Modeling Concrete Compressive Strength Variation with Age Using Origin Pro Software. - Nabaweesi, Alice (Kabale University, 2024)
Concrete is a widely used construction material whose compressive strength is a critical factor influencing its performance. This study aims to develop a mathematical model that accurately predicts the variation of concrete compressive strength with age. Origin Pro software, a powerful data analysis tool, was employed to analyze existing data and develop the model. A total of 150 observations of existing compressive strength data were used to develop the mathematical model. These were taken from compressive strength data for the concrete cast for the upgrading to paved standard of 9.5km of roads, reconstruction of 6.72km of roads including signalization of junctions and channelization of 3.9km of drainages in Lubaga, Kawempe, Makindye Division, and Wakiso district, a project by the Korea Consultants International as the Consultant, China Communications Construction Company as the Contractor and KCCA as the client. They included 37 observations for class 20, 79 observations for class 25, and 34 observations for class 30. The data was then imported into Origin Pro for statistical analysis and regression modeling. Correlation analysis was performed to identify significant factors influencing compressive strength. Subsequently, a regression model was developed to establish a relationship between compressive strength and age. The developed model was validated with a separate dataset from experimental data in the Kabale University Civil Engineering Laboratory to assess its accuracy. The model's performance was evaluated using metrics such as mean squared error (MSE) and coefficient of determination (R-squared). The results demonstrated that the model effectively captured the trend of compressive strength variation with age. The findings of this study provide valuable insights into concrete behavior over time. The developed model can be utilized in construction planning, quality control, and material research. Future research could explore the incorporation of additional factors, such as aggregate properties and environmental conditions, to enhance the model's predictive capabilities.