RESUMO
A tunnel health monitoring (THM) system ensures safe operations and effective maintenance. However, how to effectively process and denoise several data collected by THM remains to be addressed, as well as safety early warning problems. Thus, an integrated method for Savitzky-Golay smoothing (SGS) and Wavelet Transform Denoising (WTD) was used to smooth data and filter noise, and the coefficient of the non-uniform variation method was proposed for early warning. The THM data, including four types of sensors, were attempted using the proposed method. Firstly, missing values, outliers, and detrend in the data were processed, and then the data were smoothed by SGS. Furthermore, data denoising was carried out by selecting wavelet basis functions, decomposition scales, and reconstruction. Finally, the coefficient of non-uniform variation was employed to calculate the yellow and red thresholds. In data smoothing, it was found that the Signal Noise Ratio (SNR) and Root Mean Square Error (RMSE) of SGS smoothing were superior to those of the moving average smoothing and five-point cubic smoothing by approximately 10% and 30%, respectively. An interesting phenomenon was discovered: the maximum and minimum values of the denoising effects with different wavelet basis functions after selection differed significantly, with the SNR differing by 14%, the RMSE by 8%, and the r by up to 80%. It was found that the wavelet basis functions vary, while the decomposition scales are consistently set at three layers. SGS and WTD can effectively reduce the complexity of the data while preserving its key characteristics, which has a good denoising effect. The yellow and red warning thresholds are categorized into conventional and critical controls, respectively. This early warning method dramatically improves the efficiency of tunnel safety control.
RESUMO
The pandemic COVID-19 which has spread over the world in early 2020 has caused significant impacts not only on health and life, but also on production activities and freight work. However, few studies were about the effect of COVID-19 on the performance of cities' logistics. Hence, this study focuses on the Belt and Road Initiative (BRI) and compares the changes in logistics performance from a spatial perspective caused by COVID-19 that are reflected on the highway freight between its 18 node cities in 2019 and 2020 of the same periods for 72 days. This study uses the entropy weight method to reflect the impact that COVID-19 has caused to the logistics level. Based on the modified gravity model, the impact on the logistics spatial connection between node cities was analyzed. These two aspects have been combined to analyze the logistics performance. The results show that the node cities have been affected by COVID-19 dissimilarly, and the impact has regional characteristics. The logistics level and spatial connection of Wuhan are the most seriously declined. The decline in logistics level has the same spatial variation law as the confirmed cases. The logistics connection between Wuhan and the surrounding node cities and the three-node cities in the northeast of China are also severely affected by the pandemic because of the expressway control policies. The regional distribution of logistics performance has differences, and the correlation of the logistics level and logistics spatial connection decreases. Besides, this study puts forward different recovery suggestions and policies for different belts in the BRI, such as focusing on restoring areas and giving full play to the role of the Chengdu-Chongqing urban agglomeration and logistics corridor. Finally, further provides corresponding suggestions for reducing the impact of emergencies from the perspectives of logistics hubs.
