Advanced predictive techniques for estimating compressive strength in recycled aggregate concrete: exploring interaction, quadratic models, ANN, and M5P across strength classes DOI
Yousif J. Bas, Jamal I. Kakrasul,

Kamaran S. Ismail

и другие.

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2024, Номер 8(1)

Опубликована: Дек. 29, 2024

Язык: Английский

Optimization of waste plastic fiber concrete with recycled coarse aggregate using RSM and ANN DOI Creative Commons

Sumant Nivarutti Shinde,

S. T. Jaya Christa,

Rakesh Kumar Grover

и другие.

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

Опубликована: Март 6, 2025

Язык: Английский

Процитировано

1
Evaluating the strength of industrial wastesbased concrete reinforced with steel fiber using advanced machine learning DOI Creative Commons
Kennedy C. Onyelowe, Viroon Kamchoom‬, Ahmed M. Ebid

и другие.

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

Опубликована: Март 8, 2025

The traditional evaluation of compressive strength through repeated experimental works can be resource-intensive, time-consuming, and environmentally taxing. Leveraging advanced machine learning (ML) offers a faster, cheaper, more sustainable alternative for evaluating optimizing concrete properties, particularly materials incorporating industrial wastes steel fibers. In this research work, total 166 records were collected partitioned into training set (130 = 80%) validation (36 20%) in line with the requirements data partitioning sorting optimal model performance. These entries represented ten (10) components fiber reinforced such as C, W, FAg, CAg, PL, SF, FA, Vf, FbL, FbD, which applied input variables Cs, was target. Advanced techniques to (Cs) "Semi-supervised classifier (Kstar)", "M5 (M5Rules), "Elastic net (ElasticNet), "Correlated Nystrom Views (XNV)", "Decision Table (DT)". All models created using 2024 "Weka Data Mining" software version 3.8.6. Also, accuracies developed evaluated by comparing sum squared error (SSE), mean absolute (MAE), (MSE), root (RMSE), Error (%), Accuracy (%) coefficient determination (R2), correlation (R), willmott index (WI), Nash–Sutcliffe efficiency (NSE), Kling–Gupta (KGE) symmetric percentage (SMAPE) between predicted calculated values output. At end, has been found transformative approach that enhances efficiency, cost-effectiveness, sustainability wastes-based fiber. Among reviewed, Kstar DT emerge most practical achieving precise results. Their adoption significantly reduce environmental impacts promote use by-products construction. sensitivity on produced 36% from 71% 70% 60% 34% 5% 33% 67% 61% 61%. Fiber Volume Fraction (Vf) (67%) high suggests content greatly crack resistance tensile strength. Steel Orientation (61%) indicates importance alignment distributing stresses enhancing structural integrity.

Язык: Английский

Процитировано

0
Evaluation of the mechanical characteristics of bagasse ash concrete using response surface methodology DOI Creative Commons
Uzoma Ibe Iro, George Uwadiegwu Alaneme,

Nakkeeran Ganasen

и другие.

Discover Sustainability, Год журнала: 2025, Номер 6(1)

Опубликована: Апрель 21, 2025

Язык: Английский

Процитировано

0
Bacterial Solution in GGBS Concrete: A Sustainable Approach to Improving Properties DOI
Nakul Gupta, Arun Kumar Parashar, Bhuvnesh Yadav

и другие.

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

Опубликована: Апрель 23, 2025

Язык: Английский

Процитировано

0
Mechanical and sustainability performance of concrete incorporated limestone powder, recycled ceramic aggregates, and coconut fibers DOI

M. Indumathi,

G. Nakkeeran,

G. Uday Kiran

и другие.

Innovative Infrastructure Solutions, Год журнала: 2025, Номер 10(5)

Опубликована: Апрель 26, 2025

Язык: Английский

Процитировано

0
Enhancing the mechanical properties’ performances coconut fiber and CDW composite in paver block: multiple AI techniques with a Performance analysis DOI Creative Commons

G. Uday Kiran,

G. Nakkeeran,

Dipankar Roy

и другие.

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

Опубликована: Дек. 30, 2024

The present research incorporates five AI methods to enhance and forecast the characteristics of building envelopes. In this study, Response Surface Methodology (RSM), Support Vector Machine (SVM), Gradient Boosting (GB), Artificial Neural Networks (ANN), Random Forest (RF) machine learning method for optimization predicting mechanical properties natural fiber addition incorporated with construction demolition waste (CDW) as replacement Fine Aggregate in Paver blocks. factors considered were cement content, fine aggregate, CDW, coconut fibre, while resulting measure was machinal paver Furthermore, techniques precision extensively evaluated. outcomes from both training testing phases demonstrated strong predictive power RSM, SVM, GB, ANN, RF a criterion used Root Mean square error (RMSE), (MSE), Absolute Error (MAE) correlation coefficient (R). Moreover, results that GB ANN provide enhanced performance comparison SVM determining factors.

Язык: Английский

Процитировано

2
Recycling glass waste in mortar: a sustainable approach to enhancing strength and density DOI

Bhukya Govardhan Naik,

Nakkeeran Ganasen,

Dipankar Roy

и другие.

Asian Journal of Civil Engineering, Год журнала: 2024, Номер unknown

Опубликована: Дек. 19, 2024

Язык: Английский

Процитировано

0
Advanced predictive techniques for estimating compressive strength in recycled aggregate concrete: exploring interaction, quadratic models, ANN, and M5P across strength classes DOI
Yousif J. Bas, Jamal I. Kakrasul,

Kamaran S. Ismail

и другие.

Multiscale and Multidisciplinary Modeling Experiments and Design, Год журнала: 2024, Номер 8(1)

Опубликована: Дек. 29, 2024

Язык: Английский

Процитировано

0