Optimizing superelastic shape-memory alloy fibers for enhancing the pullout performance in engineered cementitious composites DOI Creative Commons
Muhammad Umar, Hui Qian, Hamad Almujibah

et al.

Science and Engineering of Composite Materials, Journal Year: 2024, Volume and Issue: 31(1)

Published: Jan. 1, 2024

Abstract This study explores the effect of integrated superelastic shape-memory alloy fibers (SMAFs) on mechanical performance engineered cementitious composites (ECCs). Various SMAF configurations – linear-shaped SMAFs (LS-SMAFs), hook-shaped (HS-SMAFs), and indented-shaped (IS-SMAFs) with diameters 0.8 1.0 mm were incorporated into ECC matrices, surface texturization was achieved through abrasive paper treatment. Their properties assessed single fiber pullout tests mixtures containing 1.5 2.0% polyvinyl alcohol (PVA), subjected to both monotonic cyclic loading conditions. Qualitative analysis, employing scanning electron microscopy, demonstrated that IS-SMAF configuration provided superior interlocking fiber–matrix adhesion, a distinct flag shape observed during tensile testing. Quantitative data indicated IS-SMAFs significantly improved strength resistance, slip distances ≥5 average loads ranging from 263 403 N. LS-SMAFs better compared HS-SMAFs in terms characteristics. Additionally, ECCs increased PVA content exhibited enhanced withdrawal performance. Thermogravimetry analysis X-ray diffraction insights high-temperature stability crystalline structure composites. These results underscore effectiveness enhancing properties, offering significant implications for development optimization high-performance composite materials civil engineering applications.

Language: Английский

Physics-informed modeling of splitting tensile strength of recycled aggregate concrete using advanced machine learning DOI Creative Commons
Kennedy C. Onyelowe, Viroon Kamchoom‬, Shadi Hanandeh

et al.

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

Published: Feb. 28, 2025

Physics-informed modeling (PIM) using advanced machine learning (ML) represents a paradigm shift in the field of concrete technology, offering potent blend scientific rigor and computational efficiency. By harnessing synergies between physics-based principles data-driven algorithms, PIM-ML not only streamlines design process but also enhances reliability sustainability structures. As research continues to refine these models validate their performance, adoption promises revolutionize how materials are engineered, tested, utilized construction projects worldwide. In this work, an extensive literature review, which produced global representative database for splitting tensile strength (Fsp) recycled aggregate concrete, was indulged. The studied components such as C, W, NCAg, PL, RCAg_D, RCAg_P, RCAg_wa, Vf, F_type were measured tabulated. collected 257 records partitioned into training set 200 (80%) validation 57 (20%) line with more reliable partitioning database. Five techniques created "Weka Data Mining" software version 3.8.6 applied predict Fsp Hoffman & Gardener method performance metrics used evaluate sensitivity variables ML models, respectively. results show Kstar model demonstrates highest level among achieving exceptional accuracy R2 0.96 Accuracy 94%. Its RMSE MAE both low at 0.15 MPa, indicating minimal deviations predicted actual values. Additional WI (0.99), NSE (0.96), KGE (0.96) further confirm model's superior efficiency consistent making it most dependable tool practical applications. Also analysis shows that Water content (W) exerts significant impact 40%, demonstrating amount water mix is critical factor optimal strength. This underscores need careful management balance workability sustainable production. Coarse natural (NCAg) has substantial 38%, its essential role maintaining structural integrity mix.

Language: Английский

Citations

2
Machine Learning Prediction Model Integrating Experimental Study for Compressive Strength of Carbon-Nanotubes Composites DOI Creative Commons
Aneel Manan, Pu Zhang, Shoaib Ahmad

et al.

Journal of Engineering Research, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 1, 2024

Language: Английский

Citations

6
Machine Learning Prediction of Recycled Concrete Powder with Experimental validation and Life Cycle Assessment study DOI Creative Commons
Aneel Manan, Pu Zhang, Weiyi Chen

et al.

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 21, P. e04053 - e04053

Published: Nov. 28, 2024

Language: Английский

Citations

6
Multi-targeted strength properties of recycled aggregate concrete through a machine learning approach DOI
Aneel Manan, Pu Zhang, Jawad Ahmad

et al.

Engineering Computations, Journal Year: 2024, Volume and Issue: 42(1), P. 388 - 430

Published: Nov. 22, 2024

Purpose Rapid industrialization and construction generate substantial concrete waste, leading to significant environmental issues. Nearly 10 billion metric tonnes of waste are produced globally per year. In addition, also accelerates the consumption natural resources, depletion these resources. Therefore, this study uses artificial intelligence (AI) examine utilization recycled aggregate (RCA) in concrete. Design/methodology/approach An extensive database 583 data points collected from literature for predictive modeling. Four machine learning algorithms, namely neural network (ANN), random forest (RF), ridge regression (RR) least adjacent shrinkage selection operator (LASSO) (LR), predicting simultaneously compressive tensile strength were evaluated. The dataset contains independent variables two dependent variables. Statistical parameters, including coefficient determination (R 2 ), mean square error (MSE), absolute (MAE) root (RMSE), employed assess accuracy algorithms. K-fold cross-validation was validate obtained results, SHapley Additive exPlanations (SHAP) analysis applied identify most sensitive parameters out input parameters. Findings results indicate that RF prediction model performance is better more satisfactory than other Furthermore, ANN algorithm ranks as second accurate algorithm. However, RR LR exhibit poor findings with low accuracy. successfully SHAP indicates cement content percentages effective parameter. special attention should be given enhance performance. Originality/value This uniquely applies AI optimize use RCA production. By evaluating four ANN, RF, on a comprehensive dataset, identities models strength. determine key result validation adds robustness. highlight superior provide actionable insights into enhancing RCA, contributing sustainable practice.

Language: Английский

Citations

5
AI-based constitutive model simulator for predicting the axial load-deflection behavior of recycled concrete powder and steel fiber reinforced concrete column DOI
Aneel Manan, Pu Zhang, Weiyi Chen

et al.

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 470, P. 140628 - 140628

Published: March 3, 2025

Language: Английский

Citations

0
Predicting the strengths of basalt fiber reinforced concrete mixed with fly ash using AML and Hoffman and Gardener techniques DOI Creative Commons
Kennedy C. Onyelowe, Viroon Kamchoom‬, Shadi Hanandeh

et al.

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

Published: April 9, 2025

Basalt fiber-reinforced concrete (BFRC) mixed with fly ash, combined advanced machine learning techniques, offers a practical, cost-effective, and less time-consuming alternative to traditional experimental methods. Conventional approaches evaluating mechanical properties, such as compressive splitting tensile strengths, typically require sophisticated equipment, meticulous sample preparation, extended testing periods. These methods demand substantial financial resources, specialized labor, considerable time for data collection analysis. The integration of provides transformative solution by enabling accurate prediction properties minimal data. from literature analysis were used 121 records collected experimentally tested basalt fiber reinforced samples measuring the strengths concrete. Eleven (11) critical factors have been considered constituents studied predict Fc-Compressive strength (MPa) Fsp-Splitting (MPa), which are output parameters. divided into training set (96 = 80%) validation (25 20%) following requirements partitioning sustainable application. Seven (7) selected techniques applied in prediction. Further, performance evaluation indices compare models' abilities lastly, Hoffman Gardener's technique was evaluate sensitivity parameters on strengths. At end exercise, results collated. In predicting (Fc), AdaBoost similarly excels, matching XGBoosting's R2 0.98 same MAE values. This shows effectiveness boosting predictive modeling estimation. For (Fsp), also outperforms most models, achieving an 0.96 phases. Its exceptionally low 0.124 MPa underscores its excellent generalization capabilities. Overall, XGBoosting consistently demonstrate superior both predictions, followed closely KNN. models benefit ensemble that efficiently handle non-linear patterns noise. SVR performs admirably, whereas GEP GMDHNN exhibit weaker capabilities due limitations handling complex dynamics. analysis, method proves instrumental identifying key drivers concrete, guiding informed decision-making material optimization construction practices.

Language: Английский

Citations

0
Mechanical properties of self compacting concrete reinforced with hybrid fibers and industrial wastes under elevated heat treatment DOI Creative Commons
Kennedy C. Onyelowe, Shadi Hanandeh, Viroon Kamchoom‬

et al.

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

Published: April 13, 2025

Machine learning prediction of the mechanical properties self-compacting concrete (SCC) reinforced with hybrid fibers, incorporating industrial wastes like fly ash and blast furnace slag, cured under elevated heat provides a reliable efficient alternative to traditional laboratory experiments. In this work, extensive literature review leading collection, sorting curation global database representative fiber mixed for sustainable construction was conducted. The collected constituted components admixtures such as Cement (C), Fly (FA), Slag (BFS), Fine Aggregate (FAg), Coarse (CAg), Water (W), Superplasticizer (PL), Fiber (Fi), Temperature (Temp.) studied Compressive Strength (Fc), Tensile (Fsp), Flexural (Ff). 114 records were divided into training set (90 = 80%) validation (24 20%) following guidelines data partitioning optimal performance in machine predictions. Different advanced methods created using "Weka Data Mining" software version 3.8.6 applied "Semi-supervised classifier (Kstar)", "M5 (M5Rules), "Elastic net (ElasticNet), "Correlated Nystrom Views (XNV)", "Decision Table (DT)" predict output. Hoffman/Gardener SHAP techniques are used estimate sensitivity input parameter on Finally, various metrics evaluate reliability models. results show that models varying degrees predictive accuracy, Kstar XNV consistently outperforming others across all properties. However, accuracies 96.5%, 96.0%, 97.0% Fc, Fsp, Ff predictions, respectively proposed most decisive model. Also, Hoffman Gardener method highlights role binders, chemical additives, curing, whereas attributes greater importance aggregates binder interactions.

Language: Английский

Citations

0
An empirical review of sustainable alternatives in concrete using sugarcane bagasse ash, copper slag, and eggshell powder DOI
Sagar W. Dhengare,

U. P. Waghe

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2025, Volume and Issue: 8(6)

Published: April 18, 2025

Language: Английский

Citations

0
Optimizing superelastic shape-memory alloy fibers for enhancing the pullout performance in engineered cementitious composites DOI Creative Commons
Muhammad Umar, Hui Qian, Hamad Almujibah

et al.

Science and Engineering of Composite Materials, Journal Year: 2024, Volume and Issue: 31(1)

Published: Jan. 1, 2024

Abstract This study explores the effect of integrated superelastic shape-memory alloy fibers (SMAFs) on mechanical performance engineered cementitious composites (ECCs). Various SMAF configurations – linear-shaped SMAFs (LS-SMAFs), hook-shaped (HS-SMAFs), and indented-shaped (IS-SMAFs) with diameters 0.8 1.0 mm were incorporated into ECC matrices, surface texturization was achieved through abrasive paper treatment. Their properties assessed single fiber pullout tests mixtures containing 1.5 2.0% polyvinyl alcohol (PVA), subjected to both monotonic cyclic loading conditions. Qualitative analysis, employing scanning electron microscopy, demonstrated that IS-SMAF configuration provided superior interlocking fiber–matrix adhesion, a distinct flag shape observed during tensile testing. Quantitative data indicated IS-SMAFs significantly improved strength resistance, slip distances ≥5 average loads ranging from 263 403 N. LS-SMAFs better compared HS-SMAFs in terms characteristics. Additionally, ECCs increased PVA content exhibited enhanced withdrawal performance. Thermogravimetry analysis X-ray diffraction insights high-temperature stability crystalline structure composites. These results underscore effectiveness enhancing properties, offering significant implications for development optimization high-performance composite materials civil engineering applications.

Language: Английский

Citations

0