Mechanical Characterization of Industrial Waste Materials as Mineral Fillers in Asphalt Mixes: Integrated Experimental and Machine Learning Analysis

Abstract

In this study, the effect of seven industrial waste materials as mineral fillers in asphalt mixtures was investigated. Silica fume (SF), limestone dust (LSD), stone dust (SD), rice husk ash (RHA), fly ash (FA), brick dust (BD), and marble dust (MD) were used to prepare the asphalt mixtures. The obtained experimental results were compared with ordinary Portland cement (OPC), which is used as a conventional mineral filler. The physical, chemical, and morphological assessment of the fillers was performed to evaluate the suitability of industrial waste to replace the OPC. The volumetric, strength, and durability of the modified asphalt mixes were examined to evaluate their performance. The experimental data have been processed through artificial neural networks (ANNs), using k-fold cross-validation as a resampling method and two different activation functions to develop predictive models of the main mechanical and volumetric parameters. In the current research, the two most relevant parameters investigated are the filler type and the filler content, given that they both greatly affect the asphalt concrete mechanical performance. The asphalt mixes have been optimized by means of the Marshall stability analysis, and after that, for each different filler, the optimum asphalt mixtures were investigated by carrying out Indirect tensile strength, moisture susceptibility, and abrasion loss tests. The moisture sensitivity of the modified asphalt mixtures is within the acceptable limit according to the Indian standard. Asphalt mixes modified with the finest mineral fillers exhibited superior stiffness and cracking resistance. Experimental results show higher moisture resistance in calcium-dominant mineral filler-modified asphalt mixtures. Except for mixes prepared with RHA and MD (4% filler content), all the asphalt mixtures considered in this study show MS values higher than 10 kN, as prescribed by Indian regulations. All the values of the void ratio for each asphalt mix have been observed to range between 3–5%, and MQ results were observed between 2 kN/mm–6 kN/mm, which falls within the acceptable range of the Indian specification. Partly due to implementing a data-augmentation strategy based on interpolation, the ANN modeling was very successful, showing a coefficient of correlation averaged over all output variables equal to 0.9967.

Road Pavement Asphalt Concretes for Thin Wearing Layers: A Machine Learning Approach towards Stiffness Modulus and Volumetric Properties Prediction

“…Nonetheless, ANNs are capable to identify the relationship or pattern that links input predictors to target variables because connections' weights and biases are set by a supervised training process that aim to minimize the predicting error of the experimental targets. This study focuses on shallow neural networks (SNNs), i.e., three-layer perceptron networks, which have been shown to solve arbitrarily well any multidimensional input-target fitting problem by providing a sufficient number of neurons in its only hidden layer [31][32][33][34][35]36]. The proposed SNN consists of a 4-neurons input layer (one neuron for each input feature), a N-neurons hidden layer whose processed output is passed to a hyperbolic tangent (Tanh -Eq.…”

Mineral powder based on basalt insulation waste for asphalt concrete

“…Mineral powder from waste materials such as industrial by-products and mining waste provides a valuable alternative to traditional asphalt additives [8][9][10]. These include materials such as fly ash, slag and other mineral-rich wastes that can be finely ground to produce a high-quality powder suitable for asphalt concrete production.…”

Performance Evaluation of Hospital Waste Ash-Modified Asphalt Mixtures

“…Reusing different waste products also benefits the environment because it frees up space for permitted landfills and deposits, which promotes resource efficiency. Hence, using waste in asphalt mixtures would lead to environmentally friendly and long-lasting pavement construction, improving smart cities' overall sustainability and getting them closer to reaching SDG-11 [8,9].…”