Rutting has been considered the most serious distress in flexible pavements for many years. Flow number is an explanatory index for the evaluation of the rutting potential of asphalt mixtures. In this study, a promising variant of genetic programming, namely, gene expression programming (GEP), is utilized to predict the flow number of dense asphalt-aggregate mixtures. The proposed constitutive models relate the flow number of Marshall specimens to the coarse and fine aggregate contents, percentage of air voids, percentage of voids in mineral aggregate, Marshall stability, and Marshall flow. Different correlations were developed using different combinations of the influencing parameters. The comprehensive experimental database used for the development of the correlations was established on the basis of a series of uniaxial dynamic-creep tests conducted in this study. Relative importance values of various predictor variables were calculated to determine their contributions to the flow number prediction. A multiple-least-squares-regression (MLSR) analysis was performed to benchmark the GEP models. For more verification, a subsequent parametric study was carried out, and the trends of the results were confirmed with the results of previous studies. The results indicate that the proposed correlations are effectively capable of evaluating the flow number of asphalt mixtures. The GEP-based formulas are simple, straightforward, and particularly valuable for providing an analysis tool accessible to practicing engineers.
Rutting continues to be one of the principal distresses in asphalt pavements worldwide. This type of distress is caused by permanent deformation and shear failure of the asphalt mix under the repetition of heavy loads. The Hamburg wheel tracking test (HWTT) is a widely used testing procedure designed to accelerate, and to simulate the rutting phenomena in the laboratory. Rut depth, as one of the outputs of the HWTT, is dependent on a number of parameters related to mix design and testing conditions. This study introduces a new model for predicting the rutting depth of asphalt mixtures using a deep learning technique - the convolution neural network (CNN). A database containing a comprehensive collection of HWTT results was used to develop a CNN-based machine learning prediction model. The database includes 10,000 rutting depth data points measured across a large variety of asphalt mixtures. The model has been formulated in terms of known influencing mixture variables such as asphalt binder high temperature performance grade, mixture type, aggregate size, aggregate gradation, asphalt content, total asphalt binder recycling content, and testing parameters, including testing temperature and number of wheel passes. A rigorous validation process was used to assess the accuracy of the model to predict total rut depth and the HWTT rutting curve. A sensitivity analysis is presented, which evaluates the effect of the investigated variables on rutting depth predictions by the CNN model. The model can be used as a tool to estimate the rut depth in asphalt mixtures when laboratory testing is not feasible, or for cost saving, pre-design trials.
Purpose – To improve the performance of the krill herd (KH) algorithm, in this paper, a series of chaotic particle-swarm krill herd (CPKH) algorithms are proposed for solving optimization tasks within limited time requirements. The paper aims to discuss these issues. Design/methodology/approach – In CPKH, chaos sequence is introduced into the KH algorithm so as to further enhance its global search ability. Findings – This new method can accelerate the global convergence speed while preserving the strong robustness of the basic KH. Originality/value – Here, 32 different benchmarks and a gear train design problem are applied to tune the three main movements of the krill in CPKH method. It has been demonstrated that, in most cases, CPKH with an appropriate chaotic map performs superiorly to, or at least highly competitively with, the standard KH and other population-based optimization methods.
Bio-Inspired Metastructure Evolution In article number 2300019, Qianyun Zhang, Amir H. Alavi, and co-workers explore the principles of natural evolution for design and discovery of thousands of metastructures with hitherto unknown structures and new modalities of operation. The evolutionary computational approach uses representative unit cells with simple shapes to launch the evolutionary process and morphologically evolves complicated microstructures over generations. The framework is used to explore a series of 2D and 3D mechanical metamaterial structures with maximum bulk modulus, maximum shear modulus and minimum Poisson’s ratio.
To reach more efficient solar system, hybrid nanomaterial has been applied through the tube. The pipe was equipped with new shape of turbulator to augment the swirl flow. Special shape of turbulator makes it possible to act as fin because of connection with pipe. Hybrid nanoparticles contain MWCNT and Al2O3 and base fluid is H2O. To model the hybrid nanomaterial, empirical correlation were incorporated considering homogeneous mixture. Turbulent flow within the pipe was molded via K-ɛ technique. Insert of turbulator creates secondary flows which provide higher radial velocity and disruption of isotherms leads to higher convective flow. Checking the accuracy of modeling was done with comparing the obtained data with previous publication about turbulent flow within a tube equipped with turbulator. Augment of revolution can augment the velocity about 43.81% while temperature of plate decreases about 0.016%. Higher pumping power leads to rise of velocity about 99.05% while outlet temperature reduces about 0.096%. Nu and Darcy factor decline by 6.57% and 66.89% with augmenting b. Friction factor decreases with rise of Re by 12.43% while Nu goes up by 75.22%.
Induced heating-healing of asphalt concrete is rapidly emerging as an innovative repairing technique in pavement engineering. In this method, asphalt concrete is heated using the external electromagnetic field. As a result, the viscosity of the asphalt binder decreases, leading to the movement of the asphalt binder through the cracks and healing them. Researchers have been intensively investigating this technique as a potential practical method to heal the cracks and extend the service life of asphalt concrete and pavement. This review paper itemizes the applications of induced heating in pavement engineering. The induced healing, as the predominant application, was compared against the self-healing mechanism in asphalt concrete. Different types of heating sources and their effects on induced heating-healing of asphalt concrete were investigated. Different types of conductive additives (i.e., fiber, powder, and granular) are utilized to enhance the induced heating-healing efficiency. This study covers the influence of conductive additive type on the rate of induced heating-healing, thermal conductivity, heat transfer, temperature distributions, and mechanical characteristics of asphalt concrete. Besides, the effects of different parameters (i.e., mixture components, aggregate gradation and type, specimen dimension, breaking temperature, crack configuration, mechanical damage, aging, moisture/freeze-thaw damage) on the induced heating-healing process in asphalt concrete were introduced and discussed in detail. Afterward, the sustainability aspects of the induced heating-healing technique (i.e., cost, greenhouse gas emission, and energy consumption) were investigated and compared with existing asphalt concrete repairing methods. Finally, the existing challenges regarding the induced heating-healing technique and suggestions for future studies were presented.
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