Stochastic Generation of Peak Ground Accelerations Based on Single Seismic Event Data for Safety Assessment of Structures
The Korean Peninsula, characterized by low-to-moderate seismicity, faces a shortage of strong ground motion records, posing challenges for the seismic safety assessment of critical infrastructures. Given the rarity of large-magnitude earthquakes, generating a variety of earthquakes with rational val...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/21/10031 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846173503929188352 |
|---|---|
| author | Jihoon Seok Jeeho Lee |
| author_facet | Jihoon Seok Jeeho Lee |
| author_sort | Jihoon Seok |
| collection | DOAJ |
| description | The Korean Peninsula, characterized by low-to-moderate seismicity, faces a shortage of strong ground motion records, posing challenges for the seismic safety assessment of critical infrastructures. Given the rarity of large-magnitude earthquakes, generating a variety of earthquakes with rational values of Peak Ground Acceleration (PGA) is essential for robust seismic fragility and risk analysis. To address this, a new stochastic approach is proposed to simulate artificial earthquakes at multiple source-to-site distances and derive the probability distribution of PGA based on recorded data from a single seismic event. Two key source parameters, seismic moment and corner frequency, are treated as random variables with a negative correlation, reflecting their uncertainties and dependence on source-to-site distance. The Monte Carlo simulation with copula sampling of the key source parameters generates Fourier spectra for artificial earthquakes, which are transformed into the time domain to yield PGA distributions at various distances. A comparison with recorded data shows that the proposed method effectively simulates ground motion intensities, with no statistically significant differences between the simulated results and recorded data (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo>></mo><mn>0.05</mn></mrow></semantics></math></inline-formula>). The present method of determining PGA distributions provides a robust framework to enhance seismic risk analysis for the safety assessment of structures. |
| format | Article |
| id | doaj-art-0d35dda04e5d4791a88b151f1e25d925 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-0d35dda04e5d4791a88b151f1e25d9252024-11-08T14:34:12ZengMDPI AGApplied Sciences2076-34172024-11-0114211003110.3390/app142110031Stochastic Generation of Peak Ground Accelerations Based on Single Seismic Event Data for Safety Assessment of StructuresJihoon Seok0Jeeho Lee1Department of Civil and Environmental Engineering, Dongguk University, Seoul 04620, Republic of KoreaDepartment of Civil and Environmental Engineering, Dongguk University, Seoul 04620, Republic of KoreaThe Korean Peninsula, characterized by low-to-moderate seismicity, faces a shortage of strong ground motion records, posing challenges for the seismic safety assessment of critical infrastructures. Given the rarity of large-magnitude earthquakes, generating a variety of earthquakes with rational values of Peak Ground Acceleration (PGA) is essential for robust seismic fragility and risk analysis. To address this, a new stochastic approach is proposed to simulate artificial earthquakes at multiple source-to-site distances and derive the probability distribution of PGA based on recorded data from a single seismic event. Two key source parameters, seismic moment and corner frequency, are treated as random variables with a negative correlation, reflecting their uncertainties and dependence on source-to-site distance. The Monte Carlo simulation with copula sampling of the key source parameters generates Fourier spectra for artificial earthquakes, which are transformed into the time domain to yield PGA distributions at various distances. A comparison with recorded data shows that the proposed method effectively simulates ground motion intensities, with no statistically significant differences between the simulated results and recorded data (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo>></mo><mn>0.05</mn></mrow></semantics></math></inline-formula>). The present method of determining PGA distributions provides a robust framework to enhance seismic risk analysis for the safety assessment of structures.https://www.mdpi.com/2076-3417/14/21/10031stochastic approachpeak ground accelerationmonte carlo simulationcopula samplingseismic safety assessmentartificial earthquake generation |
| spellingShingle | Jihoon Seok Jeeho Lee Stochastic Generation of Peak Ground Accelerations Based on Single Seismic Event Data for Safety Assessment of Structures Applied Sciences stochastic approach peak ground acceleration monte carlo simulation copula sampling seismic safety assessment artificial earthquake generation |
| title | Stochastic Generation of Peak Ground Accelerations Based on Single Seismic Event Data for Safety Assessment of Structures |
| title_full | Stochastic Generation of Peak Ground Accelerations Based on Single Seismic Event Data for Safety Assessment of Structures |
| title_fullStr | Stochastic Generation of Peak Ground Accelerations Based on Single Seismic Event Data for Safety Assessment of Structures |
| title_full_unstemmed | Stochastic Generation of Peak Ground Accelerations Based on Single Seismic Event Data for Safety Assessment of Structures |
| title_short | Stochastic Generation of Peak Ground Accelerations Based on Single Seismic Event Data for Safety Assessment of Structures |
| title_sort | stochastic generation of peak ground accelerations based on single seismic event data for safety assessment of structures |
| topic | stochastic approach peak ground acceleration monte carlo simulation copula sampling seismic safety assessment artificial earthquake generation |
| url | https://www.mdpi.com/2076-3417/14/21/10031 |
| work_keys_str_mv | AT jihoonseok stochasticgenerationofpeakgroundaccelerationsbasedonsingleseismiceventdataforsafetyassessmentofstructures AT jeeholee stochasticgenerationofpeakgroundaccelerationsbasedonsingleseismiceventdataforsafetyassessmentofstructures |