Refine
Has Fulltext
- yes (12) (remove)
Document Type
- Article (11)
- Doctoral Thesis (1)
Keywords
- Erdbeben (6)
- Maschinelles Lernen (6)
- OA-Publikationsfonds2020 (6)
- rapid visual screening (4)
- Machine learning (3)
- damaged buildings (3)
- earthquake safety assessment (3)
- Erdbebensicherheit (2)
- Fuzzy-Logik (2)
- OA-Publikationsfonds2022 (2)
- Vulnerability assessment (2)
- buildings (2)
- earthquake (2)
- soft computing techniques (2)
- vulnerability assessment (2)
- Adaptive Pushover (1)
- Arc-direct energy deposition (1)
- Baustahl (1)
- Building safety assessment (1)
- Design Spectra (1)
- Dual phase steel (1)
- Earthquake (1)
- Erbeben (1)
- Fuzzy Logic (1)
- Fuzzy logic (1)
- Machine Learning (1)
- Marmara Region (1)
- Mild steel (1)
- Multi-criteria decision making (1)
- Neuronales Netz (1)
- OA-Publikationsfonds2021 (1)
- OA-Publikationsfonds2023 (1)
- RC Buildings (1)
- Rapid Visual Assessment (1)
- Rapid Visual Screening (1)
- Schwellenwert (1)
- Seismic Vulnerability (1)
- Seismic risk (1)
- Spannungs-Dehnungs-Beziehung (1)
- Stress-strain curve (1)
- Uncertainty (1)
- Vulnerability (1)
- adaptive pushover (1)
- artificial neural network (1)
- artificial neural networks (1)
- earthquake damage (1)
- earthquake vulnerability assessment (1)
- extreme events (1)
- machine learning (1)
- mitigation (1)
- natural hazard (1)
- rapid assessment (1)
- rapid classification (1)
- seismic assessment (1)
- seismic hazard analysis (1)
- seismic risk estimation (1)
- seismic vulnerability (1)
- site-specific spectrum (1)
- supervised learning (1)
- support vector machine (1)
The Marmara Region (NW Turkey) has experienced significant earthquakes (M > 7.0) to date. A destructive earthquake is also expected in the region. To determine the effect of the specific design spectrum, eleven provinces located in the region were chosen according to the Turkey Earthquake Building Code updated in 2019. Additionally, the differences between the previous and updated regulations of the country were investigated. Peak Ground Acceleration (PGA) and Peak Ground Velocity (PGV) were obtained for each province by using earthquake ground motion levels with 2%, 10%, 50%, and 68% probability of exceedance in 50-year periods. The PGA values in the region range from 0.16 to 0.7 g for earthquakes with a return period of 475 years. For each province, a sample of a reinforced-concrete building having two different numbers of stories with the same ground and structural characteristics was chosen. Static adaptive pushover analyses were performed for the sample reinforced-concrete building using each province’s design spectrum. The variations in the earthquake and structural parameters were investigated according to different geographical locations. It was determined that the site-specific design spectrum significantly influences target displacements for performance-based assessments of buildings due to seismicity characteristics of the studied geographic location.
The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning.