There are several environmental factors such as temperature differential, moisture, oxidation, etc. that affect the extended life of the modified asphalt influencing its desired adhesive properties. Knowledge of the properties of asphalt adhesives can help to provide a more resilient and durable asphalt surface. In this study, a hybrid of Bayesian optimization algorithm and support vector regression approach is recommended to predict the adhesion force of asphalt. The effects of three important variables viz., conditions (fresh, wet and aged), binder types (base, 4% SB, 5% SB, 4% SBS and 5% SBS), and Carbon Nano Tube doses (0.5%, 1.0% and 1.5%) on adhesive force are taken into consideration. Real-life experimental data (405 specimens) are considered for model development. Using atomic force microscopy, the adhesive strength of nanoscales of test specimens is determined according to functional groups on the asphalt. It is found that the model predictions overlap with the experimental data with a high R2 of 90.5% and relative deviation are scattered around zero line. Besides, the mean, median and standard deviations of experimental and the predicted values are very close. In addition, the mean absolute Error, root mean square error and fractional bias values were found to be low, indicating the high performance of the developed model.
The conventional method for determining the Marshall Stability (MS) and Marshall Flow (MF) of asphalt pavements entails laborious, time-consuming, and expensive laboratory procedures. In order to develop new and advanced prediction models for MS and MF of asphalt pavements the current study applied three soft computing techniques: Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multi Expression Programming (MEP). A comprehensive database of 343 data points was established for both MS and MF. The nine most significant and straightforwardly determinable geotechnical factors were chosen as the predictor variables. The root squared error (RSE), Nash–Sutcliffe efficiency (NSE), mean absolute error (MAE), root mean square error (RMSE), relative root mean square error (RRMSE), coefficient of determination (R2), and correlation coefficient (R), were all used to evaluate the performance of models. The sensitivity analysis (SA) revealed the rising order of input significance of MS and MF. The results of parametric analysis (PA) were also found to be consistent with previous research findings. The findings of the comparison showed that ANN, ANFIS, and MEP are all reliable and effective methods for the estimation of MS and MF. The mathematical expressions derived from MEP represent the novelty of MEP and are relatively reliable and simple. Roverall values for MS and MF were in the order of MEP > ANFIS > ANN with all values over the permissible range of 0.80 for both MS and MF. Therefore, all the techniques showed higher performance, possessed high prediction and generalization capabilities, and assessed the relative significance of input parameters in the prediction of MS and MF. In terms of training, testing, and validation data sets and their closeness to the ideal fit, i.e., the slope of 1:1, MEP models outperformed the other two models. The findings of this study will contribute to the choice of an appropriate artificial intelligence strategy to quickly and precisely estimate the Marshall Parameters. Hence, the findings of this research study would assist in safer, faster, and more sustainable predictions of MS and MF, from the standpoint of time and resources required to perform the Marshall tests.
The present study explores the structural pavement design techniques related to pavement distresses in terms of pavement rutting, cracking and International Roughness Index (IRI) based on the materials properties, roadbed characteristics, climate and traffic loads for highway network of Saudi Arabia (KSA). The study was focused on selected site conditions at four regions in KSA: Central (Riyadh); Eastern (Al-Ahsa); Western (Jeddah) and Northern (Arar). Mechanistic-Empirical Pavement Design Guide (MEPDG) software was used to calibrate and predict pavement design life according to the mentioned distresses for different regions in the KSA. This is the first time where the exact weather stations were selected to run analysis on the software determining realistic pavement distresses. In the study, the pavement structure design is different for low traffic (700 AADTT) and high traffic (2000, 6000, and 10,000 AADTT). The tests were run on the MEPDG software to analyze the distresses predicted by the software for an interval of 5, 10, 15, and 20 years. The results predicted by the software show that the preliminary example design satisfies all the target distresses for the mentioned design life, even for 20 years. The study provides a base pavement design for pavement designers that can be modified as per project requirements using the specific data for traffic, material properties, thickness, and distress limit to achieve target design life.
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