International Roughness Index Prediction of Flexible Pavements Using Neural Networks

TLDR

International roughness index (IRI) is a widely accepted metric for pavement performance and ride quality. The study aims to develop an artificial neural network model to predict IRI for flexible pavements across wet‑freeze, dry‑freeze, wet‑no‑freeze, and dry‑no‑freeze climate zones. Using the LTPP database, the authors input climate variables (temperature, freezing index, humidity, precipitation) and traffic data (AADT, truck traffic) into a 7‑9‑9‑1 ANN trained on 50 % of the data, validated with RMSE, and further tuned with synthetic data sets split 70/15/15. The tuned ANN achieves a lowest RMSE of 0.01 on training data and 0.027 on measured data, demonstrating reasonable accuracy in predicting IRI.

Abstract

International roughness index (IRI) is a widely-accepted parameter that indicates pavement performance and ride quality. This study develops a prediction model for IRI using artificial neural networks (ANN) for flexible pavements located in wet-freeze, dry-freeze, wet no-freeze and dry no-freeze climate zones. The long-term pavement performance (LTPP) database is used for obtaining climate and traffic data. Annual average temperature, freezing index, maximum humidity, minimum humidity, precipitation, average daily traffic, and average daily truck traffic are considered as input parameters for predicting IRI. The proposed ANN model is trained with 50% of the available climate and traffic data and the remaining 50% of the data are used for testing the model. The comparison of LTPP recorded data and ANN predicted data is validated by calculating root mean square error (RMSE). The 7-9-9-1 ANN model with a hyperbolic tangent sigmoid transfer function generated the lowest RMSE of 0.01. The 7-9-9-1 ANN model is further tuned for robustness and consistency with several synthetic data sets and 70%, 15%, and 15% of the synthetic data sets are used to train, test, and validate, respectively, the ANN model. The ANN model predicts the IRI with reasonable accuracy and the lowest RMSE 0.027 in measured.