Investigation of the Impact of Deicer Materials on Thermodynamic Parameters and Its Relationship with Moisture Susceptibility in Modified Asphalt Mixtures by Carbon Nanotube

Gilani, Vahid Najafi Moghaddam; Hosseinian, Seyed Mohsen; Safari, Daniel; Movahhed, Mojtaba Bagheri

doi:10.1007/s13369-020-05000-9

Cited by 15 publications

(6 citation statements)

References 41 publications

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“…By the use of ANN models, the patterns in the specification of problem and effective factors in an accident can be identified. ANNs have been occasionally applied in determining the effect of independent variables on the target variable [37][38][39]. e basis for focusing attention on ANN approaches is the high ability of these models in modeling and predicting the incidence as well as the severity of complex accidents plus describing the level and importance of factors that affect accident occurrence.…”

Section: Ann Approachmentioning

confidence: 99%

Application of Artificial Neural Network Approaches for Predicting Accident Severity on Rural Roads (Case Study: Tehran-Qom and Tehran-Saveh Rural Roads)

Habibzadeh

Ameri

Haghighi

et al. 2022

Mathematical Problems in Engineering

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Traffic accidents occur due to a combination of factors that lead to casualties and injuries. By identifying the most effective factors, it is possible for safety authorities to provide appropriate solutions for decreasing the accident severity and implementing preventive measures. The aim of this research was to present models to predict the accident severity on two-lane two-way (TLTW) rural highways in Iran over a one-year period from 2019 to 2020. Therefore, the occurrence probability of any type of accident was determined by artificial neural network (ANN)-based prediction models using nine independent variables affecting the accident severity. This study developed numerous ANN structures using back-propagation to model the potential nonlinear relationship between the accident severity and accident-related factors. Results indicated that among the models, the multilayer perceptron neural network (MLPNN) model with 6-2-2 partition had the best performance and prediction power. This model was developed using the standardized rescaling method for covariates and batch for training. Also, 9, 5, and 2 units were considered automatically for the input, hidden, and output layers, respectively, and the hyperbolic tangent and softmax were used as an activation function in the hidden and output layers, respectively. This model had the lowest cross-entropy error of 39.6 and the highest correct percentage of 82.5%, and the area under the receiver operating characteristic (ROC) curve was 0.852. Moreover, among all the effective variables, pavement condition index, roadside hazard, shoulder width, and passing zone ratio had the greatest impact on the accident severity. Finally, safety strategies were proposed to increase safety and reduce accidents along these roads.

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Section: Ann Approachmentioning

confidence: 99%

Application of Artificial Neural Network Approaches for Predicting Accident Severity on Rural Roads (Case Study: Tehran-Qom and Tehran-Saveh Rural Roads)

Habibzadeh

Ameri

Haghighi

et al. 2022

Mathematical Problems in Engineering

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“…Network learning intelligent models help them to identify patterns in identifying problems and influential parameters in accidents. e advantage of intelligent models over statistical methods is that in intelligent models, the weight of variables and the amount of effect can be calculated, and the resulting network can be optimized while in statistical methods, it is not possible to optimize the model and obtain the influence of parameters concurrently [82][83][84][85][86].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Statistical Analysis for Study of the Effect of Dark Clothing Color of Female Pedestrians on the Severity of Accident Using Machine Learning Methods

Hosseinian

Gilani

Mirbaha

et al. 2021

Mathematical Problems in Engineering

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The color and brightness of pedestrian clothing are among the factors that could increase the severity of their accidents due to the lack of visibility, especially at night. Today, as most Iranian females tend to wear hijab or dark clothing, the necessity of investigating female pedestrian accidents influenced by clothing color is important. Many studies have been performed to analyze the severity of pedestrian accidents, but a study has not yet been conducted to determine the effect of the dark clothing color of female pedestrians on the severity of accidents. Therefore, in this study, 12 independent variables affecting the severity of female pedestrian accidents such as clothing color, age, accident time, day, weather condition, education, pedestrian action, crossing facilities, crossing permit, job, road classification, and fault status were studied. Frequency analysis, Friedman test (FT), and Factor Analysis (FA) methods, as well as modeling methods of Multiple Logistic Regression (MLR) and Artificial Neural Networks (ANNs) using Multilayer Perceptron (MLP) and Radius Basis Function (RBF), were used. Results indicated that clothing color had a significant influence on pedestrian accidents, and chador and dark clothing color increased the probability of accidents, especially at night. The MLP model had a better prediction percentage than the rest, the prediction accuracy of which was 94.6%. Finally, safety solutions were presented according to the results to reduce pedestrian accidents and increase road safety.

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“…After they got saturated, they were stored 16 hours in a freezer at −18 degrees. ey were then held in a waterbath for 24 hours at 60 degrees, returned to a place with 25 degrees, and held for 24 hours at the same degree [47]. e load was recorded for performing ITS test until rupture, and the amount of ITS (kPa) was then measured [48]:…”

Section: Its Testmentioning

confidence: 99%

Investigation of the Effect of Crumb Rubber Powder and Warm Additives on Moisture Resistance of SMA Mixtures

Ziari

Divandari

Akbar

et al. 2021

Advances in Civil Engineering

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Stone matrix asphalt (SMA) mixtures are one of the most resistant types of asphalt mixtures known to date that the bulk load is borne by the aggregate skeleton. In this research, the effect of crumb rubber powder (CRP), Vestenamer polymer, and warm additives, including Sasobit, ZycoTherm, and a porous and warm mix asphalt (PAWMA), was investigated on the moisture resistance of SMA mixtures. A segregation test was performed to determine the tendency of the polymer to separate from modified polymer bitumen under storage conditions. Also, a drain down sensitivity test was conducted to explore the bleeding phenomena and drain down of SMA mixtures. Finally, a boiling water test, as well as an indirect tensile strength (ITS) test, was applied to examine the sensitivity of mixtures to moisture. The segregation test results indicated that by the use of Vestenamer polymer, a conventional (low speed) mixer can be used instead of using a high-speed mixer, and this will be very important in the cost of rubber bitumen production. The drain down test results showed that cellulose fibers prevented the bitumen bleeding of SMA mixtures. Also, the results of the boiling water test indicated that CRP samples containing ZycoTherm had the highest moisture resistance compared to the other warm additives. Among various mixtures, 8% CRP-modified samples containing ZycoTherm resulted in the lowest amount of stripping in SMA mixtures, even more than the base sample. The ITS test results illustrated that the use of CRP, Vestenamer polymer, and warm additives had a significant effect on the ITS amounts of mixtures. Moreover, the samples containing ZycoTherm had the highest tensile strength ratio (TSR) compared to the other SMA samples.

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Investigation of the Impact of Deicer Materials on Thermodynamic Parameters and Its Relationship with Moisture Susceptibility in Modified Asphalt Mixtures by Carbon Nanotube

Cited by 15 publications

References 41 publications

Application of Artificial Neural Network Approaches for Predicting Accident Severity on Rural Roads (Case Study: Tehran-Qom and Tehran-Saveh Rural Roads)

Application of Artificial Neural Network Approaches for Predicting Accident Severity on Rural Roads (Case Study: Tehran-Qom and Tehran-Saveh Rural Roads)

Statistical Analysis for Study of the Effect of Dark Clothing Color of Female Pedestrians on the Severity of Accident Using Machine Learning Methods

Investigation of the Effect of Crumb Rubber Powder and Warm Additives on Moisture Resistance of SMA Mixtures

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