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Abstract <p>This paper presents the development and implementation of Internet of Things (IoT) enabled smart geosynthetics as instruments for real-time slope stability assessments at urban landfill sites. The monitoring methods typically based on inclinometers and piezometers exhibit restricted capabilities for both range of observation and response speed. The proposed system transforms geotextiles by incorporating combined strain gauges with accelerometers and temperature/moisture sensors which send continuous wireless data through LoRaWAN. The strain gauge demonstrated standard LVDT instrument comparison based on an R<sup>2</sup> value of 0.97 while accelerometer measurements had an R<sup>2</sup> value of 0.93 and temperature/moisture sensor measurements reached R<sup>2</sup> value of 0.95. Strain data over 30 days in real-time showed slope stress exceeded 75 µε beyond 28 µε while wetness levels went from 20.5% to 24% during rainfall periods. Slope cracks along with displacement not alongside these recorded trends. The smart system demonstrated 99.8% data transmission success throughout its operations with high power efficiency through the use of solar energy as a power source. The results show that the smart geosynthetic system effectively detects early-stage slope instability, providing a sustainable and efficient monitoring solution for urban landfills.</p> Research highlights <p><UnorderedList Mark="Bullet"> <ItemContent> <p>Developed and field-validated an IoT-integrated smart geosynthetic system combining strain, acceleration, temperature, and moisture sensors for real-time monitoring of landfill slope stability.</p> </ItemContent> <ItemContent> <p>Achieved high measurement accuracy with strong correlation to reference instruments (R<sup>2</sup> up to 0.97) and ensured 99.8% wireless data transmission reliability using LoRaWAN and solar-powered energy harvesting.</p> </ItemContent> <ItemContent> <p>Demonstrated early-warning capability by detecting precursor strain and moisture changes linked to rainfall events, enabling proactive response and improving sustainable landfill management.</p> </ItemContent> </UnorderedList></p>

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Smart geosynthetics for real-time monitoring of slope stability in urban landfills

  • 期刊论文

  • 发表日期：

    2026年6月22日

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  • Abdullah A Al-Homidy,
  • Anas Al-Roubaiey

摘要

Abstract

This paper presents the development and implementation of Internet of Things (IoT) enabled smart geosynthetics as instruments for real-time slope stability assessments at urban landfill sites. The monitoring methods typically based on inclinometers and piezometers exhibit restricted capabilities for both range of observation and response speed. The proposed system transforms geotextiles by incorporating combined strain gauges with accelerometers and temperature/moisture sensors which send continuous wireless data through LoRaWAN. The strain gauge demonstrated standard LVDT instrument comparison based on an R2 value of 0.97 while accelerometer measurements had an R2 value of 0.93 and temperature/moisture sensor measurements reached R2 value of 0.95. Strain data over 30 days in real-time showed slope stress exceeded 75 µε beyond 28 µε while wetness levels went from 20.5% to 24% during rainfall periods. Slope cracks along with displacement not alongside these recorded trends. The smart system demonstrated 99.8% data transmission success throughout its operations with high power efficiency through the use of solar energy as a power source. The results show that the smart geosynthetic system effectively detects early-stage slope instability, providing a sustainable and efficient monitoring solution for urban landfills.

Research highlights

Developed and field-validated an IoT-integrated smart geosynthetic system combining strain, acceleration, temperature, and moisture sensors for real-time monitoring of landfill slope stability.

Achieved high measurement accuracy with strong correlation to reference instruments (R2 up to 0.97) and ensured 99.8% wireless data transmission reliability using LoRaWAN and solar-powered energy harvesting.

Demonstrated early-warning capability by detecting precursor strain and moisture changes linked to rainfall events, enabling proactive response and improving sustainable landfill management.