In recent decades, because of the massive destruction of old structures, a large amount of construction and demolition (C&D) waste has been produced. These waste materials have the greatest volume and weight among solid waste with many environmental problems. Reusing them in concrete as substitute to virgin aggregates is considered an efficient practice unless significant mechanical properties and workability degradation occurs. In this study, the effect of different concentrations of C&D waste (0%, 10%, 20%, 30%, and 50%) as coarse aggregates on workability, compressive, tensile, and flexural strengths was investigated at the water-to-cement (W/C) ratios of 0.40, 0.45 and 0.50. The strengths were measured at the ages of 7 and 28 days. The results proved that C&D waste has no significant effect on compressive strength, while its negative impact on workability was palpable. With respect to tensile and flexural strength, just 50% of C&D waste led to significant reduction.
Considerable effort has been made to determine which of the most common prediction modeling techniques performs best, based on crash‐related data. Accordingly, the present study aims to evaluate how crashes in the urban road network are affected by contributing factors. Therefore, in the present paper, a comparison has been done among four artificial neural network (ANN) techniques: extreme learning machine (ELM), probabilistic neural network (PNN), radial basis function (RBF), and multilayer perceptron (MLP). According to the measures used, including Nash–Sutcliffe (NS), mean absolute error (MAE), and root mean square error (RMSE), ELM was found to be the most successful approach in addressing the objectives defined in the present study. Moreover, not only is ELM the fastest algorithm due to its different structure, but it has also led to the most accurate prediction. In the end, the RReliefF algorithm was utilized to find the importance of variables used, including V/C, speed, vehicle kilometer traveled (VKT), roadway width, existence of median, and allowable/not‐allowable parking. It was proved that VKT is the most influential variable in accident occurrence, followed by two traffic flow characteristics: V/C and speed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.