Multi-pulse decomposition for nonlinear seismic analysis of structural systems

Ahmadi, Ehsan; Salami, Mohammad R.; Risi, Raffaele De; Kashani, Mohammad M.; Alexander, Nicholas A

doi:10.1016/j.soildyn.2022.107531

Cited by 2 publications

(1 citation statement)

References 39 publications

(48 reference statements)

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“…The range of values and intervals of the parameters are shown in Table I (e.g. [6,2,30] represents an initial value of 6, an interval of 2, and an end value of 30). The order in which the model parameters are determined is sequential in the order of the table.…”

Section: E Parameter Optimizationmentioning

confidence: 99%

Intensity Prediction Model Based on Machine Learning for Regional Earthquake Early Warning

Zhang,

Lozano-Galant,

Xia

et al. 2024

IEEE Sensors J.

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Seismic intensity plays a crucial role in influencing the decision-making process of users utilizing earthquake early warning systems (EEWs) upon receiving warning information. Improving intensity warnings' speed and accuracy is vital. We present a straightforward and dependable model for predicting intensity, which is based only on location and magnitude information. We use the catalog of intensity data from the Japan Meteorological Agency (JMA) released as a dataset, totaling 944,877 intensity instances. To address the issue of imbalanced dataset distribution, we employ the Synthetic Minority Over-Sampling Technique (SMOTE) as a means to improve this situation. Considering the distribution of high intensity data and the importance of features in the model, we construct and jointly apply intensity prediction models for magnitude below 5.7 and above 5.7, respectively. Further, we analyze the robustness of the model by adding random errors for magnitude and location information. We test the transfer capability of the proposed model with four earthquake events in China. Further, we use 466 seismic events (20,542 intensity instances) without published intensity data from the Kyoshin network (K-NET) as the application dataset. We simulate the phenomenon of underestimation of large earthquakes and overestimation of small earthquakes, which is used to analyze the possible application of the proposed model to EEWs. The findings indicate that the model achieves an accuracy of 97.77% when subjected to a magnitude error of 0.3 and a location error of 0.2°. Finally, we analyze the timeliness of the proposed model with a magnitude 7.4 event in 2022.

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Section: E Parameter Optimizationmentioning

confidence: 99%