ML-powered prediction of concrete compressive strength and carbon emissions using optimized CatBoost models. Helps engineers and manufacturers make data-driven, eco-conscious material choices.

Predicting compressive strength of Steel Fibre Reinforced Concrete using six different machine learning algorithms.

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Focused on comparing the performance of hybrid ML algorithms in predicting concrete strength.

Concrete Strength Prediction Using ML Predicting 28-day concrete compressive strength using KNN, Random Forest, and XGBoost. Includes EDA, data preparation, feature engineering, and model evaluation (RMSE, MAE, MSE, R²). Dataset features material components such as cement, water, aggregates, and admixtures.

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A data-driven machine learning project that predicts the compressive strength of concrete based on its material composition and curing age. This project demonstrates the use of regression models to aid civil engineers in optimizing concrete mix designs for sustainable and high-performance construction.

Concrete compressive strength prediction using ML

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The system collects key material variables, preprocesses data, and implements an Extra Trees Regressor model for superior accuracy. This approach minimizes errors, enhances mix optimization, and enables real-time quality control, offering a scalable and efficient alternative to conventional methods.

Cement Concrete compressive strength prediction using ML Algorithms

to predict and analysis concrete compressive strength using Machine Learning techniques and auto ML

prediction of soils compressive strength using different ML models

Prediction of compressive strength of concrete using regression techniques in ML.

Cement mortar compressive strength and durability prediction using Graphene Oxide (Flask + ML + Render)

This project explores concrete compressive strength prediction using various ML models, feature engineering, and hyperparameter optimization.

In this ML project, we aim to predict the compressive strength of concrete using the composition of cement, slag, fly ash, water, plasticizer, aggregate, and age as input features. By training a model on historical data, we can enable accurate predictions of concrete strength, aiding in quality control and optimization of concrete mixtures.