Machine learning models to predict the tunnel wall convergence

Transportation Geotechnics, Год журнала: 2023, Номер 41, С. 101022 - 101022

Опубликована: Май 16, 2023

Язык: Английский

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Performance Evaluation of Rockburst Prediction Based on PSO-SVM, HHO-SVM, and MFO-SVM Hybrid Models

Mining Metallurgy & Exploration, Год журнала: 2023, Номер unknown

Опубликована: Фев. 3, 2023

Язык: Английский

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Decision tree models for the estimation of geo-polymer concrete compressive strength

Mathematical Biosciences & Engineering, Год журнала: 2023, Номер 21(1), С. 1413 - 1444

Опубликована: Янв. 1, 2023

<abstract> <p>The green concretes industry benefits from utilizing gel to replace parts of the cement in concretes. However, measuring compressive strength geo-polymer (CSGPoC) needs a significant amount work and expenditure. Therefore, best idea is predicting CSGPoC with high level accuracy. To do this, base learner super machine learning models were proposed this study anticipate CSGPoC. The decision tree (DT) applied as learner, random forest extreme gradient boosting (XGBoost) techniques are used system. In regard, database was provided involving 259 data samples, which four-fifths considered for training model one-fifth selected testing models. values fly ash, ground-granulated blast-furnace slag (GGBS), Na2SiO3, NaOH, fine aggregate, gravel 4/10 mm, 10/20 water/solids ratio, NaOH molarity input estimate evaluate reliability performance (DT), XGBoost, (RF) models, 12 evaluation metrics determined. Based on obtained results, highest degree accuracy achieved by XGBoost mean absolute error (MAE) 2.073, percentage (MAPE) 5.547, Nash–Sutcliffe (NS) 0.981, correlation coefficient (R) 0.991, R² 0.982, root square (RMSE) 2.458, Willmott's index (WI) 0.795, weighted (WMAPE) 0.046, Bias (SI) 0.054, p 0.027, relative (MRE) -0.014, a²⁰ 0.983 MAE 2.06, MAPE 6.553, NS 0.985, R 0.993, 0.986, RMSE 2.307, WI 0.818, WMAPE 0.05, SI 0.056, 0.028, MRE -0.015, 0.949 model. By importing set into trained 0.8969, 0.9857, 0.9424 DT, RF, respectively, show superiority estimation. conclusion, capable more accurately than DT RF models.</p> </abstract>

Язык: Английский

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A true triaxial strength criterion for rocks by gene expression programming

Journal of Rock Mechanics and Geotechnical Engineering, Год журнала: 2023, Номер 15(10), С. 2508 - 2520

Опубликована: Март 30, 2023

Rock strength is a crucial factor to consider when designing and constructing underground projects. This study utilizes gene expression programming (GEP) algorithm-based model predict the true triaxial of rocks, taking into account influence rock genesis on their mechanical behavior during building process. A criterion based GEP for igneous, metamorphic magmatic rocks was obtained by training using collected data. Compared modified Weibols-Cook criterion, Mohr-Coulomb Lade exhibits superior prediction accuracy performance. The has better performance in R2, RMSE MAPE data set used this study. Furthermore, shows greater stability predicting across different types. existing genetic (GP) model, proposed achieves more accurate predictions variation (σ1) with intermediate principal stress (σ2). Finally, Sobol sensitivity analysis technique, effects parameters three criteria are analysed. In general, terms both results.

Язык: Английский

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Application of the Improved POA-RF Model in Predicting the Strength and Energy Absorption Property of a Novel Aseismic Rubber-Concrete Material

Materials, Год журнала: 2023, Номер 16(3), С. 1286 - 1286

Опубликована: Фев. 2, 2023

The application of aseismic materials in foundation engineering structures is an inevitable trend and research hotspot earthquake resistance, especially tunnel engineering. In this study, the pelican optimization algorithm (POA) improved using Latin hypercube sampling (LHS) method Chaotic mapping (CM) to optimize random forest (RF) model for predicting performance a novel rubber-concrete material. Seventy uniaxial compression tests seventy impact were conducted quantify material performance, i.e., strength energy absorption properties four other artificial intelligence models generated compare predictive with proposed hybrid RF models. evaluation results showed that LHSPOA-RF has best prediction among all property concrete both training testing phases (R2: 0.9800 0.9108, VAF: 98.0005% 91.0880%, RMSE: 0.7057 1.9128, MAE: 0.4461 0.7364; R2: 0.9857 0.9065, 98.5909% 91.3652%, 0.5781 1.8814, 0.4233 0.9913). addition, sensitive analysis indicated rubber cement are most important parameters properties, respectively. Accordingly, POA-RF not only proven as effective predict materials, but also provides new idea assessing performances field

Язык: Английский

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Hybrid Random Forest-Based Models for Earth Pressure Balance Tunneling-Induced Ground Settlement Prediction

Applied Sciences, Год журнала: 2023, Номер 13(4), С. 2574 - 2574

Опубликована: Фев. 16, 2023

Construction-induced ground settlement is a serious hazard in underground tunnel construction. Accurate prediction has great significance ensuring the surface building’s stability and human safety. To that end, 148 sets of data were collected from Singapore Circle Line rail traffic project containing seven defining parameters to create database for predicting settlement. These are depth (H), advance rate (AR), EPB earth pressure (EP), mean SPTN value soil crown (Sm), water content layer (MC), modulus elasticity (E), grout used injecting into tail void (GP). Three hybrid models consisting random forest (RF) three types meta-heuristics, Ant Lion Optimizier (ALO), Multi-Verse Optimizer (MVO), Grasshopper Optimization Algorithm (GOA), developed predict Furthermore, absolute error (MAE), percentage (MAPE), coefficient determination (R2) root square (RMSE) assess predictive performance constructed The evaluation results demonstrated GOA-RF with population size 10 achieved most outstanding capability indices MAE (Training set: 2.8224; Test 2.3507), MAPE 40.5629; 38.5637), R2 0.9487; 0.9282), RMSE 4.93; 3.1576). Finally, sensitivity analysis indicated MC, AR, Sm, GP have significant impact on based model.

Язык: Английский

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Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor

Sensors, Год журнала: 2024, Номер 24(4), С. 1285 - 1285

Опубликована: Фев. 17, 2024

This research addresses the paramount issue of enhancing safety and health conditions in underground mines through selection optimal sensor technologies. A novel hybrid MEREC-CoCoSo system is proposed, integrating strengths MEREC (Method for Eliciting Relative Weights) Combined Compromise Solution (CoCoSo) methods. The study involves a three-stage framework: criteria discernment, weight determination using MEREC, prioritization framework. Fifteen ten sensors were identified, comprehensive analysis, including MEREC-based determination, led to “Ease Installation” as most critical criterion. Proximity identified choice, followed by biometric sensors, gas temperature humidity sensors. To validate effectiveness proposed model, rigorous comparison was conducted with established methods, VIKOR, TOPSIS, TODIM, ELECTRE, COPRAS, EDAS, TRUST. encompassed relevant metrics such accuracy, sensitivity, specificity, providing understanding model’s performance relation other methodologies. outcomes this comparative analysis consistently demonstrated superiority model accurately selecting best ensuring mining. Notably, exhibited higher accuracy rates, increased improved specificity compared alternative These results affirm robustness reliability establishing it state-of-the-art decision-making framework mine safety. inclusion these actual enhances clarity credibility our research, valuable insights into superior existing main objective develop robust mines, focus on conditions. seeks identify prioritize context strives contribute mining industry offering structured effective approach selection, prioritizing operations.

Язык: Английский

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Decision intelligence-driven predictive modelling of air quality index in surface mining

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 133, С. 108399 - 108399

Опубликована: Апрель 15, 2024

Язык: Английский

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Decision Intelligence-Based Predictive Modelling of Hard Rock Pillar Stability Using K-Nearest Neighbour Coupled with Grey Wolf Optimization Algorithm

Processes, Год журнала: 2024, Номер 12(4), С. 783 - 783

Опубликована: Апрель 13, 2024

Pillar stability is of paramount importance in ensuring the safety underground rock engineering structures. The pillars directly influences structural integrity mine and mitigates risk collapses or accidents. Therefore, assessing pillar crucial for safe, productive, reliable, profitable mining processes. This study developed application decision intelligence-based predictive modelling hard structures using K-Nearest Neighbour coupled with grey wolf optimization algorithm (KNN-GWO). Initially, a substantial dataset consisting 236 different cases was collected from seven projects. gathered by considering five significant input variables, namely width, height, width/height ratio, uniaxial compressive strength, average stress. Secondly, original level has been classified into three types: failed, unstable, stable, based on pillar’s instability mechanism failure process. Thirdly, several visual relationships were established order to ascertain correlation between variables corresponding level. Fourthly, entire database randomly divided training testing 70:30 sampling method. Moreover, (KNN-GWO) model predict mining. Lastly, performance suggested evaluated accuracy, precision, recall, F1-score, confusion matrix. findings proposed offer superior benchmark accurately predicting pillars. it recommended employ intelligence models effectively prioritise measures improve efficiency operational processes, management, decision-making related

Язык: Английский

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Evaluation and Interpretation of Blasting-Induced Tunnel Overbreak: Using Heuristic-Based Ensemble Learning and Gene Expression Programming Techniques

Rock Mechanics and Rock Engineering, Год журнала: 2024, Номер 57(9), С. 7535 - 7563

Опубликована: Май 14, 2024

Язык: Английский

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