Efficient evaluation of performance-based earthquake engineering equations

Earthq Engng Struct Dyn

Burks

Baker

2015

Self Cite

This paper examines four methods by which ground motions can be selected for dynamic seismic response analyses of engineered systems when the underlying seismic hazard is quantified via ground motion simulation rather than empirical ground motion prediction equations. Even with simulation-based seismic hazard, a ground motion selection process is still required in order to extract a small number of time series from the much larger set developed as part of the hazard calculation. Four specific methods are presented for ground motion selection from simulation-based seismic hazard analyses, and pros and cons of each are discussed via a simple and reproducible illustrative example. One of the four methods (method 1 'direct analysis') provides a 'benchmark' result (i.e. using all simulated ground motions), enabling the consistency of the other three more efficient selection methods to be addressed. Method 2 ('stratified sampling') is a relatively simple way to achieve a significant reduction in the number of ground motions required through selecting subsets of ground motions binned based on an intensity measure, IM. Method 3 ('simple multiple stripes') has the benefit of being consistent with conventional seismic assessment practice using as-recorded ground motions, but both methods 2 and 3 are strongly dependent on the efficiency of the conditioning IM to predict the seismic responses of interest. Method 4 ('GCIM-based selection') is consistent with 'advanced' selection methods used for as-recorded ground motions, and selects subsets of ground motions based on multiple IMs, thus overcoming this limitation in methods 2 and 3.

Section: Methods 3: Simple Multiple Stripes Selectionmentioning

confidence: 99%

Ground motion selection for simulation‐based seismic hazard and structural reliability assessment

Earthq Engng Struct Dyn

Burks

Baker

2015

Self Cite

“…In particular, the computational scheme for directly performing the numerical integrations utilizes the magnitude-oriented adaptive quadrature (MAQ) algorithm [16]. The integration parameters used were an error tolerance of 0.005 (0.5%) and a maximum number of function evaluations of 300 (for all computations presented here integral convergence was achieved).…”

Section: Approximate Methods Of Uncertainty Propagation Used In Loss mentioning

confidence: 99%

“…[16] with an error tolerance of 0.001 (0.1%). For comparison the computational demand when computing the loss hazard curve with a larger (1%) error tolerance is also given.…”

Section: Computational Demandmentioning

confidence: 99%

Accuracy of approximate methods of uncertainty propagation in seismic loss estimation

Lee

2010

Structural Safety

Self Cite

In this paper the efficacy of an approximate method of uncertainty propagation, known as the first-order second-moment (FOSM) method, for use in seismic loss estimation is investigated. The governing probabilistic equations which define the Pacific Earthquake Engineering Research (PEER)-based loss estimation methodology used are discussed, and the proposed locations to use the FOSM approximations identified. The justification for the use of these approximations is based on a significant reduction in computational time by not requiring direct numerical integration, and the fact that only the first two moments of the distribution are known. Via various examples it is shown that great care should be taken in the use of such approximations, particularly considering the large uncertainties that must be propagated in a seismic loss assessment. Finally, a complete loss assessment of a structure is considered to investigate in detail the location where significant approximation errors are incurred, where caution must be taken in the interpretation of the results, and the computational demand of the various alternatives. KEYWORDSPerformance-based earthquake engineering (PBEE); aleatory uncertainty; epistemic uncertainty; first-order second-moment (FOSM) method; loss estimation; loss deaggregation.2

“…1.2x10 7 ) the loss hazard curve is particularly sensitive to the assumption of the L|DS correlation with error ratios greater than 2 when perfect L|DS correlations were assumed and less than 0.5 when zero L|DS correlations were assumed. Table 1 presents the computational times required when in performing the seismic loss assessment on a Pentium 4 processor with 3.0 GHz CPU and 512 MB RAM using the seismic loss assessment tool (SLAT) [40], which utilizes the magnitude-oriented adaptive quadrature algorithm [41]. As discussed by Bradley and Lee [10], and evident in Table 1, the effect of nonzero correlations drastically increases the computational demand to perform the analysis.…”

Section: Total Loss Given Collapse L|cmentioning

confidence: 99%

Component correlations in structure‐specific seismic loss estimation

Earthq Engng Struct Dyn

Lee

2009

Self Cite

This paper addresses correlations between multiple components in structure-specific seismic loss estimation. To date, the consideration of such correlations has been limited by methodological tractability; increased computational demand; and a paucity of data for their computation. The effect of component correlations, which arise in various forms, is however a significant factor affecting the results of structure-specific seismic loss estimation and therefore it is prudent that adequate consideration is given to their effect. This paper provides details of a tractable and computationally efficient seismic loss estimation methodology in which correlations can be considered. Methods to determine the necessary correlations are discussed, particularly those that can be used in the absence of sufficient empirical data, for which values are suggested based on judgement. The effects of various assumptions regarding correlations are illustrated via application to a case-study office structure. It is observed that certain correlation assumptions can lead to errors in excess of 50% in the lognormal standard deviation in the loss given intensity and loss hazard relationships, while full consideration of partial correlations is 50-times more computationally expensive than other assumptions.