Peak shear strength prediction for discontinuities between two different rock types using a neural network approach

Wu, Qiong; Xu, Yanjun; Tang, Huiming; Fang, Kun; Jiang, Yaofei; Liu, Chaoyuan; Wang, Xiaohan

doi:10.1007/s10064-018-1290-x

Cited by 22 publications

(5 citation statements)

References 40 publications

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“…FA is a widely used hyperparameter optimization algorithm for ML models, which can reduce the multiwhich is de ned as the output parameter; the joint roughness coe cient (JRC), compressive strength (σc), friction angle (φb), and normal stress (σn) are de ned as the input parameters. The effects of these parameters on the rock mass joint shear strength have been con rmed in previous studies(Renaud et al 2019, Wu et al 2019, Hasanipanah et al 2021a…”

supporting

confidence: 56%

Interpreting the effects of firefly algorithm (FA) to optimize varying machine learning models for the prediction of rock joint shear strength

Wang

Qi²,

et al. 2023

Preprint

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To propose an effective and accurate model for the prediction of the shear strength of rock mass joint, the present study focuses on the comparison of different machine learning (ML) models, including the support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbors (KNN), and mixed logistic regression (MLR) models. The firefly algorithm (FA) was employed to tune the hyper-parameters of the ML algorithms, using the input parameters of the joint roughness, compressive strength, normal stress, and friction angle. The prediction performance showed that the developed model can effectively and reliably tune the hyper-parameters of the ML algorithm and arrive at the optimal structure to estimate the shear strength of the rock mass joint. Among the five ML algorithms aiming for the estimation of the shear strength, the root mean square error (RMSE) values (the training set is 0.08 and the testing set is 0.1854) of the SVM model are the lowest, and the correlation coefficient (R) values (the training set is 0.9861 and the testing set is 0.9457) are the highest, and there is no over-fitting in the prediction process. Response analysis shows that normal stress is the most influential coefficient affecting the rock mass joint shear strength, while compressive stress is the least.

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supporting

confidence: 56%

Interpreting the effects of firefly algorithm (FA) to optimize varying machine learning models for the prediction of rock joint shear strength

Wang

Qi²,

et al. 2023

Preprint

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“…Based on the literature data, the multivariate adaptive regression splines model has the best prediction performance in comparison with other models. To evaluate the stability of a rock slope with interlayered rocks, Wu et al [18] predict the peak shear strength by a neural network approach. It also considers the effect of joint wall strength combination, normal stress, and joint roughness.…”

Section: Introductionmentioning

confidence: 99%

Back Analysis of Surrounding Rock Parameters in Pingdingshan Mine Based on BP Neural Network Integrated Mind Evolutionary Algorithm

Zhang

Yin

et al. 2022

Mathematics

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The mechanical parameters of surrounding rock are an essential basis for roadway excavation and support design. Aiming at the difficulty in obtaining the mechanical parameters of surrounding rock and large experimental errors, the optimized BP neural network model is proposed in this paper. The mind evolutionary algorithm can adequately search the optimal initial weights and thresholds, while the neural network has the advantage of strong nonlinear prediction ability. So, the optimized BP neural network model (MEA-BP model) takes advantage of the two models. It can not only avoid the local extreme value problem but also improve the accuracy and reliability of the prediction results. Based on the orthogonal test method and finite element analysis method, training samples and test samples are established. The nonlinear relationship between rock mechanical parameters and roadway deformation is established by the BP model and MEA-BP model, respectively. The importance analysis of the three input variables shows that the ∆D is the most important input variable, while ∆BC has the smallest impact. The comparison of prediction performance between the MEA-BP model and BP model demonstrates that the optimized initial weights and thresholds can improve the accuracy of prediction value. Finally, the MEA-BP model has been well applied to predicting the mechanical parameter for the surrounding rock in the Pingdingshan mine area, which proves the accuracy and reliability of the optimized model.

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“…e discontinuities have a significant influence on the stability, deformability, strength, and percolation characteristics of rock mass [1][2][3][4][5]. Characteristics of discontinuity and discontinuity sets are commonly inferred from discontinuity trace parameters such as trace intensity, length, and density.…”

Section: Introductionmentioning

confidence: 99%

“…Several methods have been developed to estimate intensity on two-dimensional exposure. Zeeb et al [16] reclassified the methods to acquire aforementioned geometric parameters into these: (1) scanline sampling (e.g., Priest and Hudson [17]), (2) window sampling (e.g., Paul [18]), and (3) circular estimator method (e.g., Mauldon et al [19]). A few researchers have performed investigations on the method of estimating trace intensity in two-dimensional exposures.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Estimating Rock Discontinuity Trace Intensity Based on Rectangular Sampling Windows

Huo

Tang

et al. 2020

Advances in Civil Engineering

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Trace intensity is defined as mean total trace length of discontinuities per unit area, which is an important geometric parameter to describe fracture networks. The probability of each trace appearing in the sampling surface is different since discontinuity orientation has a scatter and is probabilistically distributed, so this factor should be taken into account in trace intensity estimation. This paper presents an approach to estimate the two-dimensional trace intensity by considering unequal appearing probability for discontinuities sampled by rectangular windows. The estimation method requires the number of discontinuities intersecting the window, the appearing probability of discontinuities with both ends observed, one end observed, and both ends censored, and the mean trace length of discontinuities intersecting the window. The new estimator is validated by using discontinuity data from an outcrop in Wenchuan area in China. Similarly, circular windows are used along with Mauldon’s equation to calculate trace intensity using discontinuity trace data of the same outcrop as a contrast. Results indicate that the proposed new method based on rectangular windows shows close accuracy and less variability than that of the method based on circular windows due to the influence of finite sample size and the variability of location of the window and has advantage in application to sampling surfaces longer in one direction than in the other such as tunnel cross sections and curved sampling surfaces such as outcrops that show some curvature.

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Peak shear strength prediction for discontinuities between two different rock types using a neural network approach

Cited by 22 publications

References 40 publications

Interpreting the effects of firefly algorithm (FA) to optimize varying machine learning models for the prediction of rock joint shear strength

Interpreting the effects of firefly algorithm (FA) to optimize varying machine learning models for the prediction of rock joint shear strength

Back Analysis of Surrounding Rock Parameters in Pingdingshan Mine Based on BP Neural Network Integrated Mind Evolutionary Algorithm

A New Approach for Estimating Rock Discontinuity Trace Intensity Based on Rectangular Sampling Windows

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