concrete-strength

Analysing the concrete dataset from I-Cheng Yeh (1998) with Python, scikit-learn, Bokeh

Concrete compressive strength prediction with machine learning using the R Caret package.

Feature Engineering on Concrete Strength prediction using Machine Learning Techniques in Python

Concrete Strength Estimator is a modern Next.js web dashboard that allows users to adjust 15 distinct mix parameters and instantly see the predicted compressive strength (in MPa).

A Hybrid Physics-Informed Machine Learning (PIML) System for Iraqi Concrete Optimization. Integrating DoE, Transfer Learning, and Lean Six Sigma for Engineering Certainty. [Phase 1: EDA]

Compare Machine Learning and Artificial Intelligence models for predicting concrete compressive strength — featuring a Tkinter GUI, TensorFlow/Keras AI, Scikit-learn ML, and SQLite integration.

Concrete compressive-strength prediction: benchmark of Linear, Ridge, Lasso, Decision Tree, Random Forest and Gradient Boosting regressors against baselines, tree-based feature importance and an interactive Streamlit simulator.

My projects and practices on various segments of machine learning and deep learning.

Concrete compressive-strength prediction with a machine learning model and an interactive application.

Concrete strength prediction based on its composition and curing process using CatBoost, XGBoost and LGBM.

A python program that uses Neural Network to predict strength of given samples of concrete.

Predict concrete compressive strength using decision tree regression, hyperparameter tuning, and an interactive Flask web application.

Tabular presentation of concrete strength

Microsoft Student Accelerator 2020: Predicting Concrete Strength with Artificial Neural Networks

Predict concrete compressive strength using an artificial neural network (ANN).

An integrated Deep Learning suite for sustainable civil engineering. Features an end-to-end Multi-Layer Perceptron neural network to predict concrete compressive strength, execute dynamic low-carbon chemical mix optimization, visualize time-series hardening kinetics, and benchmark predictive machine learning algorithms.

Reproducible datasets, computational workflows, and model implementations for externally validated tabular foundation models and explainable concrete compressive strength prediction.

Predicting concrete compressive strength using regression-based machine learning models.