Influence of Limestone-Treated Construction Waste Aggregates on Mortar Properties: Optimization Using Response Surface Methodology DOI Creative Commons

Uday Kiran Golla,

N. Ganesan,

Dipankar Roy

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 26, 2024

Abstract Multiple sectors, including agriculture and construction, have produced large amounts of waste in recent years, leading to significant environmental problems. The construction sector is currently faced with the severe difficulties decreasing natural resources a growing dependence on management, highlighting need for quick action an approach recycling introduction alternative materials. study concentrates materials developing innovative products. This includes replacing 20% cement limestone powder rice husk, 50% fine aggregates demolition waste, incorporating 1% coconut fiber. evaluation these involved conducting tests assess compressive strength, split tensile flexural strength using cube, cylinder, beam samples parallel water absorption non-distractive tests. methods machine learning, Response Surface Methodology, were utilized prediction, showing enhanced hardened properties. suggests that into practices can protect provide sustainable options future.

Language: 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

et al.

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

Published: March 6, 2025

Language: Английский

Citations

2
Optimization and prediction of paver block properties with ceramic waste as fine aggregate using response surface methodology DOI Creative Commons

G. Uday Kiran,

G. Nakkeeran,

Dipankar Roy

et al.

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

Published: Oct. 8, 2024

The ceramic industry produces a significant volume of waste (CW), representing around 20-30% its the entire output. mostly comes from challenges noticed in manufacturing process, overproduction, and damage to products. Considering substantial worldwide production ceramics, it is crucial efficiently handle recycle this promote sustainability efforts. This study explores conversion into fine aggregates suitable for paver blocks. Currently, variety assessments are being conducted determine effectiveness these enhanced evaluations involve aspects like compressive strength, water absorption (WA), dry density, flow table measurements, ultrasonic pulse velocity (UPV), rebound hammer tests. results indicate that replacing natural with up 30% CW significantly improves strength (CS) Rebound provides useful information optimising content blocks, contributing development sustainable economical construction materials. Furthermore, focusses on minimising landfill preserving resources.

Language: Английский

Citations

9
Machine learning-based destructive and non-destructive testing of paver block using fly ash and polyvinyl chloride into sustainable pedestrians DOI

Bhukya Govardhan Naik,

G. Nakkeeran,

Dipankar Roy

et al.

Innovative Infrastructure Solutions, Journal Year: 2025, Volume and Issue: 10(4)

Published: March 13, 2025

Language: Английский

Citations

1
Mechanical properties optimization and cost analysis of agricultural waste as an alternative in brick production DOI Creative Commons

G. Nakkeeran,

L. Krishnaraj,

Pshtiwan Shakor

et al.

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

Published: Oct. 14, 2024

In recent years, building materials made from agricultural waste have become popular due to their lower cost and environmental impact. The Bio-Brick is mixed with Cement-Fly Ash Hydrated Lime a fine aggregate of groundnut shell in percentages (20%, 30%, 40%, 50%, 60%). optimum mix proportions hydrated lime mortar were found the compressive strength further continued study dry density, water absorption, efflorescence. Machine Learning techniques are used optimize predict properties Bio-Bricks mortars. Response Surface Methodology (RSM) Artificial Neural Networks (ANN) employed forecast such as strength, absorption exceptional accuracy. results RSM models exhibit high degrees accuracy, R-squared values exceeding 0.88 for absorption. ANN enhance this predictive power, 0.99 predicting these critical properties.

Language: Английский

Citations

3
Evaluation of the mechanical characteristics of bagasse ash concrete using response surface methodology DOI Creative Commons
Uzoma Ibe Iro, George Uwadiegwu Alaneme,

Nakkeeran Ganasen

et al.

Discover Sustainability, Journal Year: 2025, Volume and Issue: 6(1)

Published: April 21, 2025

Language: Английский

Citations

0
Evaluation of net-zero materials in mortar bricks with predictive modelling using random forest and gradient boosting techniques DOI Creative Commons

G. Uday Kiran,

N. Ganesan,

Dipankar Roy

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: 7(6)

Published: May 27, 2025

Language: Английский

Citations

0
Recycling glass waste in mortar: a sustainable approach to enhancing strength and density DOI

Bhukya Govardhan Naik,

Nakkeeran Ganasen,

Dipankar Roy

et al.

Asian Journal of Civil Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 19, 2024

Language: Английский

Citations

2
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

et al.

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

Published: Dec. 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.

Language: Английский

Citations

2
Influence of Limestone-Treated Construction Waste Aggregates on Mortar Properties: Optimization Using Response Surface Methodology DOI Creative Commons

Uday Kiran Golla,

N. Ganesan,

Dipankar Roy

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 26, 2024

Abstract Multiple sectors, including agriculture and construction, have produced large amounts of waste in recent years, leading to significant environmental problems. The construction sector is currently faced with the severe difficulties decreasing natural resources a growing dependence on management, highlighting need for quick action an approach recycling introduction alternative materials. study concentrates materials developing innovative products. This includes replacing 20% cement limestone powder rice husk, 50% fine aggregates demolition waste, incorporating 1% coconut fiber. evaluation these involved conducting tests assess compressive strength, split tensile flexural strength using cube, cylinder, beam samples parallel water absorption non-distractive tests. methods machine learning, Response Surface Methodology, were utilized prediction, showing enhanced hardened properties. suggests that into practices can protect provide sustainable options future.

Language: Английский

Citations

1