Seismic Risk Management

Seismic risk management is a problem of many dimensions, involving multiple
inputs, interactions within risk factors, criteria, alternatives and stakeholders.
The deployment of this process is inherently fraught with the issues of
complexity, ambiguity and uncertainty, posing extra challenges in the
assessment, modelling and management stages. The complexity of earthquake
impacts and the uncertain nature of information necessitate the establishment
of a systematic approach to address the risk of many effects of seismic events in
a reliable and realistic way.
To fulfill this need, the study applies a systematic approach to the assessment
and management of seismic risk and uses an integrated risk structure. The
fuzzy set theory was used as a formal mathematical basis to handle
uncertainties involved within risk parameters. Throughout the process, the
potential impacts of an earthquake as the basic criteria for risk assessment
were identified and relations between them were accommodated through a
hierarchical structure. The various impacts of an earthquake are then
aggregated through a composite fuzzy seismic risk index (FSRi) to screen and
prioritize the retrofitting of a group of school buildings in Iran.
Given the imprecise data which is the prime challenge for development of any
risk model, the proposed model demonstrates a more reliable and robust
methodology to handle vague and imprecise information. The significant
feature of the model is its transparency and flexibility in aggregating, tracing
and monitoring the risk impacts. The novelty of this study is that it serves as
the first attempt of the process of a knowledge base risk-informed system for
ranking and screening the retrofitting group of school buildings. The model is
capable of integrating various forms of knowledge (quantitative and qualitative
information) extracted from different sources (facts, algorithms, standards and
experience). The outcomes of the research collectively demonstrate that the
proposed system supports seismic risk management processes effectively and
efficiently.