Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Seismic liquefaction potential assessed by support vector machines approaches
Abstract Liquefaction of loose, saturated granular soils during earthquakes poses a major hazard in many regions of the world. Determining the liquefaction potential of soils induced by earthquakes is a major concern and an essential criterion in the design process of civil engineering structures. The present study examines the potential of support vector machines (SVMs) for assessing liquefaction potential based on cone penetration test (CPT) field data. A hybrid model based on a combination of SVMs and particle swarm optimization (PSO) is proposed in this study to improve the forecasting performance. PSO was employed in selecting the appropriate SVM parameters to enhance forecasting accuracy. Nine parameters, such as earthquake magnitude, the water table, the total vertical stress, the effective vertical stress, the depth, the peak acceleration at the ground surface, the cyclic stress ratio, the mean grain size and the measured CPT tip resistance, were used as input parameters. Prediction results demonstrate that the classification accuracy rates of the developed PSO–SVM approach surpass those of a grid search and many other approaches.
Seismic liquefaction potential assessed by support vector machines approaches
Abstract Liquefaction of loose, saturated granular soils during earthquakes poses a major hazard in many regions of the world. Determining the liquefaction potential of soils induced by earthquakes is a major concern and an essential criterion in the design process of civil engineering structures. The present study examines the potential of support vector machines (SVMs) for assessing liquefaction potential based on cone penetration test (CPT) field data. A hybrid model based on a combination of SVMs and particle swarm optimization (PSO) is proposed in this study to improve the forecasting performance. PSO was employed in selecting the appropriate SVM parameters to enhance forecasting accuracy. Nine parameters, such as earthquake magnitude, the water table, the total vertical stress, the effective vertical stress, the depth, the peak acceleration at the ground surface, the cyclic stress ratio, the mean grain size and the measured CPT tip resistance, were used as input parameters. Prediction results demonstrate that the classification accuracy rates of the developed PSO–SVM approach surpass those of a grid search and many other approaches.
Seismic liquefaction potential assessed by support vector machines approaches
Xue, Xinhua (Autor:in) / Yang, Xingguo (Autor:in)
2015
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
BKL:
56.00$jBauwesen: Allgemeines
/
38.58
Geomechanik
/
38.58$jGeomechanik
/
56.20
Ingenieurgeologie, Bodenmechanik
/
56.00
Bauwesen: Allgemeines
/
56.20$jIngenieurgeologie$jBodenmechanik
RVK:
ELIB18
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