Development of A Surface Distress Index Prediction Framework using Artificial Neural Networks for Roads Infrastructure Management
DOI:
https://doi.org/10.52920/jttl.v6i2.487Keywords:
ANN, predictive modeling, Road Conditions, road maintenance, SDIAbstract
Road infrastructure is a crucial element of urban development, affecting transportation efficiency and road safety. Conventional techniques for evaluating road conditions often fail to capture the full complexity of deterioration and damage over time. This study explores the potential of Artificial Neural Networks (ANN) in predicting road conditions to optimize sustainable infrastructure management. The model was trained using a dataset comprising 2.467 observations of road damage collected over five years from 42 urban road sections. By integrating surface damage parameters such as crack area, crack gap, pothole, and rutting, the model was configured with 4 neurons in the first hidden layer and 4 neurons in the second hidden layer. The predictive model for the Surface Distress Index (SDI) revealed excellent performance, achieving an R² value of 0.95, a Mean Absolute Error (MAE) of 0.02, and a Root Mean Square Error (RMSE) of 0.05. The model can be effectively applied to assess road conditions with a high level of precision and reliability.
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