English  |  正體中文  |  简体中文  |  Items with full text/Total items : 65317/65317 (100%)
Visitors : 21364294      Online Users : 388
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version


    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/78066


    Title: 子計畫:降雨引發淺層崩塌之可犧牲式監測元件研發(III);Development of Sacrificed Sensors for Rainfall-Triggered Shallow Landslide Monitoring Iii
    Authors: 鐘志忠林遠見
    Contributors: 國立中央大學土木工程學系
    Keywords: 淺層滑動;全球定位系統;時域反射技術;開放式平台;無線感測網路;機器學習;Sallow landslide;Global Positioning System (GPS);Time Domain Reflectometry (TDR);Open source platform;Wireless sensor network (WSN);Machine learning
    Date: 2018-12-19
    Issue Date: 2018-12-20 10:54:40 (UTC+8)
    Publisher: 科技部
    Abstract: 本計畫根據2015坡地災害領域徵求課題2-2(大規模淺/深崩塌地調查、評估、分析、監測、及警戒基準)之第三項研究內容(坡地崩塌災害個案調查、水文地質分析及破壞機制探討、潛勢及規模評估),提出本子計畫第第三年度申請。總計畫鑑於淺層崩塌研究須結合由上到下的降雨、邊坡表面檢監測與地下條件來進行整體評估分析,藉由蒐集與分析大數據崩塌資料(子計畫一),並發展高精度降雨引發淺層崩塌模擬機制(子計畫二)與區域性降雨情境模擬機制(子計畫五),再結合淺層崩塌之非破壞檢測技術(子計畫三)與本子計畫監測技術(子計畫四),企圖提出淺層崩塌降雨警戒基準評估方法之建立與應用,以期能降低坡地災害衝擊。國內土石流相關監測研發已有一定基礎可供淺層崩塌監測參考,從依循Open Geospatial Consortium (OGC)各項標準規範建置之資訊平台,以及現場無線感測網路(Wireless Sensor Network, WSN),包含感測平台以及對應之感測器,但如何取得有效監測數據以供淺層崩塌研究,還需後續驗證。近來基於Arduino與相關單晶片微電腦有其開放原始碼、價格優惠,以及易於操作等優勢,配合以微機電(MEMS)之感測元件,已可大量應用於自動控制領域,但於實務坡地災害應用有待探討;另外單頻Global Positioning System (GPS) 定位晶片也因為價格優勢,國外研究以短基線測站比對方式,將其位移量測精度提升以適用於坡地滑動監測;時域反射法(Time Domain Reflectometry, TDR)可用於地滑以及坡地含水量自動化監測使用,目前相關研究正開發低成本之TDR單晶片主機,搭配低工耗長距離無線傳輸技術如LoRa,應可達到可犧牲式監測建構,因此本研究預計可基於低成本TDR主機以及現有TDR分層坡地含水量貫入器之成果,修訂為以WSN基礎之TDR單一坡地含水量剖面貫入器,藉以降低既有TDR建置成本。綜合上述限制與需求,本研究於第一年度開發以Arduino為基礎之監測主機,除可延伸以往土石流相關監測方法之外,並進行單頻GPS定位精度評析;第二年度建構可犧牲式TDR坡地含水量剖面貫入器,並進行示範場址實地評估,配合坡地質點影像分析(Particle Tracking Velocimetry, PTV)或數位影像相關係數法(Digital Image Correlat ;To assess the mechanism and have alert of rainfall-triggered shallow landslides, the main project aims to propose an early-warning system accounting for rainfall events in practice. The main project firstly involves understanding of shallow landslide events using Big Data methodology (Sub-project 1). Rainfall-triggered shallow landslide simulation is then developed based on high resolution physical based model (Sub-project 2) and local area rainfall forecasting (Sub-project 5). Related landslide non-destructive inspection (Sub-project 3) and field monitoring with sacrificed sensing system (Sub-project 4) would support the characterization of landslides. The ultimate goal of main project is to reduce the impact due to rainfall-triggered shallow landslides. Although some specific sensors and systems for debris flow monitoring have been developed and revealed based on the OGC (Open Geospatial Consortium) and WSN (Wireless Sensor Network) protocols in Taiwan, on-site applications on rainfall-triggered shallow landslides need further verifications. A new generation open-source based system, such as Arduino, is famous for its cost-effective and easy-manipulated. Arduino is also capable to integrate numerous MEMS sensors as a WSN node. Therefore, in the first year research plan, a cost-down single frequency Global Positioning System (GPS) chip is implemented and examined for the enough resolution to record displacements of landslides. In the second year, a Time Domain Reflectometry (TDR) device, which combined with Low Power Wide Area Network, such as LoRa WAN, will be probably obtained for multi-node soil water content profiling through a physical modeling. This physical testing will combine well known Particle Tracking Velocimetry (PTV) or Digital Image Correlation (DIC) method, for capability verification. In the third year, the aforementioned sacrificed sensors and systems are expected to be employed for rainfall-triggered shallow landslide monitoring in field. Corresponding results would support the decision making collaborated with the prediction from the non-linear, time series, or machine learning methods.
    Relation: 財團法人國家實驗研究院科技政策研究與資訊中心
    Appears in Collections:[土木工程學系 ] 研究計畫

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML60View/Open


    All items in NCUIR are protected by copyright, with all rights reserved.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback  - 隱私權政策聲明