Modelling the mechanical properties of concrete produced with polycarbonate waste ash by machine learning

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

Published: May 21, 2024

Abstract India’s cement industry is the second largest in world, generating 6.9% of global output. Polycarbonate waste ash a major problem India and around globe. Approximately 370,000 tons scientific are generated annually from fitness care facilities India. helps reduce environmental burden associated with disposal decreases need for new raw materials. The primary variable this study quantity polycarbonate (5, 10, 15, 20 25% weight cement), partial replacement cement, water-cement ratio aggregates. mechanical properties, such as compressive strength, split tensile strength flexural test results, mixtures were superior at 7, 14 28 days compared to those control mix. water absorption rate less than that standard concrete. Compared conventional concrete, concrete undergo minimal loss under acid curing conditions. utilized construction pollution improve economy. This further simulated characteristics made using least absolute shrinkage selection operator regression decision trees. Cement, waste, slump, absorption, main components considered input variables. suggested tree model was successful unparalleled predictive accuracy across important metrics. Its outstanding ability (R 2 = 0.879403), 0.91197), 0.853683) confirmed method preferred choice these predictions.

Language: Английский

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Prediction of mechanical properties of high‐performance concrete and ultrahigh‐performance concrete using soft computing techniques: A critical review

Structural Concrete, Journal Year: 2024, Volume and Issue: unknown

Published: May 23, 2024

Abstract A cement‐based material that meets the general goals of mechanical properties, workability, and durability as well ever‐increasing demands environmental sustainability is produced by varying type quantity individual constituents in high‐performance concrete (HPC) ultrahigh‐performance (UHPC). Expensive time‐consuming laboratory experiments can be used to estimate properties mixtures elements. As an alternative, these attributes approximated means predictive models created through application artificial intelligence (AI) methodologies. AI approaches are among most effective ways solve engineering problems due their capacity for pattern recognition knowledge processing. Machine learning (ML) deep (DL) a subfield gaining popularity across many scientific domains result its benefits over statistical experimental models. These include, but not limited to, better accuracy, faster performance, greater responsiveness complex environments, lower economic costs. In order assess critical features literature, comprehensive review ML DL applications HPC UHPC was conducted this study. This paper offers thorough explanation fundamental terms ideas algorithms frequently predict UHPC. Engineers researchers working with construction materials will find useful helping them choose accurate appropriate methods needs.

Language: Английский

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Development of hybrid gradient boosting models for predicting the compressive strength of high-volume fly ash self-compacting concrete with silica fume

Structures, Journal Year: 2024, Volume and Issue: 66, P. 106850 - 106850

Published: July 8, 2024

Language: 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, Journal Year: 2024, Volume and Issue: unknown

Published: June 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.

Language: Английский

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Proposed numerical and machine learning models for fiber-reinforced polymer concrete-steel hollow and solid elliptical columns

Frontiers of Structural and Civil Engineering, Journal Year: 2024, Volume and Issue: 18(8), P. 1169 - 1194

Published: July 26, 2024

Language: Английский

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Prediction of the Splitting Tensile Strength of Manufactured Sand Based High-Performance Concrete Using Explainable Machine Learning

Iranian Journal of Science and Technology Transactions of Civil Engineering, Journal Year: 2024, Volume and Issue: 48(5), P. 3717 - 3734

Published: March 17, 2024

Language: Английский

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Physics-based self-adaptive algorithm for estimating the long-term performance of concrete shrinkage

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 142, P. 109945 - 109945

Published: Jan. 5, 2025

Language: Английский

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Optimizing high-strength concrete compressive strength with explainable machine learning

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2025, Volume and Issue: 8(3)

Published: Feb. 3, 2025

Language: Английский

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Parametric investigation of rectangular CFRP-confined concrete columns reinforced by inner elliptical steel tubes using finite element and machine learning models

Heliyon, Journal Year: 2023, Volume and Issue: 10(2), P. e23666 - e23666

Published: Dec. 15, 2023

Nowadays, due to the structural advantages gained by combining three different materials' properties, columns made of carbon-fiber reinforced polymer (CFRP)-confined concrete with inner steel tube have received researchers' interest. This article presents nonlinear finite element analysis and multiple machine learning (ML) model-based study on behavior round corner rectangular CFRP-confined short high-strength elliptical under axial load. The reliability proposed model was verified against existing experimental investigations. effects parameters such as grade, thickness reinforcing tube, cross-sectional size CFRP are comprehended in this study. Furthermore, ML models were predict ultimate load, strain, lateral strain test specimens. evaluated six distinct performance metrics. From parametric investigation, it found that lower compressive strength more enhancement because confinement between than relative its unconfined strength. extreme gradient boosting random forest provided best-fit results artificial neural network Gaussian process regression predicting load strains columns.

Language: Английский

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Machine learning techniques for prediction of failure loads and fracture characteristics of high and ultra-high strength concrete beams

Innovative Infrastructure Solutions, Journal Year: 2023, Volume and Issue: 8(8)

Published: July 27, 2023

Language: Английский

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