Search Results (2)

In order to investigate the analysis and processing methods for nonstationary signals generated in bridge health monitoring systems, this study combines the advantages of complete ensemble empirical mode decomposition (CEEMD) and wavelet threshold denoising algorithms to construct the CEEMD–wavelet threshold denoising algorithm. The algorithm follows the following steps: first, add noise to the monitoring data and obtain all the mode components through empirical mode decomposition (EMD), denoise the mode components with noise using the wavelet threshold function to remove the noise components, select the optimal stratification for denoising the monitoring data of the Guozigou Bridge in Xinjiang in January 2023, determine the wavelet type and threshold selection criteria, and reconstruct the denoised intrinsic mode function (IMF) components to achieve accurate extraction of the effective signal. By referencing the deflection, temperature, and strain data of the Guozigou Bridge in Xinjiang in January 2023 and comparing the data cleaned by different mode decomposition and wavelet threshold denoising methods, the results show that compared with empirical mode decomposition (EMD)–wavelet threshold denoising and variational mode decomposition (VMD)–wavelet threshold denoising, the signal-to-noise ratios and root-mean-square errors of the four types of monitoring data obtained by the algorithm proposed in this study are the most ideal. Under the premise of minimizing reconstruction errors when processing a large amount of data, it has better convergence, verifying the practicality and reliability of the algorithm in the field of bridge health monitoring data cleaning and providing a certain reference value for further research in the field of signal processing. The computational method constructed in this study will provide theoretical support for data cleaning and analysis of nonstationary and nonlinear random signals, which is conducive to further promoting the improvement of bridge health monitoring systems. Full article

Landslide disasters frequently occur along the highway G30 in the Guozigou Valley, the corridor of energy, material, economic and cultural exchange, etc., between Yili and other cities of China and Central Asia. However, little attention has been paid to assess the detailed landslide susceptibility of the strategically important highway, especially with high spatial resolution data and the generative presence-only MaxEnt model. Landslide susceptibility assessment (LSA) is a first and vital step for preventing and mitigating landslide hazards. The goal of the current study was to perform LSA for the landslide-prone highway G30 in Guozigou Valley, China with the aid of GIS tools and Chinese high resolution Gaofen-1 (GF-1) satellite data, and analyze and compare the performance of the maximum entropy (MaxEnt) model and logistic regression (LR). Thirty five landslides were determined in the study region, using GF-1 satellite data, official data, and field surveys. Seven landslide conditioning factors, including altitude, slope, aspect, gully density, lithology, faults density, and NDVI, were used to investigate their existing spatial relationships with landslide occurrences. The LR and MaxEnt model performance were assessed by the receiver operating characteristic curve, presenting areas under the curve equal to 0.85 and 0.94, respectively. The performance of the MaxEnt model was slightly better than that of the LR model. A landslide susceptibility map was created through reclassifying the landslides occurrence probability with the classification method of natural breaks. According to the MaxEnt model results, 3.29% and 3.82% of the study region is highly and very highly susceptible to future landslide events, respectively, with the highest landslide susceptibility along the highway. The generated landslide susceptibility map could help government agencies and decision-makers to make wise decisions for preventing or mitigating landslide hazards along the highway and design schemes of highway engineering and maintenance in Guozigou Valley, the mountainous areas. Full article