• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.391-411
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• 2023

Aggregates play the skeleton and supporting role in the construction field, high-precision measurement and high-efficiency analysis of aggregates are frequently employed to evaluate the project quality. Aiming at the unbalanced operation time and segmentation accuracy for multi-class segmentation algorithms of aggregate images, a Chaotic Sparrow Search Algorithm (CSSA) is put forward to optimize it. In this algorithm, the chaotic map is combined with the sinusoidal dynamic weight and the elite mutation strategies; and it is firstly proposed to promote the SSA's optimization accuracy and stability without reducing the SSA's speed. The CSSA is utilized to optimize the popular multi-class segmentation algorithm-Multiple Entropy Thresholding (MET). By taking three METs as objective functions, i.e., Kapur Entropy, Minimum-cross Entropy and Renyi Entropy, the CSSA is implemented to quickly and automatically calculate the extreme value of the function and get the corresponding correct thresholds. The image adaptive multi-class segmentation model is called CSSA-MET. In order to comprehensively evaluate it, a new parameter I based on the segmentation accuracy and processing speed is constructed. The results reveal that the CSSA outperforms the other seven methods of optimization performance, as well as the quality evaluation of aggregate images segmented by the CSSA-MET, and the speed and accuracy are balanced. In particular, the highest I value can be obtained when the CSSA is applied to optimize the Renyi Entropy, which indicates that this combination is more suitable for segmenting the aggregate images.

• Earthquakes and Structures
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• v.27 no.5
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• pp.419-429
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• 2024

The safety and stability of bridges are critical to traffic safety. However, post-earthquake ground motion records often contain noise, which undermines the accuracy of seismic response analysis in bridge structures. To tackle this issue, we introduce a method that optimizes Variational Mode Decomposition (VMD) parameters using the Sparrow Search Algorithm (SSA) and combines it with Wavelet Thresholding (WT) to eliminate noise from strong motion signals. SSA is employed to identify the optimal VMD parameters [K, α], followed by the selection of effective modes based on the Variance Contribution Rate (VCR). These modes are then subjected to WT noise reduction, resulting in a high-quality reconstructed strong motion record. The method was validated using both simulated signals and ground motion records. In simulations, it demonstrated a 31.35% reduction in Root Mean Square Error (RMSE), a 31.6% decrease in the Smoothness Indicator (R), and a 1.17% improvement in the Correlation Coefficient (CC), compared to other methods. For ground motion records, it more accurately preserved seismic features than traditional wavelet denoising. When applied to the seismic response analysis of the Dahejia Bridge during the Jishishan earthquake, the denoised ground motion records obtained by this method produced force predictions on pier bearings that closely matched the field-observed damage, outperforming predictions based on traditional wavelet denoising. These findings confirm the accuracy and practicality of the proposed method.