Experimental Analysis of Durability in Self-Compacting Concrete: Carbonation Penetration Perspectives DOI Creative Commons
Krishan Kumar Saini,

Suresh Singh Sankhla,

Sangeeta Parihar

et al.

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

Published: Dec. 31, 2024

Abstract Carbonation, a chemical reaction between atmospheric CO2 and the hydration products of cement, leads to reduction in pH concrete, thereby increasing risk reinforcement corrosion. This study examines durability conventional concrete (CC) self-compacting (SCC) through accelerated carbonation tests, with focus on impact mineral admixtures, specifically Ground Granulated Blast Furnace Slag (GGBS) fly ash, as partial replacements for cement. The investigates depth over time under controlled conditions, using mixes varying proportions GGBS ash. results indicate that SCC higher content exhibit superior durability, evidenced by significantly lower depths compared mixes. Specifically, SCC, ranged from 8.77 mm (SCC1 30% GGBS) 11.9 (SCC7 ash content), whereas CC, 11.43 (CC2 16.1 (CC7 content). inclusion particularly GGBS, was found reduce porosity, hindering penetration CO2. However, it observed excessive replacement cement admixtures beyond an optimal threshold resulted decreased resistance due reduced availability calcium hydroxide carbonation. Additionally, highlights significance water/binder ratio influencing concrete’s strength both which are critical factors resistance. findings suggest content, offers enhanced concrete. A Multiple Linear Regression (MLR) model also developed, providing accurate predictions key parameters demonstrating potential statistical modeling optimizing mix designs improved performance sustainability.

Language: Английский

Assessing the compressive strength of eco-friendly concrete made with rice husk ash: A hybrid artificial intelligence-aided technique DOI
Ramin Kazemi, Seyed Ali Emamian, Mehrdad Arashpour

et al.

Structures, Journal Year: 2024, Volume and Issue: 68, P. 107050 - 107050

Published: Aug. 15, 2024

Language: Английский

Citations

3
Advanced machine learning techniques for predicting concrete mechanical properties: a comprehensive review of models and methodologies DOI
Fangyuan Li,

Md. Sohel Rana,

Muhammad Ahmed Qurashi

et al.

Multiscale and Multidisciplinary Modeling Experiments and Design, Journal Year: 2024, Volume and Issue: 8(1)

Published: Dec. 18, 2024

Language: Английский

Citations

3
An AI-driven approach for modeling the compressive strength of sustainable concrete incorporating waste marble as an industrial by-product DOI Creative Commons
Ramin Kazemi, Seyedali Mirjalili

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

Published: Nov. 5, 2024

Abstract A key goal of environmental policies and circular economy strategies in the construction sector is to convert demolition industrial wastes into reusable materials. As an by-product, Waste marble (WM), has potential replace cement fine aggregate concrete which helps with saving natural resources reducing harm. While many studies have so far investigated effect WM on compressive strength (CS), it undeniable that conducting experimental activities requires time, money, re-testing changing materials conditions. Hence, this study seeks move from traditional approaches towards artificial intelligence-driven by developing three models—artificial neural network (ANN) hybrid ANN ant colony optimization (ACO) biogeography-based (BBO) predict CS concrete. For purpose, a comprehensive dataset including 1135 data records employed literature. The models’ performance assessed using statistical metrics error histograms, K -fold cross-validation analysis applied avoid overfitting problems, emphasize reliable predictive capabilities, generalize them. indicated ANN-BBO model performed best correlation coefficient (R) 0.9950 root mean squared (RMSE) 1.2017 MPa. Besides, distribution results revealed outperformed ANN-ACO narrower range errors 98% predicted points training phase experienced [-10%, 10%], whereas for models, percentage was 85% 79%, respectively. Additionally, SHapley Additive exPlanations (SHAP) clarify impact input variables prediction accuracy found specimen’s age most influential variable. Eventually, validate ANN-BBO, comparison previous studies’ models.

Language: Английский

Citations

1
Experimental Analysis of Durability in Self-Compacting Concrete: Carbonation Penetration Perspectives DOI Creative Commons
Krishan Kumar Saini,

Suresh Singh Sankhla,

Sangeeta Parihar

et al.

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

Published: Dec. 31, 2024

Abstract Carbonation, a chemical reaction between atmospheric CO2 and the hydration products of cement, leads to reduction in pH concrete, thereby increasing risk reinforcement corrosion. This study examines durability conventional concrete (CC) self-compacting (SCC) through accelerated carbonation tests, with focus on impact mineral admixtures, specifically Ground Granulated Blast Furnace Slag (GGBS) fly ash, as partial replacements for cement. The investigates depth over time under controlled conditions, using mixes varying proportions GGBS ash. results indicate that SCC higher content exhibit superior durability, evidenced by significantly lower depths compared mixes. Specifically, SCC, ranged from 8.77 mm (SCC1 30% GGBS) 11.9 (SCC7 ash content), whereas CC, 11.43 (CC2 16.1 (CC7 content). inclusion particularly GGBS, was found reduce porosity, hindering penetration CO2. However, it observed excessive replacement cement admixtures beyond an optimal threshold resulted decreased resistance due reduced availability calcium hydroxide carbonation. Additionally, highlights significance water/binder ratio influencing concrete’s strength both which are critical factors resistance. findings suggest content, offers enhanced concrete. A Multiple Linear Regression (MLR) model also developed, providing accurate predictions key parameters demonstrating potential statistical modeling optimizing mix designs improved performance sustainability.

Language: Английский

Citations

0