Predicting the crack repair rate of self-healing concrete using soft-computing tools

Materials Today Communications, Journal Year: 2024, Volume and Issue: 38, P. 108043 - 108043

Published: Jan. 5, 2024

Language: Английский

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Assessing the compressive and splitting tensile strength of self-compacting recycled coarse aggregate concrete using machine learning and statistical techniques

Materials Today Communications, Journal Year: 2023, Volume and Issue: 38, P. 107970 - 107970

Published: Dec. 28, 2023

The construction industry is adopting high-performance materials due to technological and environmental advances. Researchers worldwide are studying the use of recycled coarse aggregates (RCA) as a partial alternative natural in concrete their sustainability benefits. This study compares predictive abilities three different machine learning techniques evaluating mechanical properties 28-day-old self-compacting (SCC) incorporating RCA better understand how design parameters affect SCC containing RCA. used range statistical methodologies algorithms, such ANN, SVM, M5P trees, examine relationship between elements accurately forecast characteristics concrete. ANN model exhibited notable superiority effectively forecasting compressive strength (CS) splitting tensile (STS) compared other models, with uncertainty bands 15.038%-21.154% for CS 15.701%-19.008% STS. Moreover, all uncertainties were under threshold 35%. Notably, water-cement ratio emerged most crucial parameter predicting SCC. Finally, parametric evaluation conducted revealed that STS inversely proportional aggregate-cement ratio, whereas, directly water-binder water-solids percentage.

Language: Английский

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Estimation of compressive strength of waste concrete utilizing fly ash/slag in concrete with interpretable approaches: optimization and graphical user interface (GUI)

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Feb. 26, 2024

Abstract Geo-polymer concrete has a significant influence on the environmental condition and thus its use in civil industry leads to decrease carbon dioxide (CO 2 ) emission. However, problems lie with mixed design casting field. This study utilizes supervised artificial-based machine learning algorithms (MLAs) anticipate mechanical characteristic of fly ash/slag-based geopolymer (FASBGPC) by utilizing AdaBoost Bagging MLPNN make an ensemble model 156 data points. The consist GGBS (kg/m 3 ), Alkaline activator Fly ash SP dosage NaOH Molarity, Aggregate Temperature (°C) compressive strength as output parameter. Python programming is utilized Anaconda Navigator using Spyder version 5.0 predict response. Statistical measures validation are done splitting dataset into 80/20 percent K-Fold CV employed check accurateness MAE, RMSE, R . analysis relies errors, tests against external indicators help determine how well models function terms robustness. most important factor measurements examined permutation characteristics. result reveals that ANN outclassed giving maximum enhancement = 0.914 shows least error statistical validations. Shapley GGBS, temperature influential parameter content making FASBGPC. Thus, methods suitable for constructing prediction because their strong reliable performance. Furthermore, graphical user interface (GUI) generated through process training forecasts desired outcome values when corresponding inputs provided. It streamlines provides useful tool applying model's abilities field engineering.

Language: Английский

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Indirect estimation of resilient modulus (Mr) of subgrade soil: Gene expression programming vs multi expression programming

Structures, Journal Year: 2024, Volume and Issue: 66, P. 106837 - 106837

Published: July 1, 2024

Language: Английский

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Predicting the compressive strength of polymer-infused bricks: A machine learning approach with SHAP interpretability

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 8, 2025

Abstract The rapid increase in global waste production, particularly Polymer wastes, poses significant environmental challenges because of its nonbiodegradable nature and harmful effects on both vegetation aquatic life. To address this issue, innovative construction approaches have emerged, such as repurposing Polymers into building materials. This study explores the development eco-friendly bricks incorporating cement, fly ash, M sand, polypropylene (PP) fibers derived from Polymers. primary innovation lies leveraging advanced machine learning techniques, namely, artificial neural networks (ANN), support vector machines (SVM), Random Forest AdaBoost to predict compressive strength these Polymer-infused bricks. polymer bricks’ was recorded output parameter, with PP waste, age serving input parameters. Machine models often function black boxes, thereby providing limited interpretability; however, our approach addresses limitation by employing SHapley Additive exPlanations (SHAP) interpretation method. enables us explain influence different variables predicted outcomes, thus making more transparent explainable. performance each model evaluated rigorously using various metrics, including Taylor diagrams accuracy matrices. Among compared models, ANN RF demonstrated superior which is close agreement experimental results. achieves R 2 values 0.99674 0.99576 training testing respectively, whereas RMSE value 0.0151 (Training) 0.01915 (Testing). underscores reliability estimating strength. Age, ash were found be most important variable predicting determined through SHAP analysis. not only highlights potential enhance predictive for sustainable materials demonstrates a novel application improve interpretability context repurposing.

Language: Английский

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Novel base predictive model of resilient modulus of compacted subgrade soils by using interpretable approaches with graphical user interface

Materials Today Communications, Journal Year: 2024, Volume and Issue: 40, P. 109764 - 109764

Published: July 10, 2024

Language: Английский

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Innovative machine learning approaches to predict the compressive strength of recycled plastic aggregate self-compacting concrete incorporating different waste ashes

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2024, Volume and Issue: 7(3), P. 2585 - 2604

Published: Jan. 28, 2024

Language: Английский

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Machine learning-based compressive strength estimation in nanomaterial-modified lightweight concrete

Open Engineering, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 1, 2024

Abstract The development of nanotechnology has led to the creation materials with unique properties, and in recent years, numerous attempts have been made include nanoparticles concrete an effort increase its performance create improved qualities. Nanomaterials are typically added lightweight (LWC) goal improving composite’s mechanical, microstructure, freshness, durability Compressive strength is most crucial mechanical characteristic for all varieties composites. For this reason, it essential accurate models estimating compressive (CS) LWC save time, energy, money. In addition, provides useful information planning construction schedule indicates when formwork should be removed. To predict CS mixtures or without nanomaterials, nine different were proposed study: gradient-boosted trees (GBT), random forest, tree ensemble, XGBoosted (XGB), Keras, simple regression, probabilistic neural networks, multilayer perceptron, linear relationship model. A total 2,568 samples gathered examined. significant factors influencing during modeling process taken into account as input variables, including amount cement, water-to-binder ratio, density, content aggregates, type nano, fine coarse aggregate content, water. suggested was assessed using a variety statistical measures, coefficient determination ( R 2 ), scatter index, mean absolute error, root-mean-squared error (RMSE). findings showed that, comparison other models, GBT model outperformed others predicting compression enhanced nanomaterials. produced best results, greatest value (0.9) lowest RMSE (5.286). Furthermore, sensitivity analysis that important factor prediction water content.

Language: Английский

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Machine learning-based compressive strength estimation in nano silica-modified concrete

Construction and Building Materials, Journal Year: 2023, Volume and Issue: 408, P. 133684 - 133684

Published: Oct. 11, 2023

Language: Английский

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Experimental investigation and predictive modeling of compressive strength and electrical resistivity of graphene nanoplatelets modified concrete

Materials Today Communications, Journal Year: 2023, Volume and Issue: 38, P. 107639 - 107639

Published: Nov. 23, 2023

Language: Английский

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