fereydoon moghaddasnezhad

The coating is one of the most important components that affect the efficiency of the pavements. Parameters are effective in the proper implementation of the coating such as the type of coating, the application time, the rate of application, temperature, uniformity of application, etc. The uniformity of application requires field control in the project implementation, is currently used to control the spreading weight of the tray. This test has many errors due to the lack of continuity. The issue of uniform distribution of coating has become less attention. In this study, the automatic system is presented based on image processing using a camera, GPS, microcontroller board, and ..., which can analyze the uniform distribution and provide a good, moderate and poor classification for coating distribution evaluation. Image quality has improved with image processing and compression and noise reduction have been done. The thresholding was used to separate the coating from the background. After the thresholding, various properties such as the area of the coating, coefficient of variation, local maximum, and minimum, etc. are obtained from the images and used to evaluate the coating distribution. Used categorization algorithms to select effective features in categorizing images. A comparison of the results of the classification of images by a confusion matrix. Finally, the results showed that the presented system has a precision of 86%. Also, using the effective parameters in the model, the uniform distribution index was presented. This index has a value between0 and 100, which indicates the best and worst distributions.

Rail transportation system plays an important role in the development of the economies of the countries. This system will be worn over time by operation and weather conditions and will require maintenance. One of the goals of this operation is to keep the tracks in an acceptable condition and prevent their excessive deviation from the optimal situation. Railways maintenance and repair management system has been studied and implemented to optimize activities and reduce related costs. Such systems have used various techniques to predict the future state of failure. Choosing the best maintenance policy is the goal of these systems. For policy making, the best and most cost-effective option, life-cycle cost analysis is required. In the following, with the help of the Markov prediction model, the life cycle cost (LCC) model is suggested for rail and ballast. In the end, it was found that the main costs in the ballast part are renewal costs and the track unavailability costs. The effect of renewal tonnage on these two costs is far higher than other costs. As you can see, the lowest ballast life cycle cost in the range of 100 to 150 million gross tons. In this study, assuming annual tonnage (16 million gross tonnages) as previously mentioned, it results a renewal life of about 6 to 10 years. This value for the rails is from 500 to 540 million gross tons, which is equivalent to 30 to 35 years.

Pavement inspection is one of the most important steps in implementation of pavement management system and extend efforts have been done to increase the efficiency of this system by using the new technologies. In recent years, transportation agencies focus on creating automatic and more efficient systems for pavement inspection and a large number of researches have been done for this aim. According to progress of computer science, data mining and machine learning as computer-based methods are used more in various areas (such as engineering, medical and economy) and significant results have been achieved. In pavement management area, several researches have been performed with aim of the applying the machine learning especially in pavement distresses evaluation. In this paper, the theoretical concepts have been explained and several models have been created based on deep convolutional networks using transfer learning to detect and classify pavement cracks as the most prevalent pavement distress, and the performance of these models has been evaluated considering learning and test speed, and accuracy as the most important performance parameters. The results of this research indicate that the speed of models almost depends on the pre-trained models characteristics that applied in transfer learning process. Also the accuracy of models based on various metrics (Sensitivity, F-score, etc.) is in range of 0.94 to 0.99 and indicates that deep learning method can be used to create expert systems for detection, classification, and quantification of pavement distresses such as cracking.

Improvement of the pavement surface texture charactristics and drainage quality is an important issue in the field of increasing roads safety and reducing the rate of accidents, especially in rainy weather conditions. The assessment of the pavement surface features and its relation with the accident rate is a common research topic, but no extensive research has been carried out on the evaluation of the pavement surface drainage. In this research, a system has been developed to assess the surface drainage of the pavements. For this purpose, a hardware is designed which is able to saturate the surface and capture the drainage process under constant conditions without the effects of environmental factors. The basis of the presented system is based on digital image processing techniques. Using image processing methods, three time-related indexes including Entropy,Energy and pixels proportion have been determined for the assessment of surface drainage quality of the pavement. Providing a proper combination of the indexes, the pavements are classified as appropriate, acceptable and inappropriate in terms of surface drainage performance using C5.0 datamining algorithm. The validation of the results of the proposed system shows that this system can evaluate the surface drainage situation with 95.7% accuracy. The presented system results can be used in the pavement management systems at project and network levels as a suitable measure for the evaluation of pavement safety in the rainy conditions as well as improving roads safety.

Considering the geotechnical problems caused by low strength clayey soils is important in construction projects. Chemical stabilization with additives such as cement is a common method to improve the engineering properties of clay soils. In spite of the acceptable effects of cement on strength of soils, the cost of this additive and its destructive effects on the environment should be of concern. This has led the researchers to use by-products and waste materials. Cement Kiln Dust (CKD) is a powdery by-product of the Portland cement manufacturing process. In this paper, the geotechnical parameters of cement and cement kiln dust stabilized kaolinite clay are compared. For this purpose, Atterberg limits, standard proctor, unconfined compressive strength, and California bearing ratio tests were conducted on specimens containing 5, 10 and 15% cement and CKD (by dry weight of the soil). The results show that the cement and cement kiln dust increase the soil strength. It was seen that the unconfined compressive strength of the specimen with 15% CKD is equal to the specimen with 10% cement after 28 days of curing. It is evident from the scanning electron microscopy analysis of specimens containing cement and CKD that calcium silicate and aluminate hydration products reduce the volume of the void spaces and join the soil particles, leading the strength to increase.

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.

Moisture damage in asphalt mixtures is defined as loss in strength and durability in present of water. Lack of relationship between distress mechanism in the laboratory and field, lack of measurement of effective properties of materials and their role, lack of modification solution, and other deficiencies of these methods have motivated researchers to present more appropriate experimental methods based on the properties of materials and asphalt mixtures, which could have a determining role in the occurrence of moisture-induced distress. Thus, the present research tries to present a model for predicting moisture sensitivity of asphalt mixtures based on thermodynamic parameters and characteristics of mix design of asphalt mixtures, which could predict the performance of an asphalt mixture against moisture based on the mentioned parameters.Results of this study demonstrated that using antistripping additives can generally improve the performance of asphalt mixtures against moisture; but, type and percent of these additives must be determined considering type of aggregate, type of asphalt binder, and properties of asphalt binder-aggregate mixture. Moreover, based on the presented statistical analyses, the results represented that the parameters of cohesion free energy and adhesion free energy of aggregate-asphalt binder in dry conditions, wettability of aggregate by asphalt binder, specific surface area of aggregates, and apparent film thickness of asphalt binder on the aggregate surface as well as free energy of system during antistripping event and permeability of asphalt mixture considerably influenced the occurrence of moisture damage directly and reversely, respectively.