Development of Artificial Neural Network Predictive Models for Populating Dynamic Moduli of Long-Term Pavement Performance Sections

Abstract

This paper presents a set of dynamic modulus (|E*|) predictive models to estimate the |E*| of hot-mix asphalt layers in long-term pavement performance (LTPP) test sections. These predictive models use artificial neural networks (ANNs) trained with different sets of parameters. A large national data set that covers a substantial range of potential input conditions was utilized to train and verify the ANNs. The data consist of mixture dynamic moduli measured with two test protocols: the asphalt mixture performance tester and AASHTO TP-62, under different aging conditions. The data include binder dynamic moduli values measured under different aging conditions. The ANN predictive models were trained and ranked with a common independent data set that was not used for calibrating any of the ANN models. A decision tree was developed from these rankings to prioritize the models for any available inputs. Next, the models were used to estimate the |E*| for the LTPP database materials and ultimately to characterize the master curve and shift factor function. To ensure adequate data quality, a series of quality control checks was developed and applied to grade the inputs and outputs for each prediction. Approximately 30% to 50% of all LTPP layers contained enough information to obtain reliable moduli predictions.

Utilising neural networks and closed form solutions to determine static creep behaviour and optimal polypropylene amount in bituminous mixtures

“…Tsai et al 54 calibrated mechanistic-based models to field data to produce a design process for predicting reflection cracks. Sakhaeifar et al 55 presented a set of dynamic modulus (|E*|) predictive models to estimate the |E*| of hot-mix asphalt layers in long-term pavement performance (LTPP) test sections. Tapkın et al 23 presented another application of neural networks for the prediction of Marshall test results for polypropylene modified asphalt mixtures.…”

Prediction and sensitivity analysis of CNTs-modified asphalt’s adhesion force using a radial basis neural network model

“…Sakhaeifar et al used MLPs to develop predictive model measuring the dynamic modulus ⃒E*⃒ of hot-mix asphalt (HMA). The dynamic modulus is a basic property of defining stiffness performance of HMA [19]. They trained and validated the MLP models for predicting the ⃒E*⃒ of asphalts having varied long-term aging conditions and found improved accuracy in long-term performance analysis of asphalts properties.…”

Improved method of considering air void and asphalt content changes on long-term performance of asphalt concrete pavements

“…These recent models include other full regression models (Bari and Witczak 2006), micromechanically motivated regression models (Al-Khateeb et al 2006), artificial neural network-based models (Ceylan et al 2008, Sakhaei Far et al 2010) and local calibration models (Apeagyei et al 2012). In all cases, the models rely on large data-sets containing mixtures of varying composition and properties.…”