مجله مهندسی زیر ساخت های حمل و نقل
سال سوم شماره 3 (پیاپی 11، پاییز 1396)

Evaluation of pavement performance plays a major role in pavement management systems for determination of optimum strategy in repair and maintenance of the road. One of the most prominent assets in evaluation of the pavement at network and project level is identification and survey of surface pavement distresses. In the past two decades, extensive studies have been carried out in order to develop automatic methods for pavement evaluation. Most of these methods are based on machine vision and image processing techniques. One of the most important components of machine vision system is feature extraction. In the present study, after acquisition of six different group of asphalt pavement distresses under controlled condition, in order to compare different image texture processing methods for automatic recognition and categorising of distresses, first order statistical indices based on image intensity histogram, second order statistical indices based on grey level co-occurrence matrix and third and higher order statistics based on grey level run-length matrix have been used. Based on the results of the classification of distress images acquired by Mahalanobis minimum distance method, it can be concluded that although statistics extracted from histogram and grey level co-occurrence matrix have superior sensitivity in detection of alligator cracks, but third and higher order statistical indices based on grey level run-length matrix provide better results, with 80% classification accuracy, compared to other texture analysis approaches applied in the present research.

One of the appropriate methods to improve the roadway, built on the weak subgrade, is using geosynthetic reinforcement that is highly regarded. Few field and laboratory tests have been planned and conducted under the real traffic loading to investigate the behavior of reinforced subgrade and achieve suitable design methodology. However, due to such limitations as high costs of laboratory or field tests, their implementation is limited. Numerical simulation methods can help to study the performance of such reinforced roads and evaluate the effect of important parameters. In this research, three-dimensional finite element method (FEM) was used to investigate the behavior of the geosynthetic reinforced roads under traffic loads, and the results were validated with field data. Results of the numerical modeling and field studies showed that the numerical model can predict the behavior of a geogrid-reinforced system and there was a good agreement between the results of field studies and modeling responses. Results of the numerical study also showed that the settlement of reinforced road is strongly influenced by the mechanical properties of reinforcement, including the stifness of the geogrid. Reduction of geogrid stifness by 50%, increased the surface settlement by about 61%. Also, effect of the stiffness of subgrade and base layer in reinforced test section on the surface settlement was 50% less than the unreinforced test section. Results showed TBR increase by 26.11 times due to the reinforcement of the test section with a strong reinforcing agent.

National road network constitutes a major part of each country's wealth and huge investments are spent in this sector annually on pavement maintenance, repair and rehabilitation. One of the most widespread damages in flexible pavements is rutting, which is due to weakness in shear resistence of the asphalt mixture. The best approach to prevent such malicious phenomenon is to use mixtures in which the amount of effective parameters in shear resistence of the asphalt is controlled. Cohesion (C) and internal friction angle (f) of the asphalt are the final determinants of pavement shear resistence. The best way to determine C and f in asphalt is to perform triaxial test on asphalt samples. But, for some reasons, such as high costs to perform this test, as well as the lack of triaxial­­ test equipment in the country, performing this test is virtually impossible. On the other hand, Marshal equipment is readily available in the most remote workshops and it is being used extensively in Iran. Therefore, in this research, it was tried to find some relationships between C and f in asphalt with Marshall parameters. The obtained relationships showed that there is proper relation between asphalt shear stress parameters and Marshall stability and flow. By using these relationships, concurrent with presentation of the mixing design in the laboratory and before mass production of the asphalt, the limits of C and f are determined, too. Therefore, mass production of the asphalt with high rutting potential could be prevented.

Nowadays, roller compacted concrete (RCC) is used in building dams and roads, and in recent years, using RCC is extended because of its advantages such as short construction time, availability of required materials, appropriate performance in cold regions and high life span. Compressive strength of RCC is the most important mechanical property that its enhancement can improve RCC performance. The sensitivity of RCC to its ingredients has caused some problems in prediction of the compressive strength. Parameters such as cement content, water-cement ratio, the amount of replaced cementitious materials and coarse to fine aggregates ratio affect the compressive strength of RCC. In recent decades, modeling by artificial intelligence has found a special place in technical sciences and engineering, and prediction of the behavior of complex cases has become possible with the help of this method. In this study, constructed design mixes by the authors and design mixes made in other studies were collected. By considering concrete constituents and samples age as input variables, several artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) models were prepared to predict the compressive strength of RCC. Comparison of the results indicated that ANN model has more ability in predicting the compressive strength of RCC than ANFIS and SVM models. Also, the predicted compressive strength by ANN and SVM models had the highest and lowest match with actual compressive strength, respectively. The correlation coefficient, root mean square error and mean absolute error of ANN model were 0.9717, 2.4859 and 2.1396, respectively. These values were 0.9566, 3.4013 and 3.0733 for SVM model, respectively.

Bitumen and mastic (mixture of bitumen and filler) are one of the components of asphalt mixtures, which have great influence on their behavior and performance. In recent years, many studies have been carried out on the evaluation of rheological properties of bitumen. In the present study, rheological properties of bitumen and mastic modified with warm mix asphalt additive (Sasobit) have been evaluated. The neat bitumen had a performance grade of PG64-16 that was modified with 1.5% by weight of bitumen using Sasobit additive. To prepare the mastic, siliceous riverrine filler was added with equal ratio to the bitumen. The superpave experiments for bitumen using dynamic shear rheometer and bending beam rheometer were carried out on base and modified bitumen and mastic. The fatigue and rutting parameters of the materials were determined and compared and master curve of the materials was created for general presentation of the rheological behavior of bitumen and mastic. Results indicated that Sasobit significantly increased the bitumen and mastic dynamic modulus (up to 60 and 100 %, respectively) at high temperatures, and reduced their phase angle by 5%. Sasobit has doubled the rutting parameter of bitumen and mastic. The fatigue parameter in pure bitumen did not change significantly with addition of the Sasobit, while Sasobit increased the fatigue parameter in mastic (20-30 %) and reduced fatigue resistance. The master curve model was well-fitted on experimental bitumen and mastic data modified with Sasobit, which allows comprehensive examination of the material behavior over a wide range of temperature and frequency.

One of the important issues in infrastructure's destruction by earthquake is the impact of soil on the bedrock, which is known as “site effect”. Considering the fact that city of Kerman is located in the tectonic seismic state of eastern Iran and is situated near the Golbaf, Koohbanan, Sirch and Nayband faults, it is essential to reduce the dangers of the earthquake. Moreover, one of the most important structures which will be damaged during an earthquake is urban bridges. Thus, in designing the bridges, the impact of earthquake and vulnerability of bridges in places in which earthquake is likely to occur must be taken into consideration. In this study, recorded microtremor data in 52 three-component seismometric stations have been used. To have data with minimum environmental noise, all the measurements were performed in time period of midnight to 5 am in the morning. In this paper, the microtremor data related to the developing areas in Kerman and the erection site of four important bridges which are under construction has been investigated. Considering the objective of the study, which is estimation of the natural frequency and the earth vulnerability index, the recorded microtremors in the seismometric stations were processed using spectral ratio of H/V horizontal to vertical components. Results showed that frequency values vary from 0.3 to 1.2 Hz. Based on the achieved frequencies and comparison to the standard tables, it has been concluded that all the stations are of the soft-soil type in site class I. Finally, the frequency zoning map and vulnerability indices were drawn. Results showed that vulnerability index is high in the developing areas of the city of Kerman .

Nowadays, using in-situ cold recycling has grown substantially as an economical method to rehabilitate the eroded roads. Among these cold recycling methods, the use of foamed bitumen technology has been of much interest to engineers due to its ease and rapidity of implementation. In foamed-bitumen mixtures, in order to reduce the moisture sensitivity and increase the resistance properties, active fillers such as cement and lime are used. But, there is no proper and standard mixture design for these types of mixtures and performing mechanical trial and error experiments is time consuming and expensive. Thus, it seems necessary to find a simple method that has enough accuracy and could estimate the mechanical behavior of recycled foamed-bitumen samples. In this research, mixtures were made by adding foamed bitumen binder (1, 2 and 3 percent) to the recycled material and Portland cement (0, 1, 1.5 and 2 percent), and mechanical characterics of the samples was measured by Marshall stability tests, resilient modulus and indirect tensile strength (soaked and unsoaked). Then, with statistical analysis, a new mathematical formula for each test was presented with appropriate accuracy. The presented models can predict mechanical characteristics of the recycled foamed bitumen mixture on the basis of bitumen percentage and cement with enough accuracy and without spending time and money. Also, it was found that by increasing the bitumen content, the behavior of these mixtures in the performed tests was not similar. Such that increasing bitumen from 1% to 2%, Marshall resistance and resilient modulus increased and thenafter they decreased by increasing the bitumen percent. But, by increasing the bitumen content, indirect tensile strength (soaked and unsoaked) increases too. On the other side, addition of cement increases resistance and decreases moisture sensitivity of these mixtures.