Prediction of the optimum asphalt content using artificial neural networks

Abstract

The performance of the asphalt mix is significantly influenced by the optimum asphalt content (OAC). The asphalt content is responsible for coating the aggregate surface and filling the voids between the aggregate particles. Thus, the aggregate gradation has a significant influence on the required asphalt content. The Marshall design process is the most common method used for estimating the OAC, and this process is called the asphalt mix design. However, this method is time consuming, labor intensive, and its results are subjected to variations. Thus, this paper employs the artificial neural network (ANN) to estimate the OAC from the aggregate gradation for the two most common gradations used in asphalt mixes in Egypt (3D, 4C). Results show that the proposed ANN can predict the OAC with a coefficient of correlation of 0.98 and an average error of 0.026%. As a result, a new approach for the Marshall test can be adopted using results of the proposed ANN, and only three specimens, instead of fifteen, are prepared and tested for estimating the remaining parameters. This approach saves the time, effort, and resources required for estimating the OAC. Additionally, the ANN was validated with previously developed models, and the ANN shows promising results.

Evaluation of the Hot Asphalt Mix Aggregate Gradation Using Bailey Method: A State-of-the-Art

“…In this study, 103 asphalt mix samples, that were used in the construction of roads in Egypt between 2015 and 2019, will be evaluated using the three Bailey ratios to evaluate their performance and provide some indication about their resistance and performance in the field. Thus, this study will focus on the two common aggregate gradations used for the construction of roads in Egypt (4C and 3D) [[4]- [5]]. Additionally, the aggregate type used in the samples tested is dolomite, which is the main aggregate type used in the asphalt mixes in Egypt [ [4]].…”

“…Thus, the appropriate planning, designing, and construction of road networks with the proper materials and soil properties is an essential step for the construction of these roads. Flexible pavement is the most commonly type of pavement used in Egypt [ [4]- [5]]. In general, flexible pavement consists of a surface pavement layer in direct contact with the running traffic followed by aggregate layers on top of the soil.…”

Deep Neural Network Models for the Prediction of the Aggregate Base Course Compaction Parameters

“…Thus, this technique has been employed for the prediction problem in the civil engineering field, and in the pavement engineering field. For example, Othman and Abdelwahab (2021) [3] utilized ANNs for predicting the optimum asphalt content of hot mix asphalt samples. Similarly, Othman [31] utilized ANNs to develop prediction models that can be used for predicting the characteristics of the hot asphalt mixes.…”

“…Figure 3 shows the proposed general ANN structure followed in this paper. The number of hidden layer and neurons per hidden layers used in this study are based on the architectures of ANNs that were tested in previous studies as the maximum number of layers tested in the previous studies is four, and the maximum number of neurons employed in previous studies is 20 neurons per hidden layer [3,16,25,28,31]. Additionally, the weights of the connections are randomly assigned before launching the training process.…”

“…It is known that the flexible pavement consists of different layers to transfer the traffic load to the soil. The main objective of the structure design is to make sure that the transferred load does not exceed the soil strength to avoid failure [1][2][3]. The wearing surface or the surface course is the layer in direct contact with traffic, and it provides characteristics such as friction, smoothness, noise control, rut resistance, and drainage.…”