Cited by Developing robust structure: Multi‐expression programming for anticipating mechanical properties of shape memory alloy‐confined concrete cylinders

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

S. Sathvik, Rakesh Kumar,

Archudha Arjunasamy

и другие.

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

Опубликована: Март 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.

Язык: Английский

Процитировано

Predicting the strength of alkali-activated masonry blocks using machine learning models: geopolymer mortar with quarry waste, rice husk ash, and eggshell ash DOI

Anis Ahamed,

S. Sakeek Yamani,

L. S. Dissanayaka

и другие.

Journal of Building Pathology and Rehabilitation, Год журнала: 2025, Номер 10(1)

Опубликована: Янв. 28, 2025

Язык: Английский

Процитировано

Machine learning based optimization for mix design of manufactured sand concrete DOI

Yuan Zhong-xia,

Wei Zheng, Hongxia Qiao

и другие.

Construction and Building Materials, Год журнала: 2025, Номер 467, С. 140256 - 140256

Опубликована: Фев. 12, 2025

Язык: Английский

Процитировано

Machine Learning-Based Partition Method for Cyclic Development Mode of Submarine Soil Martials from Offshore Wind Farms DOI

Ben He, Mingbao Lin, Zhishuai Zhang

и другие.

Journal of Marine Science and Engineering, Год журнала: 2025, Номер 13(3), С. 533 - 533

Опубликована: Март 10, 2025

Offshore wind turbines are subjected to long-term cyclic loads, and the seabed materials surrounding foundation susceptible failure, which affects safe construction normal operation of offshore turbines. The existing studies mechanical properties submarine soils focus on accumulation strain liquefaction, few targeted conducted hysteresis loop under loads. Therefore, 78 representative soil samples from four farms tested in study, behaviors different confining pressures CSR investigated. experiments reveal two unique development modes specify critical five martials testing conductions. Based dynamic triaxial test results, machine learning-based partition models for mode were established, discrimination accuracy discussed. This study found that RF model has a better generalization ability higher than GBDT discriminating soil, achieved prediction 0.96 recall 0.95 dataset, provides an important theoretical basis technical support design

Язык: Английский

Процитировано

Ensemble machine learning models for predicting concrete compressive strength incorporating various sand types DOI

Rupesh Kumar Tipu,

Shweta Bansal,

Vandna Batra

и другие.

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2025, Номер 8(4)

Опубликована: Март 14, 2025

Язык: Английский

Процитировано

Soft-computing models for predicting plastic viscosity and interface yield stress of fresh concrete DOI

Waleed Bin Inqiad, Muhammad Faisal Javed, Deema Mohammed Alsekait

и другие.

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

Опубликована: Март 28, 2025

Interface yield stress and plastic viscosity of fresh concrete significantly influences its pumping ability. The accurate determination these properties needs extensive testing on-site which results in time resource wastage. Thus, to speed up the process accurately determining properties, this study tends use four machine learning (ML) algorithms including Random Forest Regression (RFR), Gene Expression Programming (GEP), K-nearest Neighbor (KNN), Extreme Gradient Boosting (XGB) a statistical technique Multi Linear (MLR) develop predictive models for interface concrete. Out all employed algorithms, only GEP expressed output form an empirical equation. were developed using data from published literature having six input parameters cement, water, after mixing etc. two i.e., stress. performance was assessed several error metrices, k-fold validation, residual assessment comparison revealed that XGB is most algorithm predict (training [Formula: see text], text]) text]). To get increased insights into model prediction process, shapely individual conditional expectation analyses carried out on highlighted are influential estimate both In addition, graphical user has been made efficiently implement findings civil engineering industry.

Язык: Английский

Процитировано

Hybrid Machine Learning Based Strength and Durability Predictions of Polypropylene Fiber-Reinforced Graphene Oxide Based High-Performance Concrete DOI

Monica Kalbande,

Tejaswini Panse,

Yashika Gaidhani

и другие.

Iranian Journal of Science and Technology Transactions of Civil Engineering, Год журнала: 2025, Номер unknown

Опубликована: Апрель 19, 2025

Язык: Английский

Процитировано

Developing robust structure: Multi‐expression programming for anticipating mechanical properties of shape memory alloy‐confined concrete cylinders DOI

Saeed Eilbeigi,

Mohammadreza Tavakkolizadeh, Amir R. Masoodi

и другие.

Structural Concrete, Год журнала: 2025, Номер unknown

Опубликована: Апрель 24, 2025

Abstract This study emphasizes the expansion of novel relationships to determine maximum compressive stress, corresponding strain, ultimate and strain in order enhance precision practicality predicting behavior SMA‐confined concrete (SMACC) with spirals. It develops predictive equations for mechanical properties SMACC cylinders using multi‐expression programming (MEP) method. The MEPX software is employed derive optimal by collecting experimental data from 42 cylindrical specimens subjected uniaxial compression confined SMA findings show that developed MEP‐based not only provide practical equations, but also produce more precise results.

Язык: Английский

Процитировано