Asphalt concrete was originally invented with the purpose of carrying the high pressure and heavy loads generated from aircrafts [1]. In general, pavement consists of multiple layers to transfer the heavy loads to the soil without causing soil failure [2]. The process of asphalt pavement design consists of two main processes. The first process is the thickness design which focuses on estimating the required thickness of each pavement layer to transfer traffic load safely to the soil. The second process, which is the main focus of this paper, is called the asphalt mix design and it focuses on estimating the optimum aggregate and bitumen characteristics in the mix [3,4,5,6,7,8]. Usually, the asphalt mix design process is carried out through laboratory tests with the goal of estimating the optimum asphalt content (OAC) in the asphalt mix [9] as the OAC has a significant influence on the final performance of the mix [10]. Low asphalt content leads to harsh mix which causes durability issues, while high asphalt content leads to rutting, flushing, and insufficient air voids [11].
Marshall mix design method was firstly proposed in 1939 by Bruce Marshall in the Mississippi State Highway Department [1]. Marshall test was widely adopted in 1948 with slight modifications from country to country [9]. Currently, Marshall test remains the most common way used for estimating the optimum asphalt content [12, 13]. Marshall test procedure requires the preparation of at least fifteen samples for five different asphalt contents (three samples for each) then draw the design curves [14, 15] and estimate the OAC that satisfies a predefined criterion which depends on the Maximum stability, Maximum density or unit weight, predefined air voids percent, and a min value for voids in mineral aggregate [9]. Finally, the OAC is estimated as the average value that corresponds to the maximum stability, maximum unit weight, and prespecified air voids percentage [14, 15]. As a result, the estimation of the OAC is subjected to significant deviations, as it relies on the average value of a group of different ACs. Additionally, Marshall design method requires significant time for sample preparation and testing [14, 16]. Consequently, a large number of studies focus on developing alternative methods for Marshall test to save time.
AC is a composite material that consists of aggregate and bitumen. Aggregate makes up a high proportion of volume and mass of mixtures (around 95% of the mix weight), hence it is considered as the most important constituent of asphalt concrete [17] so characteristics of the asphalt mix mainly depend on the aggregate used and its gradation [18]. Thus, it is predicted that the aggregate properties have an enormous impact on the mixture properties. Aggregate gradation can be defined as the distribution of particle sizes expressed as a percentage of the total weight [18, 19] and considered as the centerpiece property of aggregate which needs careful consideration due to its effect on mix properties and performance of HMA mixtures, including air void, stability, stiffness, durability, permeability, workability, fatigue resistance, frictional resistance, resistance to moisture damage [18, 20] and also rutting resistance of asphalt concrete under traffic and environmental loads [21]. As a result, this parameter is considered as a very important parameter in the process of mixture design. For example, changing the aggregate gradation changes the required asphalt content because the asphalt content is responsible for coating the aggregate surface and filling the voids between the aggregate particles [16]. Moreover, the main sources of the mix strength or stability are the friction between the aggregate particles, interlocking resistance between the aggregate particles and the consistency of the bitumen used [14, 17].
Therefore, the main objective of this paper is to test the effectiveness of using ANN for accurate and fast prediction of the asphalt mix properties (mechanical and volumetric properties) to enhance the mix design process in the laboratory. The ANNs developed in this study were trained and tested on the basis of laboratory data of HMAs for two types of aggregate. The tests were carried out in the Highway and Airport Engineering Research Laboratory, Cairo University, Egypt for the most common aggregate (3D, 4C) and bitumen (60/70) used in Egypt.
Over the past few years, ANNs have been used in the prediction problem and indeed in the pavement engineering field. The study by Kaseko and Ritchie (1993) is one of the oldest studies that employed ANN for the detection of pavement cracks [22]. This was followed by Gagarin, Flood, and Albrecht (1994) study that employed ANN for the estimation of different truck attributes from the strain response readings from bridges [23]. In 1995, Cal employed an ANN that takes plasticity index, water content, and liquid limit as an input for the prediction of the soil classification [24]. In 1998, Roberts and Attoh-Okine employed an ANN for the prediction of the pavement condition [25]. Similarly, the study by Attoh-Okine (2001) proposed an ANN that uses the pavement characteristics and the surrounding conditions such as weather, age, and traffic condition in the prediction of the pavement condition [26]. In 2010, Tapkın et al. proposed an ANN for estimating Marshal test results for dense asphalt mixes that are modified with different types and percentages of an additive which is the polypropylene [10]. In 2014, Khuntia et al. used ANN for the prediction of Marshall test results for the polyethylene modified mixes using two techniques which are ANNs and support vector machine [27]. In 2016, Ozturk et al. proposed an ANN for the prediction of the volumetric properties of the asphalt mix [28]. In 2017, Ivica and Ivan proposed an ANN for the prediction of the air voids and the asphalt content of asphalt mixes in Croatia [29]. In 2018, Baldo et al. used the ANN technique for the prediction of the mechanical properties of asphalt mixes (stability and flow) [9]. In 2019, Nguyen et al. used artificial intelligence techniques for the prediction of the characteristics of the stone matrix asphalt [30].
Since 2006, when Hinton proposed several techniques for deep learning structures, configuring deep networks with more than three layers have shown widespread success in training neural networks. Artificial Neural Networks (ANN) is a learning algorithm that imitates the human neural system. An ANN consists of multiple nodes, called neurons, that communicate through synapses. Typically, there are three sets of layers: input layer, hidden layer, and output layer and each type of layer plays unique roles. The Input layer receives input information, the output layer yields output signals, and the intermediate layer (hidden layer) receives signals from the input layer and manipulates the information to give results to the output layer. An ANN model can have multiple intermediate layers [31]. In general, ANNs can be defined as parallel distributed processors that have the potential to store the learned knowledge and use it in the future. The fundamental unit of any ANN is the artificial neuron. The function of the neuron is to process the input signals and modulate its own response through an activation function or sometimes called the transfer function. The activation function determines the interruption or transmission of the outgoing impulse. Each neuron computes a weighted sum of elements of the input vector (Xs) through weights associated with the connections (W) [32]. Then, the neuron output value is calculated by applying the assigned transfer function to the weighted sum as follows:
There are many types of neural networks such as feedforward and feedback. Also, there are many types of training techniques deepening on the data such as supervised and unsupervised learning. In this study, the supervised learning is employed for the proposed ANN. Additionally, the backpropagation learning algorithm is the most common training algorithm used for the training of ANN, so the backpropagation technique is used for the training of the proposed ANN in this paper. Additionally multiple ANN will be tested to optimize the ANN architecture.