Evaluating and Predicting the Stability of Roadways in Tunnelling and Underground Space Using Artificial Neural Network-Based Particle Swarm Optimization

Zhang, Xiliang; Nguyen, Hoang; Bui, Xuan‐Nam; Le, Hong Anh; Nguyen-Thoi, Trung; Moayedi, Hossein; Mahesh, V. V.

doi:10.1016/j.tust.2020.103517

Cited by 61 publications

(13 citation statements)

References 62 publications

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“…Artificial neural networks (ANN) are a computing model inspired by the biological neural networks in animal brains. It has been used in the geotechnical engineering community for various challenges 43–50 . The architecture of ANN consists of the input layer, the output layer and several hidden layers, as demonstrated in Figure 1.…”

Section: Artificial Neural Network With Dimensionless Variablesmentioning

confidence: 99%

Three‐dimensional slope stability predictions using artificial neural networks

Meng

Mattsson

Laue

2021

Num Anal Meth Geomechanics

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To enable assess slope stability problems efficiently, various machine learning algorithms have been proposed recently. However, these developments are restricted to two‐dimensional slope stability analyses (plane strain assumption), although the two‐dimensional results can be very conservative. In this study, artificial neural networks are adopted and trained to predict three‐dimensional slope stability and a program, SlopeLab has been developed with a graphical user interface. To reduce the number of variables, groups of dimensionless parameters to express stability of slopes in classic stability charts are adopted to construct the neural network architecture. The model has been trained with a dataset from slope stability charts for fully cohesive and cohesive‐frictional soils. Furthermore, the impact of concave plan curvature on slope stability that is usually found by excavation in practice is investigated by introducing a dimensionless parameter, relative curvature radius. Slope stability analyses have been conducted with numerical calculations and the artificial neural networks are trained with dimensionless data. The performance of the trained artificial neural networks has been evaluated with the correlation coefficient (R) and root mean square error (RMSE). High accuracy has been found in all the trained models in which R > 0.999 and RMSE < 0.15. Most importantly, the proposed program can help engineers to estimate 3D effects of a slope quickly from the ratio of the factors of safety, FS3D/FS2D. When FS3D/FS2D is large (such as larger than 1.2), a 3D numerical modelling on slope stability analyses that can consider complex 3D geometry and boundary condition is advised.

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Section: Artificial Neural Network With Dimensionless Variablesmentioning

confidence: 99%

Three‐dimensional slope stability predictions using artificial neural networks

Meng

Mattsson

Laue

2021

Num Anal Meth Geomechanics

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“…This method can obtain the reasonable layout parameters α 1 and α 2 of roadway, so as to ensure the stability of surrounding rock. The basic equations of PSO algorithm are as follow (Piotr et al 2016;Zhang et al 2020):…”

Section: Pso Algorithm Processmentioning

confidence: 99%

Reasonable Layout of Roadway Based on PSO-AHP Optimization Algorithm Under Three-Dimensional Stress Field

Zhao

Cheng

Zhang

et al. 2021

Preprint

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Reasonable roadway layout is the fundamental measure to reduce the difficulty of roadway support and improve the stability of surrounding rock. The particle swarm optimization and analytic hierarchy process (PSO-AHP) algorithm of reasonable layout of roadway under the three-dimensional field was proposed, and an engineering verification was carried out in Weijiadi coal mine. The results showed that, The dip angle α1 and azimuth angle α2 affect the distribution of the deviatoric stress of the roadway surrounding rock. Under different stress field, the sensitivity of surrounding rock to α1 and α2 are different. Changing the more sensitive layout parameters can minimize the deviatoric stress and improve the stability of roadway. The PSO-AHP optimization algorithm takes the deviatoric stress as the index to calculate the layout parameters of different parts of the roadway, and then determines the optimal layout parameters through the evaluation function. The roadway of 1104 working face was arranged with the optimal parameters obtained by PSO-AHP optimization algorithm. Compared with the original layout scheme, the deviatoric stress of surrounding rock was reduced and the position of maximum deviatoric stress is transferred. The optimum layout of roadway was combined with an asymmetric support which had a good application effect.

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“…This approach focuses on the ANN training process where the metaheuristic algorithm provides a way to escape from local minimums, which is the main problem in standard gradient-based methods. Several metaheuristic algorithms have been proposed to improve the ANN training process, such as genetic algorithm [40,41], particle swarm optimization [42], and grasshopper optimization [43]. However, to improve the ANN performance, the cost function needs to balance the weight values, ANN topology, and learning parameters.…”

Section: Introductionmentioning

confidence: 99%

A Damage Detection Method Using Neural Network Optimized by Multiple Particle Collision Algorithm

et al. 2021

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A critical task of structural health monitoring is damage detection and localization. Lamb wave propagation methods have been successfully applied for damage identification in plate-like structures. However, Lamb wave processing is still a challenging task due to its multimodal and dispersive characteristics. To address this issue, data-driven machine learning approaches as artificial neural network (ANN) have been proposed. However, the effectiveness of ANN can be improved based on its architecture and the learning strategy employed to train it. The present paper proposes a Multiple Particle Collision Algorithm (MPCA) to design an optimum ANN architecture to detect and locate damages in plate-like structures. For the first time in the literature, the MPCA is applied to find damages in plate-like structures. The present work uses one piezoelectric transducer to generate Lamb wave signals on an aluminum plate structure and a linear array of four transducers to capture the scattered signals. The continuous wavelet transform (CWT) processes the captured signals to estimate the time-of-flight (ToF) that is the ANN inputs. The ANN output is the damage spatial coordinates. In addition to MPCA optimization, this paper uses a quantitative entropy-based criterion to find the best mother wavelet and the scale values. The presented experimental results show that MPCA is capable of finding a simple ANN architecture with good generalization performance in the proposed damage localization application. The proposed method is compared with the 1-dimensional convolutional neural network (1D-CNN). A discussion about the advantages and limitations of the proposed method is presented.

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Evaluating and Predicting the Stability of Roadways in Tunnelling and Underground Space Using Artificial Neural Network-Based Particle Swarm Optimization

Cited by 61 publications

References 62 publications

Three‐dimensional slope stability predictions using artificial neural networks

Three‐dimensional slope stability predictions using artificial neural networks

Reasonable Layout of Roadway Based on PSO-AHP Optimization Algorithm Under Three-Dimensional Stress Field

A Damage Detection Method Using Neural Network Optimized by Multiple Particle Collision Algorithm

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