Creating Rutting Prediction Models through Machine Learning Techniques Utilizing the Long-Term Pavement Performance Database

Abstract

Over time, roads undergo deterioration caused by various factors such as traffic loads, climate conditions, and material properties. Considering the substantial global investments in road construction, it is crucial to periodically assess and implement maintenance and rehabilitation (M and R) plans to ensure the network’s acceptable level of service. An integral component of the M and R plan involves utilizing performance prediction models, especially for rutting distress, a significant issue in asphalt pavement. This study aimed to develop rutting prediction models using data from the Long-Term Pavement Performance (LTPP) database, employing several machine learning techniques such as regression tree (RT), support vector machine (SVM), ensembles, Gaussian process regression (GPR), and Artificial Neural Network (ANN). These techniques are well-known for effectively handling extensive and complex datasets. To achieve the highest modeling accuracy, the parameters of each model were meticulously fine-tuned. Upon evaluation, the results revealed that the GPR models outperformed other techniques in various metrics, including Root Mean Square Error (RMSE), R-squared, Mean Absolute Error (MAE), and Mean Square Error (MSE). The best GPR model achieved an RMSE of 1.96, R-squared of 0.70, MAE of 1.32, and MSE of 109.33, indicating its superior predictive capabilities compared with the other machine learning methods tested in this study. Comparison Analysis was made for 10 randomly selected sections on our novel machine learning model that outperforms existing models, with an R2 of 0.989 compared with 0.303 and 0.3095 for other models. This demonstrates the potential of advanced machine learning in accurate rut depth prediction across diverse climates, aiding pavement management decisions.

Machine Learning Modeling of Wheel and Non-Wheel Path Longitudinal Cracking

“…Numerous studies have utilized computational intelligence methods to overcome the limitations of traditional empirical-based pavement performance prediction models [30][31][32][33][34][35][36][37][38][39]. In predicting pavement roughness, neural networks have demonstrated superior accuracy over conventional prediction models [37][38][39].…”

Ensemble Learning Approach for Developing Performance Models of Flexible Pavement

“…By incorporating integral channel features and a random forest algorithm, the detection framework successfully captures the inherent structure of road cracks, which improves the traditional crack detection approach [18][19][20]. Another random forest study on the LTPP database demonstrated that seal coat treatments contribute to the reduction of pavement surface cracking, with pavement condition and seal coat thickness proving critical for rutting and International Roughness Index (IRI) performance [21,22]. The significant impact of mixture gradation and aggregate-specific gravity on alligator cracking in asphaltic concrete (AC) pavement has been identified using random forest models [23].…”

Predicting Rutting Development Using Machine Learning Methods Based on RIOCHTrack Data

“…Ref. [17] Alnaqbi established several ML methods (regression trees, ANN, SVM, GPR, and ANN) to predict rutting using data from LTPP. However, the database they rely on to build models is LTPP.…”