Prediction of Compressive Strength of Geopolymer Concrete Landscape Design: Application of the Novel Hybrid RF–GWO–XGBoost Algorithm

Buildings, Journal Year: 2024, Volume and Issue: 14(3), P. 591 - 591

Published: Feb. 22, 2024

Landscape geopolymer concrete (GePoCo) with environmentally friendly production methods not only has a stable structure but can also effectively reduce environmental damage. Nevertheless, GePoCo poses challenges its intricate cementitious matrix and vague mix design, where the components their relative amounts influence compressive strength. In response to these challenges, application of accurate applicable soft computing techniques becomes imperative for predicting strength such composite matrix. This research aimed predict using waste resources through novel ensemble ML algorithm. The dataset comprised 156 statistical samples, 15 variables were selected prediction. model employed combination RF, GWO algorithm, XGBoost. A stacking strategy was implemented by developing multiple RF models different hyperparameters, combining outcome predictions into new dataset, subsequently XGBoost model, termed RF–XGBoost model. To enhance accuracy errors, algorithm optimized hyperparameters resulting in RF–GWO–XGBoost proposed compared stand-alone models, hybrid GWO–XGBoost system. results demonstrated significant performance improvement strategies, particularly assistance exhibited better effectiveness, an RMSE 1.712 3.485, R2 0.983 0.981. contrast, (RF XGBoost) lower performance.

Language: Английский

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

Mathematical Biosciences & Engineering, Journal Year: 2023, Volume and Issue: 21(1), P. 1413 - 1444

Published: Jan. 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>

Language: Английский

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

Sensors, Journal Year: 2024, Volume and Issue: 24(4), P. 1285 - 1285

Published: Feb. 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.

Language: Английский

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Strength Reduction Due to Acid Attack in Cement Mortar Containing Waste Eggshell and Glass: A Machine Learning-Based Modeling Study

Buildings, Journal Year: 2024, Volume and Issue: 14(1), P. 225 - 225

Published: Jan. 14, 2024

The present study utilized machine learning (ML) techniques to investigate the effects of eggshell powder (ESP) and recycled glass (RGP) on cement composites subjected an acidic setting. A dataset acquired from published literature was employed develop learning-based predictive models for mortar’s compressive strength (CS) decrease. Artificial neural network (ANN), K-nearest neighbor (KNN), linear regression (LR) were chosen modeling. Also, RreliefF analysis performed relevance variables. total 234 data points train/test ML algorithms. Cement, sand, water, silica fume, superplasticizer, powder, 90 days CS considered as input outcomes research showed that could be applied evaluate reduction percentage in composites, including ESP RGP, after being exposed acid. Based R2 values (0.87 ANN, 0.81 KNN, 0.78 LR), well assessment variation between test anticipated errors (1.32% 1.57% 1.69% it determined accuracy ANN model superior KNN LR. sieve diagram exhibited a correlation amongst predicted target results. suggested RGP significantly influenced loss samples with scores 0.26 0.21, respectively. research, approach suitable predicting mortar environments, thereby eliminating lab testing trails.

Language: 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, Journal Year: 2024, Volume and Issue: 57(9), P. 7535 - 7563

Published: May 14, 2024

Language: Английский

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Dynamic prediction and optimization of tunneling parameters with high reliability based on a hybrid intelligent algorithm

Information Fusion, Journal Year: 2024, Volume and Issue: unknown, P. 102705 - 102705

Published: Sept. 1, 2024

Language: Английский

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Prediction of Flyrock Distance in Surface Mining Using a Novel Hybrid Model of Harris Hawks Optimization with Multi-strategies-based Support Vector Regression

Natural Resources Research, Journal Year: 2023, Volume and Issue: 32(6), P. 2995 - 3023

Published: Sept. 4, 2023

Language: Английский

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Exploring the viability of AI-aided genetic algorithms in estimating the crack repair rate of self-healing concrete

REVIEWS ON ADVANCED MATERIALS SCIENCE, Journal Year: 2024, Volume and Issue: 63(1)

Published: Jan. 1, 2024

Abstract As a potential replacement for traditional concrete, which has cracking and poor durability issues, self-healing concrete (SHC) been the research subject. However, conducting lab trials can be expensive time-consuming. Therefore, machine learning (ML)-based predictions aid improved formulations of concrete. The aim this work is to develop ML models that could analyze forecast rate healing cracked area (CrA) bacteria- fiber-containing SHC. These were constructed using gene expression programming (GEP) multi-expression (MEP) tools. discrepancy between expected desired results, statistical tests, Taylor’s diagram, R 2 values additional metrics used assess models. A SHapley Additive exPlanations (SHAP) approach was evaluate input attributes highly relevant. With = 0.93, MAE 0.047, MAPE 12.60%, RMSE 0.062, GEP produced somewhat worse than MEP ( 0.033, 9.60%, 0.044). Bacteria had an indirect (negative) relationship with CrA SHC, while fiber direct (positive) association, according SHAP study. study might help researchers companies figure out how much each raw material needed SHCs. generate test SHC compositions based on bacteria polymeric fibers.

Language: Английский

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Refined Approaches for Open Stope Stability Analysis in Mining Environments: Hybrid SVM Model with Multi-optimization Strategies and GP Technique

Rock Mechanics and Rock Engineering, Journal Year: 2024, Volume and Issue: 57(11), P. 9781 - 9804

Published: July 11, 2024

Language: Английский

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A comparative analysis of hybrid RF models for efficient lithology prediction in hard rock tunneling using TBM working parameters

Acta Geophysica, Journal Year: 2024, Volume and Issue: 72(3), P. 1847 - 1866

Published: April 15, 2024

Language: Английский

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