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

Buildings, Год журнала: 2024, Номер 14(3), С. 591 - 591

Опубликована: Фев. 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.

Язык: 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, Год журнала: 2024, Номер 14(3), С. 615 - 615

Опубликована: Фев. 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.

Язык: Английский

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Study on the solidification/stabilization of Cu(II) and Cd(II)-contaminated soil by fly ash-red mud based geopolymer

Construction and Building Materials, Год журнала: 2025, Номер 469, С. 140515 - 140515

Опубликована: Фев. 22, 2025

Язык: Английский

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Optimizing the utilization of Metakaolin in pre-cured geopolymer concrete using ensemble and symbolic regressions

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 26, 2025

The optimization of metakaolin (MK) in pre-cured geopolymer concrete involves developing predictive models to capture the interplay various influencing factors and guide mix design for improved compressive strength sustainability. Ensemble methods symbolic regression are promising approaches this task due their complementary strengths solving challenges associated with repeated experiments laboratory. Choosing machine learning predictions over repeated, expensive, time-consuming research projects, such as optimizing utilization concrete, presents a paradigm shift how data-driven insights can revolutionize material development. integration ensemble enables researchers derive valuable optimize critical performance parameters efficiently. In work, 235 records were collected from extensive literature search different mixing ratios metakaolin-based at ages. Each record contains MK: content (kg/m3), SHS: Sodium hydroxide solution SHSM: molarity (Mole), SSS: silicate W: Extra water (not including alkaline solutions) W/S: Water Solid ratio (Total / part activator solutions + MK), Na2O/Al2O3: oxide aluminium ratio, SiO2/Al2O3: Silicon H2O/Na2O: CA/FA: Coarse Fine aggregate CAg: coarse aggregates SP: super-plasticizer PCC: 0 no pre-curing, 1 pre-curing 60 °C, 2 80 CT: Curing temperature (°C), Age: age testing (days) CS: Compressive (MPa). portioned into training set (180 records≈75%) validation (55 records≈ 25%) modeled methods. At end model metrics used evaluate models' ability Hoffman Gardener's sensitivity analysis was impact variables on mixed metakaolin. GB KNN became decisive excellent which outclassed others indicated that SHSM, SSS, W/S, Na2O/Al2O3 most influential predicted strength.

Язык: 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, Год журнала: 2024, Номер 63(1)

Опубликована: Янв. 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.

Язык: Английский

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

Buildings, Год журнала: 2024, Номер 14(4), С. 1173 - 1173

Опубликована: Апрель 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.

Язык: Английский

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Compressive strength of waste-derived cementitious composites using machine learning

REVIEWS ON ADVANCED MATERIALS SCIENCE, Год журнала: 2024, Номер 63(1)

Опубликована: Янв. 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.

Язык: Английский

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Intelligent Models for Prediction of Compressive Strength of Geopolymer Pervious Concrete Hybridized with Agro-Industrial and Construction-Demolition Wastes

Studia Geotechnica et Mechanica, Год журнала: 2024, Номер unknown

Опубликована: Сен. 26, 2024

Abstract In modern civil engineering, precisely predicting the mechanical properties of waste-modified geopolymer concrete is a vital challenge. Machine learning (ML) offers powerful tool for such predictive analysis. This article presents an experimental and python-based intelligent ML modeling study on type (GP) pervious concretes developed using agro-industrial waste products. The slag-based composite mixes were with varying dosages agro-waste, i.e., sugarcane bagasse ash (0 to 20% by weight slag) construction demolition in form recycled coarse aggregates 100% natural aggregates). aqueous solution liquid Na 2 SiO 3 NaOH pellets used as alkali activator solution. A total 13 different mix proportion designs developed, every individual sample mix, results obtained from laboratory tests. analysis was carried out compute compressive strength applying following models: Multiple Linear Regression, tuned Gradient Boost, AdaBoost, XGBoost Regressions. Further, ensemble technique that combines predictions multiple algorithms together make more accurate than any model also robust prediction through “Voting Regressor” technique. From results, models associated Ada Boost performed better. As voting regressor given higher weightage, these regressors gave best performance metrics, lower error rate compared independent models.

Язык: Английский

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Forecasting Corporate Financial Performance Using Deep Learning with Environmental, Social, and Governance Data

Electronics, Год журнала: 2025, Номер 14(3), С. 417 - 417

Опубликована: Янв. 21, 2025

In recent years, extensive research has focused on the relationship between corporate social responsibility (CSR) and financial performance. While past studies have explored this connection, they often faced challenges in quantitatively assessing effectiveness of CSR initiatives. However, advancements methodologies development Environmental, Social, Governance (ESG) measurement dimensions led to creation more robust evaluation criteria. These criteria use ESG scores as primary reference indicators for activities. This study aims utilize from InfoHub website Taiwan Stock Exchange Corporation (TSEC) benchmarks, comprising 15 items environmental (E), (S), governance (G) form predict The data cover years 2021–2022 listed companies, using return assets (ROA) equity (ROE) measures With rapid artificial intelligence applications machine learning deep (DL) proliferated across many fields. analyze remains rare. Therefore, employs models performance based performance, utilizing both classification regression approaches. Numerical results indicate that two models, Long Short-Term Memory (LSTM) Convolutional Neural Network (CNN), outperform other tasks, respectively. Consequently, techniques prove be feasible, effective, efficient alternatives predicting corporations’ metrics.

Язык: Английский

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Optimization of Mechanical Properties of Geopolymer Mortar Based on Class C Fly Ash and Silica Fume: A Taguchi Method Approach

Case Studies in Construction Materials, Год журнала: 2025, Номер 22, С. e04332 - e04332

Опубликована: Янв. 28, 2025

Язык: Английский

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