An outstanding amount of funds are allocated to maintain road network conditions. To ensure the serviceability of roads, the accurate prediction of its roughness or International Roughness Index plays a pivotal role in road management. Artificial neural network, typically used in roughness prediction, is a powerful machine learning algorithm that learns complex patterns in data with non-linear relationships. However, it remains a black box solution and relies heavily on the utilized data and its internal structures, causing model's overfitting and instability. To address such issues, a physics-guided neural network modelling framework is proposed for short- and long-term predictions of roughness aimed at improving model's accuracy, uncertainty and stability. This framework fuses the output of physics-based model simulations along with field observational data acquired from the Long-Term Pavement Performance public database as inputs to develop a neural network architecture. Additionally, the framework uses a physics-based loss function in the network's learning process to ensure the predictions are consistent with the known physics. The performances are evaluated and compared to traditional artificial neural network. The comparison results indicate that the proposed modelling framework can increase the accuracy by 4%, and 26.08%, reduce the uncertainty by 4% and at least 22.15%, and improve the stability by 24.09% and by 46.34%, for one year and multi-year predictions, respectively. This framework offers great potential for accurate, reliable and stable predictions for engineering asset conditions by leveraging the complementary strengths of numerical simulations and data-driven models
