It is of great practical significance to process the monitored settlement data, predict the settlement amount, and make early warning of the safety of the project. Based on the GM (1,1) grey model, wavelet analysis and neural network combination of related theories, using Matlab programming, this paper establishes a gray-wavelet neural network deformation prediction network model. Combined with engineering examples, the established deformation prediction network model is applied to the accumulated settlement observation data. The results show that the combined model has a very stable forecasting effect and is more accurate than the single GM(1,1) gray model. The more training samples, the better the fitting effect and the prediction is more in line with the actual situation. |
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| Keywords: | deformation monitoring GM(1 1) gray model wavelet neural network deformation prediction subway settlement |
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