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Accelerogram-Based Method for Quick Assessment of Liquefaction Occurrence
An accelerogram-based method is developed and validated for the quick assessment of liquefaction occurrence based on ground motion records. In this method, two frequency-related ground motion indices, termed RL and MIFr, are defined and extracted from accelerograms using signal-processing techniques. RL and MIFr indicate the richness of the low-frequency components and the temporal variation rate of the mean instantaneous frequency in the ground motion records, respectively. A new liquefaction database consisting of ground motion stations with both ground motion records and the corresponding liquefaction observations is compiled. Logistic regression is used to develop a new liquefaction classification model that takes RL and MIFr as inputs and calculates a liquefaction indicator (LQI) that can be used to assess liquefaction occurrence. The performance of the proposed method is evaluated and compared with existing accelerogram-based liquefaction assessment methods using a common database, and the method is further validated using a new liquefaction data set. The proposed method demonstrated superior performance, with an overall accuracy of 92.8% for the common data set. The proposed method has promising potentials for applications in real-time disaster mitigation systems and rapid postearthquake loss estimations.
Accelerogram-Based Method for Quick Assessment of Liquefaction Occurrence
An accelerogram-based method is developed and validated for the quick assessment of liquefaction occurrence based on ground motion records. In this method, two frequency-related ground motion indices, termed RL and MIFr, are defined and extracted from accelerograms using signal-processing techniques. RL and MIFr indicate the richness of the low-frequency components and the temporal variation rate of the mean instantaneous frequency in the ground motion records, respectively. A new liquefaction database consisting of ground motion stations with both ground motion records and the corresponding liquefaction observations is compiled. Logistic regression is used to develop a new liquefaction classification model that takes RL and MIFr as inputs and calculates a liquefaction indicator (LQI) that can be used to assess liquefaction occurrence. The performance of the proposed method is evaluated and compared with existing accelerogram-based liquefaction assessment methods using a common database, and the method is further validated using a new liquefaction data set. The proposed method demonstrated superior performance, with an overall accuracy of 92.8% for the common data set. The proposed method has promising potentials for applications in real-time disaster mitigation systems and rapid postearthquake loss estimations.
Accelerogram-Based Method for Quick Assessment of Liquefaction Occurrence
Zhan, Weiwei (author) / Chen, Qiushi (author)
2021-05-25
Article (Journal)
Electronic Resource
Unknown
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