Artificial neural network-based investigation of factors impacting faulting in rigid pavements for dryfreeze and dry no-freeze climatic zone

Faulting, a critical distress in rigid pavements, poses a crucial challenge to road safety and maintenance. It is defined as the elevation difference of transverse joints in Jointed Plain Concrete Pavements, which is primarily caused due to environmental effects, subgrade properties, and accumulated traffic loads. Traditional regression models cannot often capture complex relations within pavement faulting and other detrimental effects on pavement, whereas Artificial Neural networks leverage data-driven machine-learning approaches to provide more accurate predictions. Datasets have been prepared from the LTPP database of dry climate zones. Environmental factors such as Yearly ESALs, Annual Precipitation, Annual Average Temperature, Freeze-Thaw Cycles, and structural properties like Pavement Thickness, Pavement Age, Tensile Strength, and Optimum Moisture Content (OMC) are considered the factors causing wheel path faulting. An Artificial Neural Network (ANN) based faulting prediction model is developed with one hidden layer and three neurons. 8 States over 30 years of lifespan are taken into consideration for this study. The final developed ANN model can predict the faulting in pavement sections accurately for any climatic region with an R2 value of 0.81. The correlation between the factors is studied as well and an ANN linear equation is also developed. The developed predictive ANN model equation will allow transportation engineers and contractors to easily predict Faulting in Jointed Plain Concrete Pavements (JPCP) which will assist in improving maintenance strategies to alleviate the effects of JPCP faulting.