Modelling the Effects of Hydrated Lime Additives on Asphalt Mixtures by Fuzzy Logic and ANN

Service quality is one of the main issues that today's world. Firms operating in the transportation sector are also trying to improve the quality of the service they provide to their passengers. It is crucial to determine the passengers' service quality perceptions and priorities to evaluate and improve the service in this context. In this study, Kocaeli's tram service users' service quality perceptions have been evaluated by applying a survey consisting of 20 questions and user satisfaction levels from different service dimensions. Later, an artificial neural network model was developed using the users' demographic data and their responses to the survey questions to mimic their service quality satisfaction. The artificial neural network model developed has been examined to understand the importance that tram users give to service quality. Using the developed the "change of score" method, how the changes to be made in the tram system will affect the quality of service and how the opinions of different user groups will be affected can be examined in detail. The artificial neural network model's prediction capability was compared with the multiple linear regression model and found superior. According to the developed Change of Score Method, the most frequent user attaches the highest importance to the service dimensions of the convenience to pay for the tram, getting his/her destination on time, and reducing environmental pollution.

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Marshall Stability Prediction with Glass and Carbon Fiber Modified Asphalt Mix Using Machine Learning Techniques

Upadhya

Thakur

Ansari

et al. 2022

Materials

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Pavement design is a long-term structural analysis that is required to distribute traffic loads throughout all road levels. To construct roads for rising traffic volumes while preserving natural resources and materials, a better knowledge of road paving materials is required. The current study focused on the prediction of Marshall stability of asphalt mixes constituted of glass, carbon, and glass-carbon combination fibers to exploit the best potential of the hybrid asphalt mix by applying five machine learning models, i.e., artificial neural networks, Gaussian processes, M5P, random tree, and multiple linear regression model and further determined the optimum model suitable for prediction of the Marshall stability in hybrid asphalt mixes. It was equally important to determine the suitability of each mix for flexible pavements. Five types of asphalt mixes, i.e., glass fiber asphalt mix, carbon fiber asphalt mix, and three modified asphalt mixes of glass-carbon fiber combination in the proportions of 75:25, 50:50, and 25:75 were utilized in the investigation. To measure the efficiency of the applied models, five statistical indices, i.e., coefficient of correlation, mean absolute error, root mean square error, relative absolute error, and root relative squared error were used in machine learning models. The results indicated that the artificial neural network outperformed other models in predicting the Marshall stability of modified asphalt mix with a higher value of the coefficient of correlation (0.8392), R2 (0.7042), a lower mean absolute error value (1.4996), and root mean square error value (1.8315) in the testing stage with small error band and provided the best optimal fit. Results of the feature importance analysis showed that the first five input variables, i.e., carbon fiber diameter, bitumen content, hybrid asphalt mix of glass-carbon fiber at 75:25 percent, carbon fiber content, and hybrid asphalt mix of glass-carbon fiber at 50:50 percent, are highly sensitive parameters which influence the Marshall strength of the modified asphalt mixes to a greater extent.

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Modelling the Effects of Hydrated Lime Additives on Asphalt Mixtures by Fuzzy Logic and ANN

Cited by 3 publications

References 31 publications

Determination of optimum limestone filler content in asphalt mastic by dielectric spectroscopy

Determination of optimum limestone filler content in asphalt mastic by dielectric spectroscopy

Investigating the Service Quality of Kocaeli Tram Service Using Artificial Neural Networks

Marshall Stability Prediction with Glass and Carbon Fiber Modified Asphalt Mix Using Machine Learning Techniques

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