Prediction of longwall mining‐induced stress in roof rock using LSTM neural network and transfer learning method

Qin, Changkun; Zhao, Wang; Zhong, Kun; Chen, Weizhong

doi:10.1002/ese3.1037

Cited by 9 publications

(3 citation statements)

References 43 publications

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“…The latter is induced by roadway excavation and timely variations. The measurement method for the in situ stress was introduced in our previous study, 33 based on which the in situ stress for the study case is listed in Table 3, where the compressive stress is negative and the tensile stress is positive. The developed stress sensor was then used to monitor the variation in mining-induced stress, and the monitoring results are shown in Figure 8.…”

Section: Background For a Study Casementioning

confidence: 99%

Overall sensing method for the three-dimensional stress of roadway via machine learning on SHM data

Tan

Chen

Gao

et al. 2023

Structural Health Monitoring

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Monitoring roof stress is essential for identifying structural anomalies and preventing disasters during underground construction. However, current sensors mainly focus on monitoring in one dimension, and it is challenging to obtain the mechanical status of the overall roof owing to the limitations of sensor numbers and the working environment. Therefore, we aimed to present an overall sensing method for the three-dimensional stress status of a roadway roof through machine learning (ML) based on limited monitoring points. First, the framework of the overall sensing method was developed, where a three-dimensional stress sensor was created to obtain the mechanical behaviours of some sensitive positions, and an ML model driven by the physical mechanism and limited monitoring data was developed to derive the overall stress situation. The developed sensor was installed in a case study, and the ML model was formulated based on the field-monitoring data. A series of experiments were conducted to derive the stress distribution of the roadway roof in the study case. Furthermore, a numerical simulation was conducted to compare the reasonability of the deduction results. The experimental results indicated that the deduction results of roof stress were reasonable, and thus the proposed sensing method is reliable.

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Section: Background For a Study Casementioning

confidence: 99%

Overall sensing method for the three-dimensional stress of roadway via machine learning on SHM data

Tan

Chen

Gao

et al. 2023

Structural Health Monitoring

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show abstract

“…In recent years, with the rapid advancement of artificial intelligence and big data technologies, machine learning methods have gained increasing attention and application in the realm of lateral spreading prediction research. Among these methods, algorithms based on machine learning have shown tremendous potential for developing data-driven predictive models [10], [11]. Traditional analytical approaches often require precise function relationships, yet complex seismic phenomena like liquefaction-induced lateral spreading are often challenging to accurately describe using simple mathematical models.…”

Section: Introductionmentioning

confidence: 99%

Prediction of liquefaction-induced lateral spreading based on Neural network

Yang,

Lin,

et al. 2024

J. vibroeng.

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In light of inherent errors associated with the existing methods for predicting lateral spreading of liquefied soil during earthquakes, a novel approach has been proposed. Based on the Newmark sliding block method, a neural network model has been trained to calculate lateral liquefaction displacement, which was achieved by compiling a substantial dataset and establishing a comprehensive seismic motion database. Taking into consideration six input features to train the sensitivity model, based on the sensitivity analysis, a predictive model for liquefaction-induced lateral spreading was developed include three parameters, moment magnitude, peak ground acceleration and yield acceleration. This model was then compared to empirical lateral spreading prediction models. The results demonstrate that this model shows notable concurrence with the existing empirical models. Additionally, using 22 well-documented cases of liquefaction-induced lateral spreading, three high-quality models were employed to predict residual shear strength of the soil. Notably, this novel model surpasses the performance of empirical liquefaction-induced lateral spreading prediction models.

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“…It was concluded that the maximum stress decreased with increasing temperature, and it increased with an increasing strain rate. These people established the constitutive models required for quantitative analysis based on experimental samples, but these have rarely been combined with CT experiments [9,[12][13][14][15][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Subarea Description and Breakage Constitutive Model of Rock Mass Based on CT Test

et al. 2022

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Based on CT experiments of the mechanical behavior of a certain sandstone, the safety zone, the damaging zone, and the fracture zone of geotechnical materials were defined, and the CT threshold standard of this zone was discussed and empirically selected. Mathematical morphological image measurement technology was used to analyze the CT information of each section, and the variation law of the sandstone CT number with the loading process and the conditions of the damaging zone was studied. According to the statistical results of the CT number of the safety zone, the damaging zone, and the fracture zone obtained at each loading stage, a statistical-damage variable based on the CT number was defined, and the equation of the damage evolution pertinent to this damage variable was established. On the basis of all these data, a constitutive model that can reflect the divisional damage-fracture of sandstone was constructed. The theoretical stress–strain curve and the measured curve were well fitted, demonstrating that the improved constitutive model could accurately reflect the failure of sandstone partitions. A qualitative analysis of CT scans and a quantitative analysis of the constitutive model were combined in this experiment, which has a certain reference value for the development of geotechnical engineering experiments.

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Prediction of longwall mining‐induced stress in roof rock using LSTM neural network and transfer learning method

Cited by 9 publications

References 43 publications

Overall sensing method for the three-dimensional stress of roadway via machine learning on SHM data

Overall sensing method for the three-dimensional stress of roadway via machine learning on SHM data

Prediction of liquefaction-induced lateral spreading based on Neural network

Subarea Description and Breakage Constitutive Model of Rock Mass Based on CT Test

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