محمد ارباب پور بیدگلی

Based on the important effects of filler on performance-related properties of asphalt mixtures, this study investigates the influence of filler type on moisture susceptibility of asphalt mixtures under multiple freeze-thaw cycles. Furthermore, the effect of changing the type of mineral fillers, especially replacing fillers with recycled concrete materials on moisture resistance of asphalt mixture, with sustainable development approach, is evaluated. In this study, first, the indirect tensile strength and the resilient modulus tests are conducted following 1, 3, 6, and 10 cycles of freeze-thaw and then the fracture energy is calculated through the results of indirect tensile test. Next, the surface free energy components of mastics with different types of filler are calculated using the static contact angle measurement method. The results of performance-based moisture susceptibility tests show that replacing natural filler with Portland cement can result in the best performance compared to control mixture, limestone filler, and RCA filler. However, the evolution of mechanical properties of control filler, limestone filler, and RCA filler depends on the number of freeze/thaw cycles. At higher conditioning cycles, TSR values demonstrate a different behavior such that the TSR values of the asphalt mixture containing RCA filler increase up to 15% on average compared to the asphalt mixture containing limestone filler after the first cycle. Although the results of mechanical properties in initial freeze-thaw cycles are similar to surface free energy results. However, these results cannot predict the behavior of asphalt mixtures at higher freeze-thaw cycles.

The implementation of an efficient pavement management system is dependent on the acquiring desired information from the pavement condition. The automatic data collection were welcomed by mechanized systems due to adequate precision and speed. But the various systems were developed that had high construction and operation costs. Therefore, the development of cost-effective devices that is independent from the vehicle vibrations and equipped with instrumentation is necessary. In this study, the Health Monitoring System (HMS) is developed as a system of longitudinal profile measurement and road roughness evaluation based on response-type vehicle with applying the mode combined of accelerometers, distance meter, GSM. Upon harvesting the vibration response of the HMS and the vehicle, data preparation and signal filtration are performed through the Digital Signal Processing (DSP) techniques. So the dynamic equations governing the behavior of the HMS are derived based on one degree of freedom. The dynamic parameters of system are extracted based on optimization approach using GA and PSO algorithms. Afterwards, the extracted longitudinal profile based on the vibration responses is become to the longitudinal profile with the specific speed by the algorithm of the relationship between speed and frequency of harvested data. Then, the International Roughness Index (IRI) is calculated from the longitudinal profile of the studied pathway by ProVal software. Finally, the accuracy of the HMS results is validated by manual method with measuring the AEP and the NRMSD between the outputs of the system and the Road Surface Profiler (RSP) with values of 19.72% and 9.68% respectively.

Travel is one of the most important aspects of human life and is one of the most important periodic (or non-periodic) activities. For this reason, over the years, various ways have been devised to meet this need of communities. Similar many industrial systems, the transportation system are affected by supply and demand relations, and any action in this area should be planned according to existing relationships and future trends of supply and demand. In this research, using 3 Models: simple linear regression, multivariate regression and multi-layered perceptron neural network models for forecasting traffic demand of Qom-Tehran (freeway) axis was studied. The data used in this research include the Iranian Statistical Center, statistical manuals and Qom traffic information. Independent variable in multivariate regression and neural network including population, working population, income level and simple linear regression model of population. The results of this study show that Pearson correlation between variables considered in neural network methods, multivariate regression, linear regression was 0.995, 0. 933, and 0,723 respectively, and the success rate of each of these models in estimated dependent variable (travel demand) were 0.99, 0.885, and 0.541, respectively. Comparison of methods has shown that the neural network method has the highest correlation and accuracy and simple linear regression method has the least correlation and precision in demand estimation.