AI SearchAn application of IoT soil moisture sensor for landslide monitoring system using the experimental sandbox
Call Number: AIT Thesis no.GE-22-06 Material type:
SeriesSeries: Asian Institute of Technology. Thesis ; no. GE-22-06Publication details: Pathum Thani, Thailand : Asian Institute of Technology, 2023Description: 104 leaves : illSubject(s): Online resources: Dissertation note: Thesis (M. Eng.) - Asian Institute of Technology, 2023 Summary: In Thailand, landslides and debris flow triggered by rainfall have become more frequent in recent years (Jotisankasa & Vathananukij, 2012). Site investigation and monitoring were conducted for geotechnical analyses of slope failure. However, it is difficult to monitor the field conditions and find a solution to protect against landslides. To improve the site investigation and determine the field conditions in real-time. Furthermore, it can interpret data to create early warning system to protect and against the landslides. This research uses the Internet of Things system with computer programming that is easy to set up and uses a low-cost budget to predict and identify landslide conditions. To set up sandbox modeling, the data required for setting the model are the significant index to determine the boundary condition of the landslide, the scope of the scalable model, slope structure, and material properties for simulation. Then simulate the slope stability model in Geostudio. The volume of artificial rainfall is estimated from the historical rainfall in Phuket, Thailand. After clarifying the sand material properties and rainfall threshold, the slope angle was determined by a trial of the angle to choose the proper factor of safety. In the sandbox experiment, the rainfall threshold was set to 20 mm/day. The slope angle is 35 degrees. During the running of testing, the landslide started failure on 48 minutes at the toe of the slope. This laboratory took to observe the result of slope failure behavior and rainfall threshold of this sandbox. As a result, compared to the slope stability analysis with the sandbox experiment test, the time when the slope failure is the same with the Geostudio model at 45-48 minutes the critical safety factor is equal to 0.919. That occurred on the toe of a landslide. The calculation of the rainfall threshold from this model is 83.95 mm/day. Then create the landslide monitoring with data analytics and programming by using Python to interpret and calculate the soil moisture data by using and show monitoring visualization.
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Asian Institute of Technology Library Archives | AIT Thesis no.GE-22-06 (Browse shelf(Opens below)) | Available |
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Geotechnical and Earth Resources Engineering
Thesis (M. Eng.) - Asian Institute of Technology, 2023
In Thailand, landslides and debris flow triggered by rainfall have become more frequent in recent years (Jotisankasa & Vathananukij, 2012). Site investigation and monitoring were conducted for geotechnical analyses of slope failure. However, it is difficult to monitor the field conditions and find a solution to protect against landslides. To improve the site investigation and determine the field conditions in real-time. Furthermore, it can interpret data to create early warning system to protect and against the landslides. This research uses the Internet of Things system with computer programming that is easy to set up and uses a low-cost budget to predict and identify landslide conditions. To set up sandbox modeling, the data required for setting the model are the significant index to determine the boundary condition of the landslide, the scope of the scalable model, slope structure, and material properties for simulation. Then simulate the slope stability model in Geostudio. The volume of artificial rainfall is estimated from the historical rainfall in Phuket, Thailand. After clarifying the sand material properties and rainfall threshold, the slope angle was determined by a trial of the angle to choose the proper factor of safety. In the sandbox experiment, the rainfall threshold was set to 20 mm/day. The slope angle is 35 degrees. During the running of testing, the landslide started failure on 48 minutes at the toe of the slope. This laboratory took to observe the result of slope failure behavior and rainfall threshold of this sandbox. As a result, compared to the slope stability analysis with the sandbox experiment test, the time when the slope failure is the same with the Geostudio model at 45-48 minutes the critical safety factor is equal to 0.919. That occurred on the toe of a landslide. The calculation of the rainfall threshold from this model is 83.95 mm/day. Then create the landslide monitoring with data analytics and programming by using Python to interpret and calculate the soil moisture data by using and show monitoring visualization.
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