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Study on the Prediction of Slope Failure and Early Warning Thresholds Based on Model Tests

In recent years, slope failure caused by heavy rainfall from linear precipitation bands has occurred frequently, causing extensive damage. Predicting slope failure is an important and necessary issue. A method used to predict the time of failure has been proposed, which focuses on the tertiary stage...

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Published in:Geotechnics (Basel) 2024-03, Vol.4 (1), p.1-17
Main Authors: Fukuhara, Makoto, Uchimura, Taro, Wang, Lin, Tao, Shangning, Tang, Junfeng
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creator Fukuhara, Makoto
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description In recent years, slope failure caused by heavy rainfall from linear precipitation bands has occurred frequently, causing extensive damage. Predicting slope failure is an important and necessary issue. A method used to predict the time of failure has been proposed, which focuses on the tertiary stage of the creep theory, shown as V = A/(tr − t), where V is the velocity of displacement, A is a constant, and (tr − t) is the time until failure. To verify this method, indoor model experiments and field monitoring were used to observe the behavior of surface displacement. Seven cases of laboratory experiments were conducted by changing the conditions in the model, such as materials, the thickness of the surface layer, and relative density. Then, two cases of field monitoring slope failure were examined using this method. The results show that, in the tertiary stage of creep theory, the relationship between tilt angle velocity and the time until failure can be expressed as an inversely proportional relationship. When the tilt angle velocity has reached the tertiary creep stage, it initially ranges from 0.01°/h to 0.1°/h; when near failure, it was found to be over 0.1°/h, so, combining this with previous research results, this is a reasonable value as a guideline for an early warning threshold.
doi_str_mv 10.3390/geotechnics4010001
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subjects creep
early warning
multi-layer shear model
slope failure
tilt sensor
title Study on the Prediction of Slope Failure and Early Warning Thresholds Based on Model Tests
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