Artificial Neural Networks and Multiple Linear Regression in Pavement Deterioration Forecasting

Abstract

Assessing pavement conditions in Nepal is costly and time-consuming, with rising traffic and aging infrastructure making maintenance increasingly challenging. This study developed and compared pavement deterioration models to predict the Surface Distress Index (SDI) without manual assessment, using historical road data. SDI was modeled as a function of five key factors: International Roughness Index (IRI), pavement age, total annual rainfall, annual temperature range, and commercial vehicle traffic. Data were collected from relevant government sources, covering 157 road sections with a combined length of 15,783 km, for the period from 2012 to 2022. Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models were developed for SDI prediction. MLR analysis, conducted in Microsoft Excel, assessed statistical significance through ANOVA, R² values, and regression coefficients. In contrast, ANN modeling utilized a Multi-Layer Perceptron (MLP) architecture implemented in TensorFlow and Keras. The ANN model was optimized through iterative experimentation with varied architectures, employing ReLU activation and the Adam optimizer for adaptive learning. The study evaluated a range of architectures, beginning with simple single-layer networks and extending to Deep Neural Networks (DNNs) with up to four hidden layers. Results showed that, during model development, MLR achieved an R² of 0.735, whereas the ANN model, with a 5-232-1 structure and 104 epochs, outperformed MLR with an R² of 0.809. Validation of both models indicated strong alignment between observed and predicted values, with ANN demonstrating superior predictive accuracy (R² = 0.816) compared to MLR (R² = 0.74). The error histogram further confirmed ANN’s better performance, which confirms its improved reliability. The study highlights the effectiveness of both models while emphasizing ANN’s advantage in capturing complex nonlinear relationships. These findings suggest that integrating ANN into Nepal’s pavement management framework can enhance predictive accuracy, reduce assessment costs, and support more efficient maintenance planning.