Structural Evaluation and Predictive Modeling of Flexible Pavement in Rural Areas: A Case Study on the Puente Palca – Palca Road
This study aims to develop a predictive model for evaluating the structural condition of the asphalt layer and its impact on the serviceability of flexible pavement in the Puente Palca–Palca road, Huancavelica, Peru. The research focuses on 18 selected points along a 3-kilometer segment, employing t...
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | Perú |
| Institución: | Universidad Nacional Autónoma de Tayacaja Daniel Hernández Morillo |
| Repositorio: | Revista de investigación científica y tecnológica Llamkasun |
| Idioma: | español |
| OAI Identifier: | oai:ojs2.llamkasun.unat.edu.pe:article/132 |
| Acceso en línea: | https://llamkasun.unat.edu.pe/index.php/revista/article/view/132 |
| Access Level: | acceso abierto |
| Palabra clave: | Modelo predictivo Condición estructural Pavimento flexible Mantenimiento preventivo Vida útil del pavimento Predictive model Structural condition Flexible pavement Preventive maintenance Pavement lifespan |
| Sumario: | This study aims to develop a predictive model for evaluating the structural condition of the asphalt layer and its impact on the serviceability of flexible pavement in the Puente Palca–Palca road, Huancavelica, Peru. The research focuses on 18 selected points along a 3-kilometer segment, employing techniques such as deflectometry, macrotexture analysis, skid resistance (CRD), degree of compaction, and the International Roughness Index (IRI). The study follows a quantitative, non-experimental, correlational-explanatory design, using non-linear regression models and multivariable analysis to predict the pavement's lifespan and functional capacity. The results indicate that compaction significantly affects deflectometry (R = 0.543), while asphalt content inversely influences macrotexture (R² = 0.5648). A critical reduction in surface texture (<1.0 mm) occurs when asphalt content exceeds 4.2%. Heavy traffic and climatic conditions further accelerate structural degradation, reducing pavement lifespan to 10-12 years without intervention. However, the predictive model extends this lifespan to 15 years, optimizing resources and lowering maintenance costs by 20%. The conclusion emphasizes that the developed predictive model for structural condition significantly impacts flexible pavement serviceability, improving its lifespan by 3-5 years and reducing repair costs, thereby enhancing safety and functionality in Huancavelica. |
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