Machine Learning Approach to Model Rock Strength: Prediction and Variable Selection with Aid of Log Data

Miah, Mohammad Islam; Ahmed, Salim; Zendehboudi, Sohrab; Butt, Stephen

doi:10.1007/s00603-020-02184-2

Cited by 57 publications

(16 citation statements)

References 101 publications

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“…This approach enables us to learn from a sufficiently dense dataset, and then configure a black-boxtype prediction model in order to solve the problems in the form of a closed simple equation. The ANN approach has been used to identify various rock parameters from several empirical tests in the field of rock engineering (e.g., Yang and Zhang, 1997;Mert et al, 2011;Ocak and Seker, 2012;Gholami et al, 2013;Miah et al, 2020;Mohamad Ali Ridho et al, 2021). In addition, the soft computing of the bearing capacity of foundations on rock masses using the ANN approach has also been presented by a few researchers (Ziaee et al, 2015;Alavi and Sadrossadat, 2016;Millán et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks for Predicting the Stability of Rectangular Tunnels in Hoek–Brown Rock Masses

Keawsawasvong

Seehavong

Ngamkhanong

2022

Front. Built Environ.

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An artificial neural network (ANN) model for predicting the stability of rectangular tunnels in rock masses based on the Hoek–Brown (HB) failure criterion is presented in this study. Since the safety assessment of the tunnel stability is one critical issue for civil engineers during the construction, it is very important to develop a reliable and accurate stability analysis of such problems. The finite element limit analysis (FELA) with the HB failure criterion is used to develop the numerical upper and lower bound solutions of the problem of rectangular tunnels in rock masses. A novel machine learning-aided prediction of this problem is then developed based on the datasets of the numerical bound solutions obtained from the FELA. The inputs consist of six dimensionless parameters including the cover-depth ratio of tunnels, the width ratio of tunnels, the normalized uniaxial compressive strength, the geological strength index, the mi parameter, and the degree of disturbance of rock masses. The results show that the optimal ANN models provide very great accuracy in predicting the stability of the rectangular tunnels based on the HB failure criterion. The solutions will provide a prompt assessment of tunnel stability in rock masses for geotechnical engineers during the construction of rock tunnels.

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Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks for Predicting the Stability of Rectangular Tunnels in Hoek–Brown Rock Masses

Keawsawasvong

Seehavong

Ngamkhanong

2022

Front. Built Environ.

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“…Several studies have explored ML methods, such as artificial neural networks (ANNs), support vector machines (SVM), and convolutional neural networks (CNNs) for predicting rock strength parameters, 29,30 locating seismic events within faults, 31 monitoring rock failure events in mining, 32 and for crack patterns recognition in rocks 33 (see the cited references around each topic). Miah et al 29 applied ANNs and SVMs to predict the unconfined compression strength of rocks based on field log data and identified the most relevant predictors.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of the post‐failure behavior of rocks: Combining artificial intelligence and acoustic emission sensing

Yousefpour

Pouragha

2022

Num Anal Meth Geomechanics

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Acoustic emission (AE) reading is among the most common methods for monitoring the behavior of brittle materials such as rock and concrete. This study uses discrete element method (DEM) simulations to explore the correlations between the pre-failure AE readings with the post-failure behavior and residual strength of rock masses. The deep learning (DL) method based on long short-term memory (LSTM) algorithms has been applied to generate predictive models based on the data from DEM simulations of biaxial compression. The dataset has been populated by varying interparticle friction while keeping bond cohesion constant. Various configurations of the LSTM algorithm were evaluated considering different scenarios for input features (strain, stress, and AE energy records) and a range of values for the key hyperparameters. The prime AI models show promising accuracy in predicting residual strength decay with strain based on pre-failure patterns in AE readings. The results indicate that the pre-failure AE indeed encapsulates information about the developing failure mechanisms and the post-failure response in rocks, which can be captured through artificial intelligence.

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“…, 2021; Yang et al. , 2021; Miah et al. , 2020), as well as identification of uncertainty soil parameters (Jin et al.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, machine learning (ML) has emerged as a new alternative approach to modeling complex practical problems. The application of ML overcomes the limitations of conventional physics-based models (Zhang et al, 2020;Liu and Wu, 2019) and offers great potential in the prediction of settlements and landslides (Wang et al, 2020;Chen et al, 2019), soil properties (Duan et al, 2021;Yang et al, 2021;Miah et al, 2020), as well as identification of uncertainty soil parameters (Jin et al, 2020;Yin et al, 2017) in geotechnical engineering. Nevertheless, the idea of these traditional machine learning applications is to apply experimental or computationally generated data to create surrogate models.…”

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confidence: 99%

Physics-informed neural networks for consolidation of soils

Zhang

Lan²,

Li³

et al. 2022

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PurposePrediction of excess pore water pressure and estimation of soil parameters are the two key interests for consolidation problems, which can be mathematically quantified by a set of partial differential equations (PDEs). Generally, there are challenges in solving these two issues using traditional numerical algorithms, while the conventional data-driven methods require massive data sets for training and exhibit negative generalization potential. This paper aims to employ the physics-informed neural networks (PINNs) for solving both the forward and inverse problems.Design/methodology/approachA typical consolidation problem with continuous drainage boundary conditions is firstly considered. The PINNs, analytical, and finite difference method (FDM) solutions are compared for the forward problem, and the estimation of the interface parameters involved in the problem is discussed for the inverse problem. Furthermore, the authors also explore the effects of hyperparameters and noisy data on the performance of forward and inverse problems, respectively. Finally, the PINNs method is applied to the more complex consolidation problems.FindingsThe overall results indicate the excellent performance of the PINNs method in solving consolidation problems with various drainage conditions. The PINNs can provide new ideas with a broad application prospect to solve PDEs in the field of geotechnical engineering, and also exhibit a certain degree of noise resistance for estimating the soil parameters.Originality/valueThis study presents the potential application of PINNs for the consolidation of soils. Such a machine learning algorithm helps to obtain remarkably accurate solutions and reliable parameter estimations with fewer and average-quality data, which is beneficial in engineering practice.

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Machine Learning Approach to Model Rock Strength: Prediction and Variable Selection with Aid of Log Data

Cited by 57 publications

References 101 publications

Application of Artificial Neural Networks for Predicting the Stability of Rectangular Tunnels in Hoek–Brown Rock Masses

Application of Artificial Neural Networks for Predicting the Stability of Rectangular Tunnels in Hoek–Brown Rock Masses

Prediction of the post‐failure behavior of rocks: Combining artificial intelligence and acoustic emission sensing

Physics-informed neural networks for consolidation of soils

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