article · Journal of Road Engineering
Effective management of road networks relies on monitoring pavement conditions to plan timely maintenance and minimise expenditure. This study evaluates computational methods to predict the Pavement Condition Index for flexible road surfaces. Multiple linear regression, artificial neural networks, and fuzzy logic inference models were developed and compared using field data collected in 2018 and 2021. The models incorporated eight distinct pavement distress factors, including rutting, fatigue cracking, block cracking, longitudinal cracking, transverse cracking, patching, potholes, and delamination. Performance assessment using the coefficient of determination, root mean squared error, and mean absolute error showed that artificial neural networks outperformed the other techniques. The neural network models yielded significantly lower prediction errors and higher correlation values, demonstrating their superior capability in forecasting pavement deterioration from observed surface defects.
Maintaining road networks requires substantial public funding, and delaying repairs leads to costly structural damage. By predicting pavement degradation accurately from standard visible surface distresses, transportation authorities can schedule timely interventions. Using neural networks improves the reliability of condition monitoring, helping agencies allocate limited maintenance budgets efficiently and keep roads safe for the travelling public.
The findings can inform predictive modules within pavement management systems utilised by municipal road authorities and highway maintenance contractors. The approach relies on standard distress survey metrics, making it suitable for incorporation into asset management software. Having been tested on historical field datasets, the method is at an applied research stage and would require software integration and validation across broader geographical regions before routine operational deployment.
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Pavement management systems (PMS) are used by transportation government agencies to promote sustainable development and to keep road pavement conditions above the minimum performance levels at a reasonable cost. To accomplish this objective, the pavement condition is monitored to predict deterioration and determine the need for maintenance or rehabilitation at the appropriate time. The pavement condition index (PCI) is a commonly used metric to evaluate the pavement's performance. This research aims to create and evaluate prediction models for PCI values using multiple linear regression (MLR), artificial neural networks (ANN), and fuzzy logic inference (FIS) models for flexible pavement sections. The authors collected field data spans for 2018 and 2021. Eight pavement distress factors were considered inputs for predicting PCI values, such as rutting, fatigue cracking, block cracking, longitudinal cracking, transverse cracking, patching, potholes, and delamination. This study evaluates the performance of the three techniques based on the coefficient of determination, root mean squared error (RMSE), and mean absolute error (MAE). The results show that the R2 values of the ANN models increased by 51.32%, 2.02%, 36.55%, and 3.02% compared to MLR and FIS (2018 and 2021). The error in the PCI values predicted by the ANN model was significantly lower than the errors in the prediction by the FIS and MLR models.
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DOI: 10.1016/j.jreng.2023.04.002
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