Emission factors are very important measures for developing an emission inventory, making decisions, designing control strategies, mitigating climate change, and even improving public health, in terms of respiratory system diseases. The emission factors could be either measured from field tests or estimated by an emission model. Existing models seldom consider the impacts of some special factors such as pavement roughness. As the impacts of the pavement roughness on emissions are very complicated, a linear model or physical model may not depict the mappings from affecting factors to resulted emission factors. In this paper, two non-linear models, including K-Nearest Neighbor (K-NN) and Neural Network (NN) were built to estimate vehicle emission factors using roughness involved input data. A best fitted model was identified to illustrate the emission pattern along a wide range of pavement roughness. Multiple field tests were conducted in five regions of the State of Texas, United States, with a total of 1,609 km test length. One dedicated test vehicle was employed throughout the test. Pavement roughness was tested using a smartphone based application. All tested data were separated into four groups, each representing a different range of roughness, while the modeling was conducted within each group. The predictive performance of each model was evaluated by (1) correlation coefficient; (2) relative errors; and (3) two tailed unequal variance t-test. Results suggest that, K-NN can be better than NN to model the emission factors for the Texas highway system, and driving on a smoother and rougher pavement result in higher vehicle emissions.
This paper mainly uses numerical simulation software to study the influence of different slope shapes on the seismic stability of slopes. With building three different numerical models, the safety factor is used to compare the stability of convex, concave and linear slopes under earthquake. The calculation results show that there are great differences in the seismic responses of slopes with different slope shapes.
Due to its special material composition, formation environment, and special structure, loess usually shows different engineering and dynamic characteristics from ordinary soil. Based on previous research results, this paper studies the Haiyuan loess. It is found that: (1) the variation coefficients of various physical properties (except for the liquid index) of loess in the region are relatively stable, but the mechanical properties are relatively discrete; (2) the correlation among natural water content, void ratio, and density is high, but the correlation between mechanical properties is poor; (3) the consolidation ratio, effective confining pressure, and dynamic shear modulus ratio change in direct proportion, while the damping ratio changes in the opposite direction with increasing shear strain; and (4) in this paper, the recommended model of shear wave velocity and burial depth of loess in the study area is given. Compared with the example, the error is smaller and more reliable. (5) The characteristic period of the seismic response spectrum of the loess soil layer is not sensitive to the change in the damping ratio, and the change amplitude is small.
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