Titlebook: Recent Advances in Civil Engineering for Sustainable Communities; Select Proceeding of N. Vinod Chandra Menon,Sreevalsa Kolathayar,K. S. C

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M. Geetha Priya,Dilsa Nasar,A. R. Deva Jefflin,Sushil Kumar Singh,Sandip Oza

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Recent Advances in Civil Engineering for Sustainable CommunitiesSelect Proceeding of

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PURG

Conventional and Ensemble Machine Learning Techniques to Predict the Compressive Strength of Sustainh as decision tree (DT) was developed as a conventional machine learning (CML) model, whereas Random Forest (RF), AdaBoost (AdB), and Gradient Boosting (GB) were developed as ensemble machine learning (EML) models. Hyperparameter tuning was also performed to enhance each model’s performance. As a re

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Experimental Study on Strength Properties of Concrete Incorporated with Bacteriactive and contribute to reduced CO. emissions. Consequently, bacterial concrete significantly enhances the strength and durability of concrete structures, promoting a sustainable and eco-friendly future.

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Predicting the Porosity of SCM-Blended Concrete Composites Using Ensemble Machine Learning Modelsng (EML) models to predict the values of porosity with differing proportions of SCMs in the concrete mix. Random forest (RF), AdaBoost (AdB), and gradient boosting (GB) were the EML models that were developed in this study. Gradient boosting was shown to be the best predictor of porosity, while the

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Prognosis of Concrete Strength: The State of Art in Using Different Machine Learning Algorithmsd filters out the less important features. The insights of using such concepts bring numerous possibilities for reducing the errors for better predictions. This study can demonstrate different possibilities for making the infrastructure sustainable and predictable by studying the mechanical properti