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Recommended by Panos LiatsisThe techniques of K-means algorithm and Gaussian Markov random field model are integrated to provide a Gaussian Markov random field model GMRFM feature which can describe the texture information of different pixel colors in an image. Based on this feature, an image retrieval method is also provided to seek the database images most similar to a given query image. In this paper, a genetic-based parameter detector is presented to decide the fittest parameters used by the proposed image retrieval method, as well. The experimental results manifested that the image retrieval method is insensitive to the rotation, translation, distortion, noise, scale, hue, light, and contrast variations, especially distortion, hue, and contrast variations.

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Simpli ed methods for assessing soil liquefaction potential based on the standard penetration test (SPT) are prevalent in practice and widely accepted by several seismic design codes. When encountering sites that have not been investigated using SPT, such as offshore sites or sites with a high level of gravel content, engineers can only substitute the methods based on piezocone penetration test data (CPT-q c methods) or shear wave velocity measurements (V S -based methods); however, these two approaches perform inconsistently with methods based on SPT data (SPT-N methods). As a result, this paper exploits the datasets consisting of SPT, CPTU, and in-situ seismic test measurements from 13 alluvium sites in the Taipei Basin to compare the performance of prevalent SPT-N, CPT-q c , and V S -based methods. The discrepancies (uncertainties) of these methods are characterized as Gaussian distribution models, which is believed to be a feasible strategy for predicting equivalent results for SPT-N methods when SPT data are not available. Finally, the application of the proposed models to liquefaction potential index evaluation is demonstrated using a real case study.

Simplified methods for assessing soil liquefaction potential based on the standard penetration test (SPT) are prevalent in practice and widely accepted by several seismic design codes. When encountering sites that have not been investigated using SPT, such as offshore sites or sites with a high level of gravel content, engineers can only substitute the methods based on piezocone penetration test data (CPT-qc methods) or shear wave velocity measurements (VS-based methods); however, these two approaches perform inconsistently with methods based on SPT data (SPT-N methods). As a result, this paper exploits the datasets consisting of SPT, CPTU, and in-situ seismic test measurements from 13 alluvium sites in the Taipei Basin to compare the performance of prevalent SPT-N, CPT-qc, and VS-based methods. The discrepancies (uncertainties) of these methods are characterized as Gaussian distribution models, which is believed to be a feasible strategy for predicting equivalent results for SPT-N methods when SPT data are not available. Finally, the application of the proposed models to liquefaction potential index evaluation is demonstrated using a real case study.