Short-Term Rockburst Damage Assessment in Burst-Prone Mines: An Explainable XGBOOST Hybrid Model with SCSO Algorithm

Rock Mechanics and Rock Engineering, Journal Year: 2023, Volume and Issue: 56(12), P. 8745 - 8770

Published: Sept. 2, 2023

Language: Английский

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Interpretable machine learning for predicting the strength of 3D printed fiber-reinforced concrete (3DP-FRC)

Journal of Building Engineering, Journal Year: 2023, Volume and Issue: 72, P. 106648 - 106648

Published: April 25, 2023

Language: Английский

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Sustainable Digital Marketing Under Big Data: An AI Random Forest Model Approach

IEEE Transactions on Engineering Management, Journal Year: 2024, Volume and Issue: 71, P. 3566 - 3579

Published: Jan. 1, 2024

Digital marketing refers to the process of promoting, selling, and delivering products or services through online platforms channels using internet electronic devices in a digital environment. Its aim is attract engage target audiences various strategies methods, driving brand promotion sales growth. The primary objective this scholarly study seamlessly integrate advanced big data analytics artificial intelligence (AI) technology into realm marketing, thereby fostering progression optimization sustainable practices. First, characteristics applications involving vast, diverse, complex datasets are analyzed. Understanding their attributes scope application essential. Subsequently, comprehensive investigation AI-driven learning mechanisms conducted, culminating development an AI random forest model (RFM) tailored for marketing. Subsequent this, leveraging real-world case enterprise X, fundamental customer collected subjected meticulous analysis. RFM model, ingeniously crafted study, then deployed prognosticate anticipated count prospective customers said enterprise. empirical findings spotlight pronounced prevalence university-affiliated individuals across diverse age cohorts. In terms occupational distribution within base, categories workers educators emerge as dominant, constituting 41% 31% demographic, respectively. Furthermore, price patrons exhibits skewed pattern, whereby bracket 0–150 encompasses 17% population, whereas range 150–300 captures notable 52%. These delineated bands collectively constitute substantial proportion, exceeding 450 embodies minority, accounting less than 20%. Notably, devised endeavor demonstrates remarkable proficiency accurately projecting forthcoming passenger volumes over seven-day horizon, significantly surpassing predictive capability logistic regression. Evidently, proffered herein excels precise anticipation counts, furnishing pragmatic foundation intelligent evolution strategies.

Language: Английский

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A deep dive into tunnel blasting studies between 2000 and 2023—A systematic review

Tunnelling and Underground Space Technology, Journal Year: 2024, Volume and Issue: 147, P. 105727 - 105727

Published: March 30, 2024

Language: Английский

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Optimized Random Forest Models for Rock Mass Classification in Tunnel Construction

Geosciences, Journal Year: 2025, Volume and Issue: 15(2), P. 47 - 47

Published: Feb. 2, 2025

The accurate prediction of rock mass quality ahead the tunnel face is crucial for optimizing construction strategies, enhancing safety, and reducing geological risks. This study developed three hybrid models using random forest (RF) optimized by moth-flame optimization (MFO), gray wolf optimizer (GWO), Bayesian (BO) algorithms to classify surrounding in real time during boring machine (TBM) operations. A dataset with 544 TBM tunneling samples included key parameters such as thrust force per cutter (TFC), revolutions minute (RPM), penetration rate (PR), advance (AR), revolution (PRev), field index (FPI), classification based on Rock Mass Rating (RMR) method. To address class imbalance, Borderline Synthetic Minority Over-Sampling Technique was applied. Performance assessments revealed MFO-RF model’s superior performance, training testing accuracies 0.992 0.927, respectively, predictors identified PR, AR, RPM. Additional validation 91 data sets confirmed reliability model unseen data, achieving an accuracy 0.879. graphical user interface also developed, enabling engineers technicians make instant reliable predictions, greatly supporting safe operational efficiency. These contribute valuable tools real-time, data-driven decision-making projects.

Language: Английский

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LGBM-based modeling scenarios to compressive strength of recycled aggregate concrete with SHAP analysis

Mechanics of Advanced Materials and Structures, Journal Year: 2023, Volume and Issue: 31(23), P. 5999 - 6014

Published: June 22, 2023

Concrete production contributes significantly to global greenhouse gas emissions, and its manufacture requires substantial natural resources. These concerns can be partly mitigated by recycling construction demolition waste as aggregates produce Recycled Aggregate (RAC). RAC has gained momentum due lower environmental impact, costs, increased sustainability. The aim of this study was advance the reasonable use recycled aggregate in concrete achieve optimal mixture ratio design. Four advanced machine learning algorithms, Support Vector Machine (SVR), Light Gradient Boosting (LGBM), Random Forest (RF), Multi-Layer Perceptron (MLP), were employed, novel optimization biogeography-based (BBO), Multi-Verse Optimizer (MVO) Gravitational Search Algorithm (GSA), integrated predict compressive strength RAC. Six potential influential factors for considered models. employed four evaluation metrics, Taylor diagrams Regression Error Characteristic plots compare model performance. result shows LGBM-based hybrid outperformed other methods, demonstrating high accuracy predicting strength. Shapley Additive Explanation (SHAP) results emphasize importance understanding interactions between various their effects on mechanical properties findings inform development more sustainable environmentally friendly building materials.

Language: Английский

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Several Tree-Based Solutions for Predicting Flyrock Distance Due to Mine Blasting

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(3), P. 1345 - 1345

Published: Jan. 19, 2023

Blasting operations involve some undesirable environmental issues that may cause damage to equipment and surrounding areas. One of them, probably the most important one, is flyrock induced by blasting, where its accurate estimation before operation essential identify blasting zone’s safety zone. This study introduces several tree-based solutions for an prediction flyrock. has been done using four techniques, i.e., decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), adaptive (AdaBoost). The modelling techniques was conducted with in-depth knowledge understanding their influential factors. mentioned factors were designed through use parametric investigations, which can also be utilized in other engineering fields. As a result, all models are capable enough blasting-induced prediction. However, predicted values obtained AdaBoost technique. Observed forecasted training testing phases received coefficients determination (R2) 0.99 0.99, respectively, confirm power this technique estimating Additionally, according results input parameters, powder factor had highest influence on flyrock, whereas burden spacing lowest impact

Language: Английский

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Data-Driven Optimized Artificial Neural Network Technique for Prediction of Flyrock Induced by Boulder Blasting

Mathematics, Journal Year: 2023, Volume and Issue: 11(10), P. 2358 - 2358

Published: May 18, 2023

One of the most undesirable consequences induced by blasting in open-pit mines and civil activities is flyrock. Furthermore, production oversize boulders creates many problems for continuation work usually imposes additional costs on project. In this way, breakage associated with throwing small fragments particles at high speed, which can lead to serious risks human resources infrastructures. Hence, accurate prediction flyrock boulder crucial avoid possible its’ environmental side effects. This study attempts develop an optimized artificial neural network (ANN) particle swarm optimization (PSO) jellyfish search algorithm (JSA) construct hybrid models anticipating distance resulting a quarry mine. The PSO JSA algorithms were used determine optimum values neurons’ weight biases connected neurons. regard, database involving 65 monitored recording was collected that comprises six influential parameters distance, i.e., hole depth, burden, angle, charge weight, stemming, powder factor one target parameter, distance. ten various ANN, PSO–ANN, JSA–ANN established estimating their results investigated applying three evaluation indices coefficient determination (R2), root mean square error (RMSE) value accounted (VAF). calculation indicators revealed R2, (0.957, 0.972 0.995) (0.945, 0.954 0.989) determined train test proposed predictive models, respectively. yielded denoted although ANN model capable PSO–ANN anticipate more accuracy. performance accuracy level estimate better compared models. Therefore, identified as superior from blasting. final, sensitivity analysis conducted showed factor, angle have impact changes.

Language: Английский

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Modelling Soil Compaction Parameters Using an Enhanced Hybrid Intelligence Paradigm of ANFIS and Improved Grey Wolf Optimiser

Mathematics, Journal Year: 2023, Volume and Issue: 11(14), P. 3064 - 3064

Published: July 11, 2023

The criteria for measuring soil compaction parameters, such as optimum moisture content and maximum dry density, play an important role in construction projects. On sites, base/sub-base soils are compacted at the optimal to achieve desirable level of compaction, generally between 95% 98% density. present technique determining parameters laboratory is a time-consuming task. This study proposes improved hybrid intelligence paradigm alternative tool method estimating density soils. For this purpose, advanced version grey wolf optimiser (GWO) called GWO (IGWO) was integrated with adaptive neuro-fuzzy inference system (ANFIS), which resulted high-performance model named ANFIS-IGWO. Overall, results indicate that proposed ANFIS-IGWO achieved most precise prediction (degree correlation = 0.9203 root mean square error 0.0635) 0.9050 0.0709) outcomes suggested noticeably superior those attained by other ANFIS models, built standard GWO, Moth-flame optimisation, slime mould algorithm, marine predators algorithm. geotechnical engineers can benefit from newly developed during design stage civil engineering MATLAB models also included parameters.

Language: Английский

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Prediction of hydrogen solubility in aqueous solution using modified mixed effects random forest based on particle swarm optimization for underground hydrogen storage

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 87, P. 373 - 388

Published: Sept. 9, 2024

Language: Английский

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