The construction of suitable roads in rainy areas has created problems in the construction process due to the low resistance of asphalt to moisture. To solve this problem, materials are commonly used that make mixtures resistant to moisture; however, these materials may reduce the dynamic resistance of asphalt. Therefore, materials should be used that, in addition to increasing the dynamic resistance, also increase the moisture resistance of asphalt mixtures. One of these materials used in this research is steel wool fiber (SWF), which in addition to creating conductive roads also could have a significant effect on moisture resistance. In this study, the impact of 2%, 4%, 6%, 8%, and 10% SWF on the Marshall stability and moisture sensitivity of mixtures was investigated using the Marshall stability and indirect tensile strength (ITS) tests, respectively. Moreover, using SWF as a conductive fiber, the conductivity properties of asphalt mixtures were explored to find the optimal amount of electrical conductivity. The results of the Marshall stability test indicated that by increasing SWF contents, the stability of mixtures increased, compared with the base sample, and greater amounts of 6% SWF resulted in the reduction of the Marshall stability. The results of ITS showed that modification of bitumen by SWF increased ITS and tensile strength ratio (TSR) amounts of mixtures. 6% SWF was the optimal amount for enhancing the resistance of asphalt mixtures to moisture sensitivity. The results of the electrical resistivity test showed that the resistivity had three phases: high resistivity, transit, and low resistivity. Mixtures containing less than 4% SWF illustrated an insulating behavior, with electrical resistivity greater than 7.62  ×  108  Ω . m . At the transit phase, the resistivity of mixtures had a sharp reduction from 7.62  ×  108  Ω . m to 6.17  ×  104  Ω . m . Finally, 8% SWF was known as the optimal content for the electrical conductivity of mixtures.

Modeling the severity of accidents based on the most effective variables accounts for developing a high-precision model presenting the possibility of occurrence of each category of future accidents, and it could be utilized to prioritize the corrective measures for authorities. The purpose of this study is to identify the variables affecting the severity of the injury, fatal, and property damage only (PDO) accidents in Rasht city by collecting information on urban accidents from March 2019 to March 2020. In this regard, the multiple logistic regression and the pattern recognition type of artificial neural network (ANN) as a machine learning solution are used to recognize the most influential variables on the severity of accidents and the superior approach for accident prediction. Results show that the multiple logistic regression in the forward stepwise method has R2 of 0.854 and an accuracy prediction power of 89.17%. It turns out that the accidents occurred between 18 and 24 and KIA Pride vehicle has the highest effect on increasing the severity of accidents, respectively. The most important result of the logit model accentuates the role of environmental variables, including poor lighting conditions alongside unfavorable weather and the dominant role of unsafe and poor quality of vehicles on increasing the severity of accidents. In addition, the machine learning model performs significantly better and has higher prediction accuracy (98.9%) than the logit model. In addition, the ANN model’s greater power to predict and estimate future accidents is confirmed through performance and sensitivity analysis.

Due to population growth and the increasing number of vehicles on rural roads, traffic accidents have become one of the most important problems in the transportation system, which greatly affects the social and economic situation of the people. The main purpose of this study was to apply the analytical method to investigate the factors affecting the severity of traffic accidents on rural roads of Guilan, Iran, in order to determine the most effective factor in the occurrence of these accidents. At first, the frequency analysis was used to evaluate the variables and their frequency, then the Friedman test (FT) was applied to prioritize the factors, and the exploratory factor analysis (EFA) was used to determine the most effective factor in the occurrence of vehicle accidents in Guilan rural roads. Based on the FT, weather condition was the most important factor effective in these accidents. According to the results of the EFA, five factors were identified as the main factors involved in accidents in which the first factor contributing to accidents was the environmental factor, including weather condition and road surface condition. This indicates that concurrent result of the FT and the EFA, weather condition as an environmental factor, was identified as the most important factor affecting vehicle accidents on rural roads of Guilan. Finally, safety strategies were proposed to increase safety and reduce accidents along these roads.

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The main objective of this paper was to investigate the relationship between PCI and IRI values of the rural road network. To this end, 6000 pavement sections of the rural road network in Iran were selected. Road surface images and roughness linear profiles were collected using an automated car to calculate PCI and IRI, respectively. Three exponential regression models were developed and validated in three different IRI intervals. Analysis of the results indicated that exponential regression was the best model to relate IRI and PCI. In these models, R2 values were found to be acceptable, equal to 0.75, 0.76, and 0.59 for roads with good, fair, and very poor qualities, respectively, indicating a good relationship between IRI and PCI. Moreover, validation results showed that the model had a high accuracy. Also, the relation between IRI and PCI became weaker as a result of increasing the level of road surface roughness, which can be caused by the increase in the number and severity of failures. Furthermore, two failures of rail R.C. and rutting were rarely observed in the studied roads. Therefore, the proposed model is more applicable for roads without the mentioned failures and asphalt-pavement rural road network.