Design, Construction, and In-Service Causes of Premature Pavement Deterioration: A Fuzzy Delphi Application

Abstract

Flexible pavements are prone to premature deterioration, and researchers are unresolved regarding the importance of the underlying causes resulting in inappropriately selected modelling parameters and increased uncertainty in predicting subsequent behaviour and performance. A windshield survey, literature survey, and fuzzy Delphi study are undertaken as complementary approaches to costly conventional investigations to identify reasons for flexible pavement deterioration in the design, construction and lifespan phases. Overall, the results revealed that the lifespan phase consists of the most contributors to pavement deterioration, which is approximately twice as much as the design and construction phases. However, the findings suggest that most causes of deterioration in the lifespan phase can be attributed to deficiencies in the preceding phases. Experts believe that structural and traffic are the most significant contributors to pavement deterioration, more so than construction, environment and maintenance factors. Additionally, the surface and subgrade layers were deemed to be the most problematic. Applying the Fuzzy Delphi method minimises the ambiguities associated with the causes of pavement deterioration identified in the literature and is advantageous for limited data. This study proposes measures for improving the design and construction of more sustainable tropical pavements. Improved knowledge of the causes of deterioration is vital for selecting the appropriate design, construction, and maintenance strategies.

Ensemble Learning Approach for Developing Performance Models of Flexible Pavement

“…The third performance measure is the mean absolute error (MAE), which is calculated using the formula shown in Equation (11) and represents the average absolute difference between observed and predicted values. The final statistical metric is root mean square error (RSME), which calculates the measure of the magnitude of the prediction error and is presented in Equation (12). In the set of equations below, y i is the actual value, ŷi is the predicted value by the model output, y i is the mean of the actual values, and N is the total number of observations.…”

“…Roadway pavements are subject to deterioration due to a variety of factors, such as environmental disasters, aging, design flaws, etc. Studies demonstrate that pavement deterioration is primarily influenced by structural and traffic-related factors [10][11][12]. Additionally, environmental factors intensify several of these deterioration mechanisms, including traffic fatigue and surface temperature stresses, which have an impact on the functionality and serviceability of highway networks [13].…”

Influencia de la escoria metalúrgica en las propiedades mecánicas del pavimento flexible

“…Sin embargo, el mantenimiento y la reconstrucción inadecuados, a menudo restringidos por limitaciones presupuestarias, pueden conducir a un deterioro prematuro del pavimento (Yan et al, 2021). Por otra parte, en el caso de la infraestructura vial, a pesar de su contribución al crecimiento económico y la conectividad entre diversas regiones, se ve obstaculizada por las deficientes condiciones del pavimento flexible, lo que genera problemas en el transporte vehicular, incluyendo vibraciones no deseadas en los vehículos, accidentes de tránsito y un aumento en el consumo de combustible (Milling et al, 2023;Monge & Garrido, 2020).…”