Evaluation of neural network models for prediction of the performance of bituminous mixes R. Vitoi* , C. Prioste* , L. Carvalho* , G. L. Marques * , M. R. C. Farage* , L. Goliatt* Federal University of Juiz de Fora CEP 36036­330, Juiz de Fora, MG Brazil Keywords: bituminous mixes, neural networks, extreme learning machines, resilient modulus, predictive model, fully connected architecture Corresponding author : Leonardo Goliatt, Faculty of Engineering, Federal University of Juiz de Fora, CEP 36036­330, Juiz de Fora, MG, Brazil +55 32 21023469 leonardo.goliatt@ufjf.edu.br The pavement structure is a combination of subbase , base course, and surface course. The surface course is one or more layers of a pavement structure designed to accommodate the traffic load, the top layer of which resists skidding, traffic abrasion, and the disintegrating effects of climate. Surface layer may consist of asphalt (also called bituminous) concrete, resulting in a so­called flexible pavement, or Portland cement concrete, classified as rigid pavement. The elastic modulus for pavement materials is most commonly characterized in terms of the resilient modulus (MR). This modulus is defined as the ratio of the applied cyclic stress to the recoverable (elastic) strain after many cycles of repeated loading and thus is a direct measure of stiffness for unbound materials in pavement systems. It is the single most important unbound material property input in most current pavement design procedures. Determined by dynamic tests, MR is one of the main mechanical properties of asphalt mixes and it is used in the design of asphalt pavements to compute stresses, strains, and deformations induced in the pavement structure by the applied traffic loads. MR is influenced by several parameters related to the type of bituminous mix, the amount and particle size of the aggregate, content and type of binder asphalt, as well as the building technique and the degree of compaction adopted in the preparation of the material. Besides, the temperature is an important parameter, since it can change the viscosity of the mix and may affect considerably its compactibility . The protocols for resilient modulus testing in the laboratory usually involve repeated analyzes resulting in general in time consuming tasks. An interesting alternative relies on machine learning tools as surrogate models to expensive laboratory tests. Several machine learning tools have been explored to predict outcomes of interest. Among them, the Neural Networks (NN) are a powerful and accurate technique to deal with regression or approximation problems. The objective of this work is to evaluate the performance of NN models to predict the resilient modulus value of dense asphalt mixes. The parameters considered in the analyzes are the aggregate granulometry , kind of asphalt binder, asphalt binder content, the kind of compacting and temperature. A total of 700 test was used to populate an experimental database resulting in 234 distinct compositions, characterized according to the aggregate granulometry , volumetric properties, bituminous mix properties, and temperature. A Multilayer Perceptron (MLP) and Extreme Learning Machine (ELM) neural network models were implemented and tested. MLP implements sigmoid activation function and it uses standard or fully connected feed­forward architecture. The MLP is trained using the backpropagation algorithm and allows for several layers with different number of neurons in each one. ELM is a feed­forward neural network with one hidden layer. Unlike traditional implementations, ELM chooses the input­hidden weights at random and employs a linear model to learn the hidden­output weights. Although simpler than MLP, ELM is a faster since it avoids a time consuming training procedure such as back­propagation. The Root­Squared Mean Error (RMSE) was used to assess the performance of each model. In order to obtain a better fit, a set of values was chosen for each parameter of MLP and ELM. The database was split in training and test sets and grid search with cross­validation was conducted to find the parameter settings which leads to the minimal errors. The computational experiments indicate that the MLP with two or three layers with few neurons leads to better results than ELM. When compared to MLP, ELM needs a higher number of neurons in the hidden layer to obtain similar results considering the RMSE. Also, using a fully connected layer architecture does not improve the results in MLP. The computational methodology focuses on the assessment of prediction of the resilient modulus, and can potentially replace unnecessary destructive tests, guiding the experiments to a more rational use of the materials, which helps to save laboratory resources.
