Innovative Data-Driven Machine Learning Approaches for Predicting Sandstone True Triaxial Strength DOI Creative Commons
Rui Zhang, Jian Zhou, Zhenyu Wang

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(17), С. 7855 - 7855

Опубликована: Сен. 4, 2024

Given the critical role of true triaxial strength assessment in underground rock and soil engineering design construction, this study explores sandstone using data-driven machine learning approaches. Fourteen distinct test datasets were collected from existing literature randomly divided into training (70%) testing (30%) sets. A Multilayer Perceptron (MLP) model was developed with uniaxial compressive (UCS, σc), intermediate principal stress (σ2), minimum (σ3) as inputs maximum (σ1) at failure output. The optimized Harris hawks optimization (HHO) algorithm to fine-tune hyperparameters. By adjusting structure activation function characteristics, final made continuously differentiable, enhancing its potential for numerical analysis applications. Four HHO-MLP models different functions trained validated on set. Based comparison prediction accuracy meridian plane analysis, an high predictive meridional behavior consistent theoretical trends selected. Compared five traditional criteria (Drucker–Prager, Hoek–Brown, Mogi–Coulomb, modified Lade, Weibols–Cook), demonstrated superior performance both datasets. It successfully captured complete variation space, showing smooth continuous envelopes deviatoric planes. These results underscore model’s ability generalize across conditions, highlighting a powerful tool predicting geotechnical

Язык: Английский

Optimization of multi-pass coating for magnetic-thermal-assisted laser cladding based on data-enhanced WOA-DE-TELM and LHS-AMOPSO algorithm DOI

Jiangtao Gong,

Haiqing Li,

Helong Yu

и другие.

Surface and Coatings Technology, Год журнала: 2025, Номер 497, С. 131765 - 131765

Опубликована: Янв. 10, 2025

Язык: Английский

Процитировано

0

Reply to Discussion by Adoko on “Refined Approaches for Open Stope Stability Analysis in Mining Environments: Hybrid SVM Model with Multi‑optimization Strategies and GP Technique” Rock Mech Rock Eng, 57, 9781–9804 DOI
Shuai Huang, Jian Zhou

Rock Mechanics and Rock Engineering, Год журнала: 2025, Номер unknown

Опубликована: Фев. 2, 2025

Язык: Английский

Процитировано

0

Reliability analysis of soil slopes stabilized with piles under rainfall DOI Creative Commons
Xiangyu Ma, Yuanyuan Tao, Lu Meng

и другие.

Journal of Rock Mechanics and Geotechnical Engineering, Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0

Ground Settlement Prediction in Urban Tunnelling: Leveraging Metaheuristic-Optimized Random Forest Models DOI Creative Commons
Peixi Yang, Jian Zhou, Yulin Zhang

и другие.

Arabian Journal for Science and Engineering, Год журнала: 2025, Номер unknown

Опубликована: Март 5, 2025

Abstract With the continuous acceleration of urbanization, problem ground settlement induced by underground tunnel construction has received more and widespread attention. This study addresses challenge predicting surface subsidence in urban construction, a critical concern geotechnical engineering. Random forest (RF) models were optimized using three distinct metaheuristic algorithms: ant lion optimizer (ALO), multiverse (MVO), grasshopper optimization algorithm (GOA). The enhancements significantly improved model accuracy, as demonstrated detailed performance metrics GOA-optimized RF (GOA-RF Pop = 20) on Changsha Metro Line 3 dataset, which included 294 instances 12 feature parameters. achieved an MAE 1.3820, MAPE 181.2249, correlation coefficient 0.9273, RMSE 2.5209 training set; 2.4695, 275.2054, R value 0.8877, 4.2540 testing set. A sensitivity analysis within random framework revealed that torque (To) condition (Gc) had most significant impact subsidence, whereas influence modified dynamic penetration test (MDPT) was least pronounced. Additionally, MATLAB-based application developed App Designer module, integrating these into user-friendly GUI facilitates prediction management risks, thereby enhancing practical effectiveness engineering risk mitigation strategies.

Язык: Английский

Процитировано

0

Predicting the minimum horizontal principal stress using genetic expression programming and borehole breakout data DOI Creative Commons
Rui Zhang, Jian Zhou

Journal of Rock Mechanics and Geotechnical Engineering, Год журнала: 2024, Номер unknown

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

2

Subsurface Geological Profile Interpolation Using a Fractional Kriging Method Enhanced by Random Forest Regression DOI Creative Commons
Qi-Le Ding, Yiren Wang, Yu Zheng

и другие.

Fractal and Fractional, Год журнала: 2024, Номер 8(12), С. 717 - 717

Опубликована: Дек. 5, 2024

Analyzing geological profiles is of great importance for various applications such as natural resource management, environmental assessment, and mining engineering projects. This study presents a novel geostatistical approach subsurface profile interpolation using fractional kriging method enhanced by random forest regression. Using bedrock elevation data from 49 boreholes in area southeast China, we first use regression to predict optimize variogram parameters. We then the interpolate analyze variability. also compare proposed model with traditional methods, including linear regression, K-nearest neighbors, ordinary kriging, cross-validation metrics. The results indicate that reduces prediction errors enhances spatial reliability compared other models. MSE 25% lower than 10% kriging. In addition, execution time slightly higher findings suggest effectively captures complex relationships, offering reliable precise solution performing tasks.

Язык: Английский

Процитировано

1

Innovative Data-Driven Machine Learning Approaches for Predicting Sandstone True Triaxial Strength DOI Creative Commons
Rui Zhang, Jian Zhou, Zhenyu Wang

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(17), С. 7855 - 7855

Опубликована: Сен. 4, 2024

Given the critical role of true triaxial strength assessment in underground rock and soil engineering design construction, this study explores sandstone using data-driven machine learning approaches. Fourteen distinct test datasets were collected from existing literature randomly divided into training (70%) testing (30%) sets. A Multilayer Perceptron (MLP) model was developed with uniaxial compressive (UCS, σc), intermediate principal stress (σ2), minimum (σ3) as inputs maximum (σ1) at failure output. The optimized Harris hawks optimization (HHO) algorithm to fine-tune hyperparameters. By adjusting structure activation function characteristics, final made continuously differentiable, enhancing its potential for numerical analysis applications. Four HHO-MLP models different functions trained validated on set. Based comparison prediction accuracy meridian plane analysis, an high predictive meridional behavior consistent theoretical trends selected. Compared five traditional criteria (Drucker–Prager, Hoek–Brown, Mogi–Coulomb, modified Lade, Weibols–Cook), demonstrated superior performance both datasets. It successfully captured complete variation space, showing smooth continuous envelopes deviatoric planes. These results underscore model’s ability generalize across conditions, highlighting a powerful tool predicting geotechnical

Язык: Английский

Процитировано

0