Seismic risk assessment of reinforced concrete buildings using fuzzy based techniques

Lessons learned from performance of reinforced concrete buildings during previous earthquakes and researches over the last three decades have led to the development of improved codes for the seismic design of buildings. However, the existing buildings are vulnerable because they were designed to the older codes and/or posses structural irregularities. Most of these buildings are still occupied which makes evaluation and retrofit necessary in order to minimize the damage induced by earthquakes. Because of large volume of vulnerable buildings, however, consideration of a risk-based seismic assessment and retrofit prioritization is plausible. Seismic (earthquake) risk is the probability (likelihood) that a specified loss will exceed some quantifiable value during a given exposure time. Seismic risk assessment is intricately dependent on site seismic hazard, building vulnerability, and importance/exposure factor. This intricate process is modelled through a two-tier heuristics-based approach. The two-tier models utilized a hierarchical structure by incorporating wisdom and intuitive knowledge obtained from practitioners and experts. The Tier 1 model considers building performance modifiers (factors) in congruence with the FEMA 154 rapid visual screening manual including: (i) building type, (ii) vertical irregularity, (iii) plan irregularity, (iv) year of construction, and (v) construction quality. These performance modifiers can readily be obtained from a walk down survey and engineering drawings. The Tier 2 model is an extension of the building vulnerability module of Tier 1 that incorporates detailed performance modifiers as specified in FEMA 310. In the present study seismic risk assessment is based on evaluating risk index of the RC building, which will be obtained following a seven-step aggregation scheme. Uncertainty due to subjectivity involved in the evaluation process is handled through the fuzzy set theory and fuzzy rule based modelling is utilized to inferences through the proposed hierarchical structure. The proposed methods are demonstrated and validated using the data from 1994 Northridge Earthquake (California) and the 2003 Bingol Earthquake (Turkey). The proposed methodology in modular form is implemented in a prototype MS Excel based software tool (CanRisk). The Canadian site seismic hazard is also incorporated into the CanRisk