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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Mechanical Framework for Geopolymer Gels Construction: An Optimized LSTM Technique to Predict Compressive Strength of Fly Ash-Based Geopolymer Gels Concrete

Gels, Journal Year: 2024, Volume and Issue: 10(2), P. 148 - 148

Published: Feb. 16, 2024

As an environmentally responsible alternative to conventional concrete, geopolymer concrete recycles previously used resources prepare the cementitious component of product. The challenging issue with employing in building business is absence a standard mix design. According chemical composition its components, this work proposes thorough system or framework for estimating compressive strength fly ash-based (FAGC). It could be possible construct predicting FAGC using soft computing methods, thereby avoiding requirement time-consuming and expensive experimental tests. A complete database 162 datasets was gathered from research papers that were published between years 2000 2020 prepared develop proposed models. To address relationships inputs output variables, long short-term memory networks deployed. Notably, model examined several methods. modeling process incorporated 17 variables affect CSFAG, such as percentage SiO

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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Towards a Reliable Design of Geopolymer Concrete for Green Landscapes: A Comparative Study of Tree-Based and Regression-Based Models

Buildings, Journal Year: 2024, Volume and Issue: 14(3), P. 615 - 615

Published: Feb. 26, 2024

The design of geopolymer concrete must meet more stringent requirements for the landscape, so understanding and designing with a higher compressive strength challenging. In performance prediction strength, machine learning models have advantage being accurate faster. However, only single model is usually used at present, there are few applications ensemble models, optimization processes lacking. Therefore, this paper proposes to use Firefly Algorithm (AF) as an tool perform hyperparameter tuning on Logistic Regression (LR), Multiple (MLR), decision tree (DT), Random Forest (RF) models. At same time, reliability efficiency four integrated were analyzed. was analyze influencing factors determine their ability. According experimental data, RF-AF had lowest RMSE value. value training set test 4.0364 8.7202, respectively. R 0.9774 0.8915, compared other three has stronger generalization ability accuracy. addition, molar concentration NaOH most important factors, its influence far greater than possible including content. it necessary pay attention molarity when concrete.

Language: Английский

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Analyzing chloride diffusion for durability predictions of concrete using contemporary machine learning strategies

Materials Today Communications, Journal Year: 2024, Volume and Issue: 38, P. 108543 - 108543

Published: March 1, 2024

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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Investigating the effectiveness of carbon nanotubes for the compressive strength of concrete using AI-aided tools

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 20, P. e03083 - e03083

Published: March 28, 2024

Sustainable development in the building industry can be achieved through use of versatile cementitious composites. Thus, incorporating nanoparticles into cement composites create materials with enhanced performance and numerous applications. The utilization carbon nanotubes (CNTs) construction has great promise for developing efficient solutions to establish a sustainable ecosystem diverse characteristics. However, forecasting characteristics these is significant challenge due their intricate composite structure nonlinear behavior. Designing conducting laboratory experiments on samples across multiple age groups challenging, time-consuming, costly. Moreover, there presently lack model that predict concrete's compressive strength (fc') nanoparticles. Three machine learning (ML) techniques, K-nearest neighbor (KNN), linear regression (LR), artificial neural network (ANN), were used fc' nanocomposites containing CNTs this research. A thorough database consisting 282 data entities CNTs-based concrete model's reliability was assessed using R2 test statistical error analysis. ANN had most outstanding value 0.885, while KNN LR models values 0.838 0.744, respectively. RReliefF analysis utilized evaluate primary components predicting outcomes. This research shows properties CNT-based are greatly affected by water-to-binder ratio, followed proportions coarse aggregates. ML algorithms exhibited superior generalization capabilities, suggesting potential accurate predictions properties.

Language: Английский

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Supplementary cementitious materials-based concrete porosity estimation using modeling approaches: A comparative study of GEP and MEP

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

Published: Jan. 1, 2024

Abstract Using supplementary cementitious materials in concrete production makes it eco-friendly by decreasing cement usage and the corresponding CO 2 emissions. One key measure of concrete’s durability performance is its porosity. An empirical prediction porosity high-performance with added elements goal this work, which employs machine learning approaches. Binder, water/cement ratio, slag, aggregate content, superplasticizer (SP), fly ash, curing conditions were considered as inputs database. The aim study to create ML models that could evaluate Gene expression programming (GEP) multi-expression (MEP) used develop these models. Statistical tests, Taylor’s diagram, R values, difference between experimental predicted readings metrics With = 0.971, mean absolute error (MAE) 0.348%, root square (RMSE) 0.460%, Nash–Sutcliffe efficiency (NSE) MEP provided a slightly better-fitted model improved when contrasted GEP, had 0.925, MAE 0.591%, RMSE 0.745%, NSE 0.923. water/binder conditions, content direct (positive) relationship concrete, while SP, slag an indirect (negative) association, according SHapley Additive exPlanations study.

Language: Английский

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Understanding Penetration Attenuation of Permeable Concrete: A Hybrid Artificial Intelligence Technique Based on Particle Swarm Optimization

Buildings, Journal Year: 2024, Volume and Issue: 14(4), P. 1173 - 1173

Published: April 21, 2024

Permeable concrete is a type of porous with the special function water permeability, but permeability permeable will decrease gradually due to clogging behavior arising from surrounding environment. To reliably characterize concrete, particle swarm optimization (PSO) and random forest (RF) hybrid artificial intelligence techniques were developed in this study predict coefficient optimize aggregate mix ratio concrete. Firstly, reliable database was collected established input output variables for machine learning. Then, PSO 10-fold cross-validation used hyperparameters RF model using training testing datasets. Finally, accuracy verified by comparing predicted value actual coefficients (R = 0.978 RMSE 1.3638 dataset; R 0.9734 2.3246 dataset). The proposed can provide predictions that may face trend its development.

Language: Английский

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Compressive strength of waste-derived cementitious composites using machine learning

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

Published: Jan. 1, 2024

Abstract Marble cement (MC) is a new binding material for concrete, and the strength assessment of resulting materials subject this investigation. MC was tested in combination with rice husk ash (RHA) fly (FA) to uncover its full potential. Machine learning (ML) algorithms can help formulation better MC-based concrete. ML models that could predict compressive (CS) concrete contained FA RHA were built. Gene expression programming (GEP) multi-expression (MEP) used build these models. Additionally, evaluated by calculating R 2 values, carrying out statistical tests, creating Taylor’s diagram, comparing theoretical experimental readings. When MEP GEP models, yielded slightly better-fitted model prediction performance ( = 0.96, mean absolute error 0.646, root square 0.900, Nash–Sutcliffe efficiency 0.960). According sensitivity analysis, CS most affected curing age content, then contents. Incorporating waste such as marble powder, RHA, into building reduce environmental impacts encourage sustainable development.

Language: Английский

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