Probabilistic seismic hazard analysis of civil infrastructure - Seismic Risk Analysis and Management of Civil Infrastructure Systems - page 4

incorporated in modern PSHA studies through the use of a logic tree
method (Petersen et al. , 2008; Atkinson and Goda, 2011). Nevertheless, dif-
fi culties arise, because not all the models that analysts wish to apply are
based on consistent data/assumptions and, more importantly, a full range
of alternatives and uncertainties is not conceivable. This is an on-going issue
in PSHA, and needs to be improved in the near future. Furthermore, it is
important to carry out detailed and comprehensive sensitivity analyses as
a part of PSHA. The fi nal step of PSHA is the integration of hazard con-
tributions due to all possible scenarios and models/assumptions. This is
conventionally done using numerical integration, while Monte Carlo simu-
lation can be used as an alternative (Musson, 2000; Hong et al. , 2006).
Because the latter is versatile in dealing with various probabilistic models
and is easily extendable to advanced earthquake engineering analyses, this
chapter is focused on the Monte Carlo approach.
PSHA is an essential part of probabilistic seismic risk analysis (PSRA)
in the performance-based earthquake engineering (PBEE) framework
(Cornell et al. , 2002; McGuire, 2004; Goulet et al. , 2007; Ruiz-Garcia and
Miranda, 2007). The objective of advanced engineering analyses is to quan-
tify the extent of inelastic seismic demand (e.g. maximum inter-story drift)
and consequence (e.g. building damage cost and indirect cost related to
business down time) caused by extreme ground motions probabilistically.
Such analyses are facilitated by the use of a so-called fragility curve, which
is the conditional probability distribution of a seismic performance measure
as a function of seismic intensity level; in risk analysis, fragility functions
are integrated over all possible earthquake scenarios. The output is usually
expressed as a seismic risk curve, and can be used for facilitating informed
decision-making related to seismic risk mitigation (Goda and Hong, 2006;
Goulet et al. , 2007). The concerted efforts of developing seismic hazard and
fragility models for various seismic regions and structural types are one of
the signifi cant advancements in recent years.
PSHA can be combined with geotechnical techniques, such as the stress-
based liquefaction potential analysis of Seed and Idriss (1971). In the Seed
and Idriss stress-based method, the required input information is the
expected PGA and moment magnitude (both of which are available from
PSHA). With such information, the likelihood of liquefaction occurrence/
triggering can be evaluated in probabilistic terms (based on contributions
of various earthquake scenarios to the overall seismic hazard). By integrat-
ing PSHA with liquefaction assessment, a new engineering tool for proba-
bilistic liquefaction hazard analysis (PLHA), which is useful for rational
decision making in geotechnical applications, can be developed (Kramer
and Mayfi eld, 2007; Juang et al. , 2008; Goda et al. , 2011). Furthermore, addi-
tional model components, such as a prediction model for ground settlement