Prediction of Uniaxial Compressive Strength and Modulus of Elasticity in Calcareous Mudstones Using Neural Networks, Fuzzy Systems, and Regression Analysis

Mahdiabadi, N.; Khanlari, Gholamreza

doi:10.3311/ppci.13035

Cited by 20 publications

(7 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Umrao et al [ 38 ] used the ANFIS procedure to predict the UCS and E of various sedimentary rocks. Mahdiabadi and Khanlari [ 39 ] used multiple linear regression, multiple nonlinear regression, multilayer perceptron, and adaptive neuro-fuzzy inferences systems for UCS and E prediction. They used 80 samples that were recovered from calcareous mudstones of the Aghajari Formation located in Tehran, Iran, as the main data set.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Method for the Prediction of the Index Mechanical Properties and Strength Parameters of Marlstone

2022

View full text Add to dashboard Cite

The index mechanical properties, strength, and stiffness parameters of rock materials (i.e., uniaxial compressive strength, c, ϕ, E, and G) are critical factors in the proper geotechnical design of rock structures. Direct procedures such as field surveys, sampling, and testing are used to estimate these properties, and are time-consuming and costly. Indirect methods have gained popularity in recent years due to their time-saving and highly accurate results, which are comparable to those obtained through direct approaches. This study presents a procedure for establishing a deep learning-based predictive model (DNN) for obtaining the geomechanical characteristics of marlstone samples that have been recovered from the South Pars region of southwest Iran. The model was implemented on a dataset resulting from the execution of numerous geotechnical tests and the evaluation of the geotechnical parameters of a total of 120 samples. The applied model was verified by using benchmark learning classifiers (e.g., Support Vector Machine, Logistic Regression, Gaussian Naïve Bayes, Multilayer Perceptron, Bernoulli Naïve Bayes, and Decision Tree), Loss Function, MAE, MSE, RMSE, and R-square. According to the results, the proposed DNN-based model led to the highest accuracy (0.95), precision (0.97), and the lowest error rate (MAE = 0.13, MSE = 0.11, and RMSE = 0.17). Moreover, in terms of R2, the model was able to accurately predict the geotechnical indices (0.933 for UCS, 0.925 for E, 0.941 for G, 0.954 for c, and 0.921 for φ).

show abstract

Section: Introductionmentioning

confidence: 99%

A Deep Learning Method for the Prediction of the Index Mechanical Properties and Strength Parameters of Marlstone

2022

View full text Add to dashboard Cite

show abstract

“…Initially, the UCS was obtained mainly through laboratory uniaxial compression tests using the standards proposed by the American Society for Testing and Materials (ASTM) or the International Society for Rock Mechanics (ISRM) [7][8][9][10]. Nevertheless, the laboratory experiments of UCS have been controversial for three reasons, including time-consuming, cost-ineffective, and rock sample quality-dependent [2,[11][12][13]. Therefore, it is practically meaningful and scientifically significant to develop economical and effective but robust methods to obtain UCS to meet the needs of engineering practices and research.…”

Section: Introductionmentioning

confidence: 99%

A Kernel Extreme Learning Machine-Grey Wolf Optimizer (KELM-GWO) Model to Predict Uniaxial Compressive Strength of Rock

Zhou

Dias

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Uniaxial compressive strength (UCS) is one of the most important parameters to characterize the rock mass in geotechnical engineering design and construction. In this study, a novel kernel extreme learning machine-grey wolf optimizer (KELM-GWO) model was proposed to predict the UCS of 271 rock samples. Four parameters namely the porosity (Pn, %), Schmidt hardness rebound number (SHR), P-wave velocity (Vp, km/s), and point load strength (PLS, MPa) were considered as the input variables, and the UCS is the output variable. To verify the effectiveness and accuracy of the KELM-GWO model, extreme learning machine (ELM), KELM, deep extreme learning machine (DELM) back-propagation neural network (BPNN), and one empirical model were established and compared with the KELM-GWO model to predict the UCS. The root mean square error (RMSE), determination coefficient (R2), mean absolute error (MAE), prediction accuracy (U1), prediction quality (U2), and variance accounted for (VAF) were adopted to evaluate all models in this study. The results demonstrate that the proposed KELM-GWO model was the best model for predicting UCS with the best performance indices. Additionally, the identified most important parameter for predicting UCS is the porosity by using the mean impact value (MIV) technique.

show abstract

“…Physical characteristics of investigated rocks such as P wave velocity, dry density, porosity, and water absorption were used as model inputs, whereas UCS and E were used as output factors to build the prediction models. Mahdiabadi and Khanlari (2019) used MLR, multiple nonlinear regression, artificial neural networks, and adaptive neuro-fuzzy inference system to predict UCS and E in calcareous mudstones of the Aghajari Formation. The point loading, block punch, and cylinder punch tests were performed on samples of calcareous mudstones.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Maximum Bending Moment of Cantilever Sheet Pile Walls by Using Multiple Linear Regression Analysis

Akan

2022

Mühendislik Bilimleri Ve Tasarım Dergisi

View full text Add to dashboard Cite

Sheet pile walls are flexible retaining structures that are used to hold the horizontal soil pressures behind them, especially in situations that cause stress changes such as excavation. They are divided into two as cantilever and externally supported. Cantilever walls are used in excavations with a maximum depth of 6 meters and are supported by anchors in excavations deeper than this. Some of the values to be calculated in the design of cantilever sheet pile walls are the embedment depth and the maximum bending moment that(Mmax) will occur in the cross-section of the wall. There are various approaches in analytical methods that have complex calculation steps such as determining earth pressures, solving second and thirdorder equations. In this study, the Mmax that will occur in the cross-section of a cantilever sheet pile wall penetrates in the sand is estimated by the expressions obtained with the help of multiple linear regression(MLR) analysis. The results showed that the Mmax may not be achieved by only MLR models but with the help of polynomial equations.

show abstract

Prediction of Uniaxial Compressive Strength and Modulus of Elasticity in Calcareous Mudstones Using Neural Networks, Fuzzy Systems, and Regression Analysis

Cited by 20 publications

References 39 publications

A Deep Learning Method for the Prediction of the Index Mechanical Properties and Strength Parameters of Marlstone

A Deep Learning Method for the Prediction of the Index Mechanical Properties and Strength Parameters of Marlstone

A Kernel Extreme Learning Machine-Grey Wolf Optimizer (KELM-GWO) Model to Predict Uniaxial Compressive Strength of Rock

Estimation of the Maximum Bending Moment of Cantilever Sheet Pile Walls by Using Multiple Linear Regression Analysis

Contact Info

Product

Resources

About