Refine
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)
Rapid Visual Screening (RVS) is a procedure that estimates structural scores for buildings and prioritizes their retrofit and upgrade requirements. Despite the speed and simplicity of RVS, many of the collected parameters are non-commensurable and include subjectivity due to visual observations. This might cause uncertainties in the evaluation, which emphasizes the use of a fuzzy-based method. This study aims to propose a novel RVS methodology based on the interval type-2 fuzzy logic system (IT2FLS) to set the priority of vulnerable building to undergo detailed assessment while covering uncertainties and minimizing their effects during evaluation. The proposed method estimates the vulnerability of a building, in terms of Damage Index, considering the number of stories, age of building, plan irregularity, vertical irregularity, building quality, and peak ground velocity, as inputs with a single output variable. Applicability of the proposed method has been investigated using a post-earthquake damage database of reinforced concrete buildings from the Bingöl and Düzce earthquakes in Turkey.
Earthquake is among the most devastating natural disasters causing severe economical, environmental, and social destruction. Earthquake safety assessment and building hazard monitoring can highly contribute to urban sustainability through identification and insight into optimum materials and structures. While the vulnerability of structures mainly depends on the structural resistance, the safety assessment of buildings can be highly challenging. In this paper, we consider the Rapid Visual Screening (RVS) method, which is a qualitative procedure for estimating structural scores for buildings suitable for medium- to high-seismic cases. This paper presents an overview of the common RVS methods, i.e., FEMA P-154, IITK-GGSDMA, and EMPI. To examine the accuracy and validation, a practical comparison is performed between their assessment and observed damage of reinforced concrete buildings from a street survey in the Bingöl region, Turkey, after the 1 May 2003 earthquake. The results demonstrate that the application of RVS methods for preliminary damage estimation is a vital tool. Furthermore, the comparative analysis showed that FEMA P-154 creates an assessment that overestimates damage states and is not economically viable, while EMPI and IITK-GGSDMA provide more accurate and practical estimation, respectively.