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 m...
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2025-08-01
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| author | Jose Manuel Palomino Ojeda Lenin Quiñones Huatangari Billy Alexis Cayatopa Calderon Manuel Emilio Milla Pino José Luis Piedra Tineo Marco Antonio Martínez Serrano Rosario Yaqueliny Llauce Santamaria |
| author_facet | Jose Manuel Palomino Ojeda Lenin Quiñones Huatangari Billy Alexis Cayatopa Calderon Manuel Emilio Milla Pino José Luis Piedra Tineo Marco Antonio Martínez Serrano Rosario Yaqueliny Llauce Santamaria |
| author_sort | Jose Manuel Palomino Ojeda |
| collection | DOAJ |
| description | 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: R<sup>2</sup> = 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 R<sup>2</sup> greater than 98%, thereby serving as a robust instrument for the adjustment and revalidation of activities before and following their execution. |
| format | Article |
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| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-23990a73fcd24addbe52e4235ef90e992025-08-20T04:00:50ZengMDPI AGApplied Sciences2076-34172025-08-011515873810.3390/app15158738Sigmoidal Mathematical Models in the Planning and Control of Rigid Pavement WorksJose Manuel Palomino Ojeda0Lenin Quiñones Huatangari1Billy Alexis Cayatopa Calderon2Manuel Emilio Milla Pino3José Luis Piedra Tineo4Marco Antonio Martínez Serrano5Rosario Yaqueliny Llauce Santamaria6Seismological and Construction Research Institute, National University of Jaen, Jaen 06800, PeruInstituto de Investigacion en Estudios Estadisticos y Control de Calidad, Facultad de Ingenieria Zootecnista, Biotecnologia, Agronegocios y Ciencia de Datos, Universidad Nacional Toribio Rodriguez de Mendoza de Amazonas, Chachapoyas 01001, PeruSeismological and Construction Research Institute, National University of Jaen, Jaen 06800, PeruDepartment of Civil Engineering, Faculty of Engineering, National University of Jaen, Jaen 06800, PeruSeismological and Construction Research Institute, National University of Jaen, Jaen 06800, PeruSeismological and Construction Research Institute, National University of Jaen, Jaen 06800, PeruAcademic Department of Basic and Applied Sciences, National University of Jaen, Jaen 06800, PeruThe 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: R<sup>2</sup> = 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 R<sup>2</sup> greater than 98%, thereby serving as a robust instrument for the adjustment and revalidation of activities before and following their execution.https://www.mdpi.com/2076-3417/15/15/8738construction managementproject controlS-curvemathematical modelsrigid pavements |
| spellingShingle | Jose Manuel Palomino Ojeda Lenin Quiñones Huatangari Billy Alexis Cayatopa Calderon Manuel Emilio Milla Pino José Luis Piedra Tineo Marco Antonio Martínez Serrano Rosario Yaqueliny Llauce Santamaria Sigmoidal Mathematical Models in the Planning and Control of Rigid Pavement Works Applied Sciences construction management project control S-curve mathematical models rigid pavements |
| title | Sigmoidal Mathematical Models in the Planning and Control of Rigid Pavement Works |
| title_full | Sigmoidal Mathematical Models in the Planning and Control of Rigid Pavement Works |
| title_fullStr | Sigmoidal Mathematical Models in the Planning and Control of Rigid Pavement Works |
| title_full_unstemmed | Sigmoidal Mathematical Models in the Planning and Control of Rigid Pavement Works |
| title_short | Sigmoidal Mathematical Models in the Planning and Control of Rigid Pavement Works |
| title_sort | sigmoidal mathematical models in the planning and control of rigid pavement works |
| topic | construction management project control S-curve mathematical models rigid pavements |
| url | https://www.mdpi.com/2076-3417/15/15/8738 |
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