Authors: Rashida Noori, Atharv Patil, Samarth Gote, Om Gadre, Satish Rathod, Malik Mulani, Prof. V.P.Bhusare
Abstract: Concrete is one of the most widely used construction materials, and its compressive strength is a key parameter that determines its structural performance and durability. Traditionally, determining the compressive strength of concrete requires laboratory testing, which is time-consuming, costly, and dependent on curing conditions and sample preparation. In this study, a data-driven approach is applied to predict the compressive strength of concrete using regression analysis in Microsoft Excel. A dataset containing input variables such as cement content, water-cement ratio, fine and coarse aggregate proportions, and curing age is analysed. Various regression techniques—such as linear, multiple linear, and polynomial regression—are implemented to develop predictive models. The correlation between experimental and predicted results is evaluated using statistical indicators like R², standard error, and residual analysis. The study demonstrates that regression models can effectively predict concrete compressive strength with reasonab le accuracy, thereby reducing the need for extensive experimental trials. This approach highlights the potential of Excel as a simple yet powerful tool for engineers and researchers to perform predictive modelling and optimise concrete mix design. The compressive strength of concrete is a crucial property that determines its quality and load-bearing capacity. Conventionally, this strength is obtained through laboratory testing after curing, which can be time-consuming and resource-intensive. This project focuses on predicting the compressive strength of concrete using regression analysis in Microsoft Excel. By utilising input parameters such as cement content, water-cement ratio, fine and coarse aggregates, and curing age, a regression model is developed to estimate strength values. Multiple linear regression is applied to establish a relationship between these variables and the compressive strength. The accuracy of the model is evaluated through statistical measures like the coefficient of determination (R²) and error analysis. The results indicate that regression-based prediction provides a reliable and cost-effective alternative to traditional testing methods. This approach demonstrates the usefulness of Excel as an accessible tool for data analysis and decision-making in civil engineering applications.
DOI: http://doi.org/
