The problem addressed in this project is the accurate prediction of compressive strength in concrete. Compressive strength is a critical mechanical property of concrete that influences the structural integrity and performance of various construction projects. Traditionally, predicting compressive strength has relied on empirical formulas or labor-intensive laboratory testing, which can be time-consuming, costly, and may not capture all relevant factors affecting strength.The main challenge is to develop a predictive model that can accurately estimate the compressive strength of concrete based on input parameters such as Grade, UPV, Rebound and age. This model should be able to generalize well to unseen data and provide reliable predictions across different concrete mixtures and environmental conditions. The specific objectives of the project include:
- Gathering and preprocessing a comprehensive dataset containing information on concrete mix proportions, curing conditions, and compressive strength measurements.
- Exploratory data analysis to understand the relationships between input variables and compressive strength and identify potential patterns or trends.
- Feature selection to determine the most relevant input variables that significantly impact compressive strength prediction.
- Implementing and training various machine learning algorithms, such as linear regression, support vector regression, random forest, and gradient boosting, to build predictive models.
- Evaluating the performance of the developed models using appropriate metrics such as mean absolute error, mean squared error, and coefficient of determination.
- Conducting feature importance analysis to identify the key factors driving compressive strength predictions.
- Providing insights and recommendations based on the analysis of results to improve the accuracy and reliability of compressive strength prediction in concrete.