Ecotoxicological risk of asphalt pavements to aquatic animals associated with pollutant leaching.

Contaminants such as heavy metals and polycyclic aromatic hydrocarbons (PAHs) can be released from asphalt pavement and transported through stormwater runoff to nearby water bodies, leading to water pollution and potential harm to living aquatic animals. This study characterizes the heavy metal and PAH leaching from various asphalt paving materials and their potential ecotoxicological effects on zebrafish Danio rerio. Artificial runoffs were prepared in the laboratory concerning the effects of water, temperature, and traffic. The concentrations of heavy metals and PAHs in the leachates were quantified, while the toxicity assessment encompassed mortality, metal stress, PAH toxicity, inflammation, carcinogenicity, and oxidative damage. Gene expressions of related proteins or transcription factors were assessed, including metallothionines, aryl hydrocarbon receptors, interleukin-1ß, interleukin-10, nuclear factor-κB, tumor necrosis factor-α, tumor suppressor p53, heat shock protein 70, and reactive oxygen species (ROS). The findings demonstrate that leachates from asphalt pavements containing waste bottom ash, crumb rubber, or specific chemicals could induce notable stress and inflammation responses in zebrafish. In addition, potential carcinogenic effects and the elevation of ROS were identified within certain treatment groups. This study represents the first attempt to assess the ecotoxicity of pavement leachates employing a live fish model, thereby improving the current understanding of the environmental impact of asphalt pavements.

Kinetics and toxicological potential of heavy metal leaching from asphalt pavements.

The leaching of heavy metals from asphalt pavement has attracted increasing attention due to its associated environmental risks. Comprehending the leaching process is crucial for ensuring the safe utilization of asphalt pavement. This study investigates heavy metal leaching kinetics from asphalt pavements using tank-leaching tests and dynamic simulations employing both first and second-order kinetic models. Furthermore, this study reveals the toxicological potential of heavy metal leaching from asphalt pavement by assessing its temporal metal accessibility based on the obtained kinetic attributes. Six distinct asphalt mixtures were prepared and tested, each exhibiting two different gradations. The findings demonstrated that both kinetic models effectively elucidated the leaching process. Notably, the relatively stable final leaching stages primarily adhered to first-order kinetics, while the second-order kinetics provided a superior description of the more intricate initial leaching stages. In terms of toxicological potential, the results indicated that recycled waste-incorporated asphalt pavements, specifically bottom ash-incorporated asphalt and asphalt rubber, exhibited excessive heavy metal leaching for varying durations, ranging from several days to months under specific conditions. This study has provided valuable insights into the metal leaching kinetics of asphalt pavements and their associated toxicological impact, significantly advancing the current understanding of the consequences of heavy metal leaching from asphalt pavements.

Adaptive VMD-K-SVD-Based Rolling Bearing Fault Signal Enhancement Study.

To address the challenges associated with nonlinearity, non-stationarity, susceptibility to redundant noise interference, and the difficulty in extracting fault feature signals from rolling bearing signals, this study introduces a novel combined approach. The proposed method utilizes the variational mode decomposition (VMD) and K-singular value decomposition (K-SVD) algorithms to effectively denoise and enhance the collected rolling bearing signals. Initially, the VMD method is employed to separate the overall noise into intrinsic mode functions (IMFs), reducing the noise content within each IMF. To optimize the mode component, K, and the penalty factor, α, in VMD, an improved arithmetic optimization algorithm (IAOA) is employed. This ensures the selection of optimal parameters and the decomposition of the signal into a set of IMFs, forming the original dictionary. Subsequently, the signals are decomposed into multiple IMFs using VMD, and an original dictionary is constructed based on these IMFs. K-SVD is then applied to the original dictionary to further reduce the noise in each IMF, resulting in a denoised and enhanced signal. To validate the efficacy of the proposed method, rolling bearing signals collected from Case Western Reserve University (CWRU) and thrust bearing test rigs were utilized. The experimental results demonstrate the feasibility and effectiveness of the proposed approach in denoising and enhancing the rolling bearing signals.

Parameter Determination of the 2S2P1D Model and Havriliak-Negami Model Based on the Genetic Algorithm and Levenberg-Marquardt Optimization Algorithm.

This study utilizes the genetic algorithm (GA) and Levenberg-Marquardt (L-M) algorithm to optimize the parameter acquisition process for two commonly used viscoelastic models: 2S2P1D and Havriliak-Negami (H-N). The effects of the various combinations of the optimization algorithms on the accuracy of the parameter acquisition in these two constitutive equations are investigated. Furthermore, the applicability of the GA among different viscoelastic constitutive models is analyzed and summarized. The results indicate that the GA can ensure a correlation coefficient of 0.99 between the fitting result and the experimental data of the 2S2P1D model parameters, and it is further proved that the fitting accuracy can be achieved through the secondary optimization via the L-M algorithm. Since the H-N model involves fractional power functions, high-precision fitting by directly fitting the parameters to experimental data is challenging. This study proposes an improved semi-analytical method that first fits the Cole-Cole curve of the H-N model, followed by optimizing the parameters of the H-N model using the GA. The correlation coefficient of the fitting result can be improved to over 0.98. This study also reveals a close relationship between the optimization of the H-N model and the discreteness and overlap of experimental data, which may be attributed to the inclusion of fractional power functions in the H-N model.