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Prediction of compressive strength of glass fiber-reinforced self-compacting concrete interpretable by machine learning algorithms
Self-compacting concrete (SCC) is a versatile construction material known for its ability to consolidate naturally under its own weight, making it well suited for challenging placements and reinforced sections. Incorporating a minimal amount of glass fiber into self-compacting concrete augments its benefits during the fresh state and translates to enhanced performance in the hardened state. In this present study, an experimental investigation has been carried out to examine the effects of glass fiber of varying proportions 3%, 6%, and 9% with a constant 10% of fly ash as mineral admixture used as partial replacement of ordinary portland cement (OPC) in SCC. The experimental result reveals that the introduction of 3% glass fiber has a substantial impact on the compressive strength. To forecast compressive strength, three machine learning algorithms AdaBoost, XGBoost, and Gradient Boost regressor were employed to interpret the experimental data and predict the anticipated outcomes of compressive strength. Among the three models, XGBoost shows higher accuracy with R2 of 0.9918 and 0.9797 in training and testing in comparison to the other two models due to its adeptness in managing complex interaction relationships. Additionally, a sensitivity analysis was conducted using both Sobol and SHAP methods. The results revealed that glass fiber and fly ash exerted substantial influence on the compressive strength, as indicated by their greater impacts identified through both sensitivity analysis techniques. This study significantly contributes to bridging the gaps that were present in prior research not only enhances our understanding of the factors influencing compressive strength but also provides a comprehensive approach to sensitivity analysis, thus making a meaningful contribution to the field.
Prediction of compressive strength of glass fiber-reinforced self-compacting concrete interpretable by machine learning algorithms
Self-compacting concrete (SCC) is a versatile construction material known for its ability to consolidate naturally under its own weight, making it well suited for challenging placements and reinforced sections. Incorporating a minimal amount of glass fiber into self-compacting concrete augments its benefits during the fresh state and translates to enhanced performance in the hardened state. In this present study, an experimental investigation has been carried out to examine the effects of glass fiber of varying proportions 3%, 6%, and 9% with a constant 10% of fly ash as mineral admixture used as partial replacement of ordinary portland cement (OPC) in SCC. The experimental result reveals that the introduction of 3% glass fiber has a substantial impact on the compressive strength. To forecast compressive strength, three machine learning algorithms AdaBoost, XGBoost, and Gradient Boost regressor were employed to interpret the experimental data and predict the anticipated outcomes of compressive strength. Among the three models, XGBoost shows higher accuracy with R2 of 0.9918 and 0.9797 in training and testing in comparison to the other two models due to its adeptness in managing complex interaction relationships. Additionally, a sensitivity analysis was conducted using both Sobol and SHAP methods. The results revealed that glass fiber and fly ash exerted substantial influence on the compressive strength, as indicated by their greater impacts identified through both sensitivity analysis techniques. This study significantly contributes to bridging the gaps that were present in prior research not only enhances our understanding of the factors influencing compressive strength but also provides a comprehensive approach to sensitivity analysis, thus making a meaningful contribution to the field.
Prediction of compressive strength of glass fiber-reinforced self-compacting concrete interpretable by machine learning algorithms
Asian J Civ Eng
Gogineni, Abhilash (author) / Rout, M. K. Diptikanta (author) / Shubham, Kumar (author)
Asian Journal of Civil Engineering ; 25 ; 2015-2032
2024-02-01
18 pages
Article (Journal)
Electronic Resource
English