Sigmoidal Mathematical Models in the Planning and Control of Rigid Pavement Works

The objective of the research was to use sigmoidal mathematical models for the planning and control of rigid pavement works. A dataset was constructed using 140 technical files, which were then analyzed to extract the valued work schedules. These schedules contained the variables time and cost per month. Subsequently, two groups were created from the dataset: a training group comprising 80% of the data and a test group comprising the remaining 20%. Subsequently, the variables were normalized and adjusted with the proposed logistic, Von Bertalanffy, and Gompertz models using Python 3.11.13. Following the implementation of training and validation procedures, the logistic model was identified as the optimal fit, as indicated by the following metrics: R2 = 0.9848, MSE = 0.0026, RMSE = 0.0506, and MAE = 0.0278. The implementation of the aforementioned model facilitates the establishment of an early warning system with a high degree of effectiveness. This system enables the evaluation of the discrepancy between the actual progress and the planned progress with an R2 greater than 98%, thereby serving as a robust instrument for the adjustment and revalidation of activities before and following their execution.