Developing a Knowledge Based Expert System (KBES) for Seismic Risk Management

Abstract

Seismic risk management is multidisciplinary process in which broad range of variables as well as great deal of uncertainty involved with both parameters and modeling process. Disaster system can be seen as combination of not only physical and structural consequence of a natural hazard but also socio-economical , environmental impacts also influence the assessment process. Decision making in such complex system that often rely on subjective and objective knowledge should be handled though a knowledge based approach. The study was launched to review and discuss multifaceted character of seismic risk and examining the feasibility of knowledge base system to model such problem effectively and efficiently. Such knowledge base system should capable of (1)integrating various perspective of seismic risk (2) conducting seismic risk assessment and (3) handling uncertainty within risk assessment and to support mitigation strategies.

Seismic and Tsunami Risk Analysis for Installing Resilient Power Systems Based on Isolated Microgrids on Buildings: The Case of Puerto Ayora in Santa Cruz Island, Galapagos

“…A conventional methodology based on a classical perspective determines the consequences or effects, for which a qualitative and quantitative expression of risk is defined. The qualitative risk is defined as the product of the probability and the consequence characterized as low, moderate, or high [32][33][34]. In contrast, quantitative risk is defined as the combination of three elements: hazard (H), exposure (E), and vulnerability (V) expressed by R = H × E × V [32].…”

Application of a multiple attribute group decision making (MAGDM) model for selecting appropriate maintenance strategy for marine and offshore machinery operations

“…Linguistic variables are often used when describing situations that are too complex and fuzzy to be analysed quantitatively (Vahdat et al, 2014a). Human judgements, including preferences, are often vague and their preferences cannot be indicated by an exact numerical value (Vahdat et al, 2014b), therefore, a more realistic approach may be to use linguistic assessments such as "very good", "medium good" and "good" instead of numerical values. (Choo et al, 1999).…”