Geo-disaster prediction with double-block mechanics based on Newton force measurement

He, Manchao; Tao, Zhigang; Gong, W. L.

doi:10.1007/s40948-016-0046-y

Cited by 15 publications

(7 citation statements)

References 6 publications

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“…erefore, the anchor cable after adding the constant resistance device is able to absorb the constant resistance energy and resist large deformation. e schematic diagram of the constant resistance device is shown in Figure 4 [20].…”

Section: Component Structure Improvementmentioning

confidence: 99%

Experimental and Optimization Study on the Sliding Force Monitoring and Early Warning System for High and Steep Slopes

Wang

Tao

He³

et al. 2020

Advances in Civil Engineering

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A series of high and steep slopes have been formed due to the deep exploitation of resources in open-pit mines across China. The stability of these high and steep slopes has become an essential factor affecting the efficient, safe, and sustainable development of deep mineral resources. Due to numerous problems such as constant resistance fluctuation and pipe jamming of the original sliding force monitoring system, leading to system failure, a series of improvements on the current monitoring systems were implemented. This specific work included a mechanical characteristics test of the anchor cable, improvement of the constant resistance structure, and measurement of the internal displacement of the slope. The communication mode and the software architecture of the system were also adjusted. This work significantly improved the overall performance of the sliding force monitoring and early warning system. The improvements performed in this research are systematically described to provide an example of good practice for other sites with similar features. The collected data show that the improved sliding force monitoring system can accurately reflect the whole process of landslide incubation. Moreover, the validity of the early warning criterion based on the sliding force is verified again using the field test.

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Section: Component Structure Improvementmentioning

confidence: 99%

Experimental and Optimization Study on the Sliding Force Monitoring and Early Warning System for High and Steep Slopes

Wang

Tao

He³

et al. 2020

Advances in Civil Engineering

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“…The yellow color in Figure 7 indicates the sliding mass, blue color indicates the steel strand, and red color indicates the energy absorbing unit and its deformation band. The monitoring equipment installed at the external end of the cable is invented by State Key Laboratory for Geo-mechanics and Deep Underground Engineering [14,15] to collect and transmit the mechanical information on the sliding surface. The internal end of cable is fixed by the cement plaster in the borehole.…”

Section: Working Principle Of New Cablementioning

confidence: 99%

Static Pull Testing of a New Type of Large Deformation Cable with Constant Resistance

Tao

Pang

Zhou

et al. 2017

Advances in Materials Science and Engineering

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A new type of energy-absorbing cable, Constant-Resistance Large Deformation cable (CRLD cable) with three different specifications, has been recently developed and tested. An effective cable should occupy the ability of absorbing deformation energy from these geodisaster loads and additionally must be able to yield with the sliding mass movements and plastic deformation over large distances at high displacement rates. The new cable mainly consists of constant-resistance casing tube and frictional cone unit that transfers the load from the slope. When experiencing a static or dynamic load and especially the load exceeding the constant resistance force (CR-F, a static friction force derived from the movement of frictional cone unit in casing tube) of CRLD cable, the frictional cone unit will move in the casing tube along the axis and absorb deformation energy, accordingly. In order to assess the performance of three different specified cables in situ, a series of field static pull tests have been performed. The results showed that the first type of CRLD cable can yield 2000 mm displacement while acting 850 kN static pull load, which is superior to that of other two types, analyzing based on the length of the displacement and the level of static pull load.

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“…Due to the complexity of landslide occurrence in terms of the geological conditions, genesis mechanism, and external influencing factors, problems related to accurate landslide warnings cannot be solved fundamentally by purely relying on deformation monitoring [14][15][16]. Numerical simulation techniques have become an effective means to solve these problems.…”

Section: Introductionmentioning

confidence: 99%

Determination of Landslide Displacement Warning Thresholds by Applying DBA-LSTM and Numerical Simulation Algorithms

Dai

et al. 2022

Applied Sciences

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Numerical simulation has emerged as a powerful technique for landslide failure mechanism analysis and accurate stability assessment. However, due to the bias of simplified numerical models and the uncertainty of geomechanical parameters, simulation results often differ greatly from the actual situation. Therefore, in order to ensure the accuracy and rationality of numerical simulation results, and to improve landslide hazard warning capability, techniques and methods such as displacement back-analysis, machine learning, and numerical simulation are combined to create a novel landslide warning method based on DBA-LSTM (displacement back-analysis based on long short-term memory networks), and a numerical simulation algorithm is proposed, i.e., the DBA-LSTM algorithm is used to invert the equivalent physical and mechanical parameters of the numerical model, and the modified numerical model is used for stability analysis and failure simulation. Taking the Shangtan landslide as an example, the deformation mechanism of the landslide was analyzed based on the field monitoring data, and subsequently, the superiority of the DBA-LSTM algorithm was verified by comparing it with DBA-BPNN (displacement back-analysis based on back-propagation neural network); finally, the stability of the landslide was analyzed and evaluated a posteriori using the warning threshold calculated by the proposed method. The analytical results show that the displacement back-analysis based on the machine learning (DBA-ML) algorithm can achieve more than 95% accuracy, and the deep learning algorithm exemplified by LSTM had higher accuracy compared to the classical BPNN algorithm, meaning that it can be used to further improve the existing intelligent inversion theory and method. The proposed method calculates the landslide’s factor of safety (FOS) before the accelerated deformation to be 1.38 and predicts that the landslide is in a metastable state after accelerated deformation rather than in failure. Compared to traditional empirical warning models, our method can avoid false warnings and can provide a new reference for research on landslide hazard warnings.

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Geo-disaster prediction with double-block mechanics based on Newton force measurement

Cited by 15 publications

References 6 publications

Experimental and Optimization Study on the Sliding Force Monitoring and Early Warning System for High and Steep Slopes

Experimental and Optimization Study on the Sliding Force Monitoring and Early Warning System for High and Steep Slopes

Static Pull Testing of a New Type of Large Deformation Cable with Constant Resistance

Determination of Landslide Displacement Warning Thresholds by Applying DBA-LSTM and Numerical Simulation Algorithms

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