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Complexity in the behaviour of an asphalt binder is further escalated with geopolymer (fly ash and alkali liquid) modification, thus making it difficult to accurately predict the performance of the binder. This study employs artificial neural network modelling to predict the complex shear modulus, storage modulus, loss modulus and phase angle outcomes of experimental results from dynamic shear rheometer (DSR) oscillation tests under four separate scenarios. The proposed artificial neural network models received test conditions (temperature and frequency) and three different geopolymer concentrations (3%, 5% and 7% by the weight of bitumen) as the predictor parameters. The variants of the optimal algorithms were Levenberg-Marquardt (LM), Scaled conjugate gradient and Polak-Ribiere conjugate gradient (CPG) training algorithms with different combinations of network structures and tan-sig and log-sig as activation functions. The coefficient of determination, covariance and root mean square error (RMSE) were used as statistical measures of model prediction performance. Based on the statistical performance indicators, the LM algorithm with a 3-5-1 network architecture and tan-sig as the activation function was the best performing model for predicting the complex modulus with R 2 values of 0.996 for the training dataset and 0.971 for the testing dataset and RMSE values of 0.118 and 0.139 for the training and testing datasets, respectively. Furthermore, it was observed that the least efficient model was the phase angle prediction model developed with the CPG training algorithm, which had a 3-8-1 network architecture and log-sig as the activation function. The model yielded R 2 values of 0.909 and 0.829 for the training and testing datasets, respectively. Poor prediction performance for the testing dataset indicated that the model was unable to learn complexity in the data and would perform below a significance level of 0.90 in predicting using untrained data.

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The current study investigated the physical properties and rheological characteristics of asphalt cement (AC) modified by nano ironoxide (Fe3O4) and nano silica (SiO2) particles. Seven different blends including base AC, Acrylete-StyreneAcrylonitrile (ASA)/ Nanosilica (Si) and ASA/ Nanoironoxide (Fe) were the subject of experimental investigations. ASA was used at 5% for all modified blends while the nanomaterials were blended in 3, 5 and 7% concentrations by the weight of AC. Temperature susceptibility, rotational viscosity (RV) and storage stability for the base and polymer nanocomposite modified asphalt cement (PNCAC) were evaluated by the physical testing procedures while the frequency sweep test and the multiple stress creep recovery (MSCR) tests were conducted using a dynamic shear rheometer. The results showed that, the viscoelastic properties of the ASA/Si and ASA/Fe modified binders were improved when subjected to a range of temperatures and loading conditions. ASA/Fe composite modified AC demonstrated superior high temperature performance characteristics while ASA/Si composite modified AC although presented less significant improvement at the high temperature conditions, it was also able to improve the intermediate temperature fatigue resistance of asphalt more than the ASA/Fe composite modified AC. The optimum concentration of additives were found to be 3 and 5% for the ASA/Fe and ASA/Si modified AC respectively. Further addition of nanomaterials beyond the abovementioned concentrations resulted in degradation of the enhancement in physical and rheological properties of PNCAC, which was associated to the occurrence of agglomeration of nanoparticles and phase separation between the polymer-nanomaterial-asphalt matrix.

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The effect of aluminum oxide nanoparticles (Al2O3) on the 60/70 penetration of asphalt cement (AC) was investigated in terms of the physical and rheological characteristics by using the Superpave testing procedures. Al2O3 at 3, 5, and 7% concentrations were blended with 60/70 penetration of grade AC. Conventional testing procedures were adopted regarding the physical characteristics, while dynamic shear rheometer (DSR) testing procedures were conducted to evaluate the high and low temperature failure parameters. In addition, heuristic modelling techniques, artificial neural networks (ANN), and support vector machines (SVM) were employed to predict the performance characteristics of AC by using the mechanical testing conditions. The frequency sweep test and multiple stress creep recovery (MSCR) test results revealed that the optimum composition of Al2O3 was at 5% concentration considering the high temperature performance characteristics since further addition of the Al2O3 resulted in degradation in the enhanced properties due to agglomeration of the nanoparticles in the blend. On the contrary, Al2O3 5% demonstrated the lowest viscoelastic behavior at intermediate temperatures. The higher complex modulus ( G ∗ ) and lower phase angle ( δ ) parameters indicated that the increase in stiffness due to the modification process was at the cost of losing elastic properties against fatigue cracking. Moreover, based on the statistical performance indicator, coefficient of determination (R2), it was observed that the ANN models for predicting G ∗ and δ achieved a prediction accuracy of 0.989 and 0.911 while SVM models were able to achieve 0.984 and 0.929, respectively, considering the training datasets. On the other hand, it was noted that SVM models outperformed the ANN models in terms of a smaller gap between the results obtained from the training and testing datasets. The difference between the training and testing datasets for G ∗ and δ parameters for the SVM models were 3.2% and 6.8% while for the ANN models, the differences were 11.6% and 9.5%, respectively, indicating that the ANN models were more prone to the overfitting phenomenon.

The current study focuses on the effect of Acrylate-styrene-acrylonitrile (ASA)/Nanosilica (Si) modified binders on the complex modulus (G*) and rutting resistance parameters (G*/ sinδ) of Asphalt Cement (AC). Four different blends including the base binder and the polymer nanocomposites which were formed by blending 5% ASA to base binder with the addition of nanosilica at 3, 5 and 7% by the weight were the subject of investigations. Conventional and Dynamic Shear Rheometer (DSR) testing procedures were conducted as well as morphology analysis using Fourier Transform Infrared Spectroscopy (FT-IR). The rheological characteristics of AC were analysed by master curves, isochronal plots and rutting resistance parameter plots. Test results revealed that G* and G*/ sinδ of all modified samples were significantly enhanced compared to the base binder. Multiple Stress Creep Recovery Test (MSCRT) conducted at 100 Pa and 3200 Pa showed that, non-recoverable compliance was reduced and elastic recovery of modified binders were improved. Optimum concentration was found to be 5% ASA/Si composite, as further addition of polymer nanocomposite resulted in lower enhancement in the rheological properties of modified AC due to the occurrence of agglomeration between the composite and the base binder.

Previous parallel computing implementations for Computational Fluid Dynamics (CFD) focused extensively on Complex Instruction Set Computer (CISC). Parallel programming was incorporated into the previous generation of the Raspberry Pi Reduced Instruction Set Computer (RISC). However, it yielded poor computing performance due to the processing power limits of the time. This research focuses on utilising two Raspberry Pi 3 B+ with increased processing capability compared to its previous generation to tackle fluid flow problems using numerical analysis and CFD. Parallel computing elements such as Secure Shell (SSH) and the Message Passing Interface (MPI) protocol were implemented for Advanced RISC Machine (ARM) processors. The parallel network was then validated by a processor call attempt and core execution test. Parallelisation of the processors enables the study of fluid flow and computational fluid dynamics (CFD) problems, such as validation of the NACA 0012 airfoil and an additional case of the Laplace equation for computing the temperature distribution via the parallel system. The experimental NACA 0012 data was validated using the parallel system, which can simulate the airfoil's physics. Each core was enabled and tested to determine the system's performance in parallelising the execution of various programming algorithms such as pi calculation. A comparison of the execution time for the NACA 0012 validation case yielded a parallelisation efficiency above 50%. The case studies confirmed the Raspberry Pi 3 B+'s successful parallelisation independent of external software and machines, making it a self-sustaining compact demonstration cluster of parallel computers for CFD.