Rubberized geopolymer composites: A comprehensive review

Ceramics International, Год журнала: 2022, Номер 48(17), С. 24234 - 24259

Опубликована: Июнь 13, 2022

Язык: Английский

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Compressive strength of concrete material using machine learning techniques

Cleaner Engineering and Technology, Год журнала: 2023, Номер 15, С. 100661 - 100661

Опубликована: Июль 20, 2023

Significant efforts have been made to improve the strength of concrete by utilizing industrial waste like Fly Ash as a partial replacement cement in concrete. However, predicting compressive is one challenging tasks since it affected several factors such shape and size aggregates, water-cement ratio. The paper presents study on various investigation machine learning (ML) algorithms estimate (CS) containing fly ash (FA). research also aims compare accuracy different ML models, including non-ensemble models (Multiple Linear Regressor, Support Vector Regressor) ensemble (AdaBoost Random Forest Regression, XGBoost Bagging Regressor), CS with focus identifying most accurate estimation method. For this purpose, dataset 633 experimental results wide range values, ranging from 6.27 MPa 79.99 MPa, was collected existing literature validated using statistical analysis. primary input parameters for included quantities cement, fine aggregate (FA), coarse aggregates (CA), water content, percentage superplasticizer, curing days, output. Performance evaluation conducted performance indices, MAE, MSE, R2, MAPE, RMSE, a20-index, assess reliability. comparison reveals that Regressor reliable model, demonstrating highest coefficient determination (R2) 0.95, a-20 index 0.913, lowest RMSE value 3.06 MAE 2.13 while Multiple LR model least method R2 equal 0.52, 0.433, 9.40 7.68 MPa. Additionally, provide deeper insights into relationship between CS, sensitivity parametric analysis were employed, enabling comprehensive understanding impact other prediction. From study, observed age essential feature, followed water, information gain values 32.91, 23.50, 15.10, respectively. highlights effectiveness techniques, particularly accurately estimating Furthermore, offers researchers faster more cost-effective means evaluating effect estimation, avoiding need time-consuming costly studies.

Язык: Английский

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Nonlinear finite element and analytical modelling of reinforced concrete filled steel tube columns under axial compression loading

Results in Engineering, Год журнала: 2023, Номер 19, С. 101341 - 101341

Опубликована: Авг. 3, 2023

Local buckling of steel and excessive spalling concrete have necessitated the need for evaluation reinforced columns subjected to axial compression loading. Thus, this study investigates behaviour filled tube (CFST) (RCFST) RCFST under using finite element modelling (FEM) machine learning (ML) techniques. To achieve aim, a total 85 from existing studies were analysed FEM simulation. The ultimate load generated datasets was predicted various ML findings showed that columns' compressive strength, ductility, toughness improved by reducing transverse reinforcement spacing, increasing number reinforcing bars, thickness yield strength outer tube. Under loading, analysis provided an accurate assessment structural performance columns. Compared other approaches, gradient boosting exhibited best metrics with R2 RMSE 99.925% 0.00708 99.863% 0.00717 in training testing phases, respectively predict column's load. Overall, can be applied prediction CFST compression, conserving resources, time, cost investigate through laboratory testing.

Язык: Английский

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ANN-based swarm intelligence for predicting expansive soil swell pressure and compression strength

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Июнь 25, 2024

Abstract This research suggests a robust integration of artificial neural networks (ANN) for predicting swell pressure and the unconfined compression strength expansive soils ( P s UCS -ES). Four novel ANN-based models, namely ANN-PSO (i.e., particle swarm optimization), ANN-GWO grey wolf ANN-SMA slime mould algorithm) alongside ANN-MPA marine predators’ were deployed to assess -ES. The models trained using nine most influential parameters affecting -ES, collected from broader range 145 published papers. observed results compared with predictions made by metaheuristics models. efficacy all these formulated was evaluated utilizing mean absolute error (MAE), Nash–Sutcliffe (NS) efficiency, performance index ρ , regression coefficient R 2 ), root square (RMSE), ratio RMSE standard deviation actual observations (RSR), variance account (VAF), Willmott’s agreement (WI), weighted percentage (WMAPE). All developed -ES had an significantly > 0.8 overall dataset. However, excelled in yielding high values training dataset TrD testing TsD validation VdD ). model also exhibited lowest MAE 5.63%, 5.68%, 5.48% respectively. model’s revealed that exceeded 0.9 . decreased Also, yielded higher (0.89, 0.93, 0.94) comparatively low (5.11%, 5.67, 3.61%) case PSO, GWO, SMA, witnessed overfitting problem because aforementioned 0.62, 0.56, 0.58 On contrary, no significant observation recorded ANN-base tested a-20 index. For maximum points lie within ± 20% error. sensitivity as well monotonicity analyses depicted trending corroborate existing literature. Therefore, it can be inferred recently built swarm-based ANN particularly ANN-MPA, solve complexities tuning hyperparameters ANN-predicted replicated practical scenarios geoenvironmental engineering.

Язык: Английский

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Hybrid nonlinear regression model versus MARS, MEP, and ANN to evaluate the effect of the size and content of waste tire rubber on the compressive strength of concrete

Heliyon, Год журнала: 2024, Номер 10(4), С. e25997 - e25997

Опубликована: Фев. 1, 2024

Tire rubber waste is globally accumulated every year. Therefore, a solution to this problem should be found since, if landfilled, it not biodegradable and causes environmental issues. One of the most effective ways recycling those wastes or using them as replacement for normal aggregate in concrete mixture, which has high impact resistance toughness; thus, will good choice. In study, 135 data were collected from previous literature develop model prediction rubberized compressive strength; database comprised different mixture proportions, maximum size (1-40 mm), percentage (0-100%) replacing natural fine coarse aggregates among input parameters addition cement content (380-500 kg/m

Язык: Английский

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Machine learning and nonlinear finite element analysis of fiber‐reinforced polymer‐confined concrete‐steel double‐skin tubular columns under axial compression

Structural Concrete, Год журнала: 2024, Номер unknown

Опубликована: Июнь 21, 2024

Abstract Fiber‐reinforced polymer (FRP)‐confined double‐skin tubular columns (DSTCs) are an innovative type of hybrid that consist outer tube made FRP, inner circular steel tube, and a concrete core sandwiched between them. Available literature focuses on hollow DSTCs with limited research tubes filled concrete. Overall, have many applications, highlighting the importance studying effects filling strength composite system. To address this gap, finite element models (FEMs) both traditional machine learning (ML) techniques were used to develop accurate for predicting load‐bearing capacity confined ultimate strain under axial loads. A comprehensive database 60 experimental tests 45 FEMs simulations was analyzed, five parameters selected as input variables ML‐based models. New like gradient boosting (GB), random forest (RF), convolutional neural networks, long short‐term memory compared established algorithms multiple linear regression, support vector regression (SVR), empirical mode decomposition (EMD)‐SVR. Regression error characteristics curve, Shapley Additive Explanation analysis, statistical metrics assess performance these using containing 105 test results cover range variables. While EMD‐SVR GB perform well strain, suggested EMD‐SVR, GB, RF show superior predictive accuracy load. be more precise, load prediction, obtain values 0.99, 0.989, 0.960, respectively. The at 0.690 However, design engineers by “black‐box” nature ML. In order solve this, study presents open‐source GUI based which gives ability precisely estimate various conditions, enabling them make well‐informed decisions about mix proportion.

Язык: Английский

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Grey wolf optimizer integrated within boosting algorithm: Application in mechanical properties prediction of ultra high-performance concrete including carbon nanotubes

Applied Materials Today, Год журнала: 2025, Номер 42, С. 102601 - 102601

Опубликована: Янв. 18, 2025

Язык: Английский

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Success and challenges in predicting TBM penetration rate using recurrent neural networks

Tunnelling and Underground Space Technology, Год журнала: 2022, Номер 130, С. 104728 - 104728

Опубликована: Сен. 1, 2022

Язык: Английский

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Machine learning based computational approach for crack width detection of self-healing concrete

Case Studies in Construction Materials, Год журнала: 2022, Номер 17, С. e01610 - e01610

Опубликована: Окт. 25, 2022

Язык: Английский

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Review of the grey wolf optimization algorithm: variants and applications

Neural Computing and Applications, Год журнала: 2023, Номер 36(6), С. 2713 - 2735

Опубликована: Ноя. 22, 2023

Язык: Английский

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