• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model
【2h】

Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model

机译：基于实时监测和取水模型的不稳定斜坡变形预测

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

With increased urbanization, accidents related to slope instability are frequently encountered in construction sites. The deformation and failure mechanism of a landslide is a complex dynamic process, which seriously threatens people’s lives and property. Currently, prediction and early warning of a landslide can be effectively performed by using Internet of Things (IoT) technology to monitor the landslide deformation in real time and an artificial intelligence algorithm to predict the deformation trend. However, if a slope failure occurs during the construction period, the builders and decision-makers find it challenging to effectively apply IoT technology to monitor the emergency and assist in proposing treatment measures. Moreover, for projects during operation (e.g., a motorway in a mountainous area), no recognized artificial intelligence algorithm exists that can forecast the deformation of steep slopes using the huge data obtained from monitoring devices. In this context, this paper introduces a real-time wireless monitoring system with multiple sensors for retrieving high-frequency overall data that can describe the deformation feature of steep slopes. The system was installed in the Qili connecting line of a motorway in Zhejiang Province, China, to provide a technical support for the design and implementation of safety solutions for the steep slopes. Most of the devices were retained to monitor the slopes even after construction. The machine learning Probabilistic Forecasting with Autoregressive Recurrent Networks (DeepAR) model based on time series and probabilistic forecasting was introduced into the project to predict the slope displacement. The predictive accuracy of the DeepAR model was verified by the mean absolute error, the root mean square error and the goodness of fit. This study demonstrates that the presented monitoring system and the introduced predictive model had good safety control ability during construction and good prediction accuracy during operation. The proposed approach will be helpful to assess the safety of excavated slopes before constructing new infrastructures.
机译：随着城市化增加，建筑工地经常遇到与坡度不稳定相关的事故。滑坡的变形和失效机制是一种复杂的动态过程，这严重威胁着人们的生命和财产。目前，通过使用物联网（物联网）技术来实时监测滑坡变形以及人工智能算法来预测变形趋势的预测和预警。但是，如果在施工期间发生坡度故障，建设者和决策者发现它有效地利用IOT技术来监测紧急情况并协助提出治疗措施的挑战。此外，对于在操作期间的项目（例如，山区的高速公路），不存在公认的人工智能算法，可以使用从监控设备获得的巨大数据来预测陡坡的变形。在这种情况下，本文介绍了具有多个传感器的实时无线监控系统，用于检索可以描述陡坡变形特征的高频整体数据。该系统安装在中国浙江省一条高速公路的齐齐连接线，为陡坡的设计和实施提供技术支持。即使在施工之后，大多数设备也被保留以监测斜坡。基于时间序列和概率预测的基于自回归经常性网络（Deepar）模型的机器学习概率预测被引入项目，以预测斜率位移。通过平均绝对误差，根均方误差和适合的良好验证了取釜模型的预测准确性。该研究表明，在施工期间，所提出的监测系统和引入的预测模型在施工期间具有良好的安全控制能力和良好的预测准确性。在建造新基础设施之前，建议的方法将有助于评估挖掘斜坡的安全性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号