Probabilistic Liquefaction Hazard Analysis for Four Canadian Cities

Goda, Katsuichiro; Atkinson, Gail M.; Hunter, J.A.; Crow, Heather; Motazedian, Dariush

doi:10.1785/0120100094

Cited by 30 publications

(25 citation statements)

References 28 publications

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“…It is noted that the proposed framework is focused on a scenario earthquake, which is suitable for back analysis of pile foundation performance. This can be further extended to take into account multiple scenarios that are consistent with regional seismic hazard (similar extension for liquefaction hazard analysis was demonstrated in Goda et al [15]). The extended framework is more applicable to retrofitting of existing piles and design of new piles; such extension will be reported elsewhere and is beyond the scope of the current study.…”

Section: Introductionmentioning

confidence: 83%

“…Using the first-order reliability method, Juang et al [22] developed a similar model based on V S data. The significance of these developed models is that key uncertainties associated with the input data/ parameters and the adopted models themselves are taken into account; the developed models can evaluate unbiased potential of liquefaction initiation, and are useful to conduct probabilistic liquefaction hazard analysis [15].…”

Section: Assessment Of the Unsupported Pile Length D Lmentioning

confidence: 99%

“…Using these conditional statistics of PGA bldg , a simulation-based liquefaction potential assessment using Eqs. (6)-(9) can be carried out [15]. A typical outcome from such assessment is the profile of liquefied and non-liquefied soil layers.…”

Section: Pile Foundation Stability Due To Buckling: Base Casementioning

confidence: 99%

See 2 more Smart Citations

Probabilistic buckling analysis of axially loaded piles in liquefiable soils

Bhattacharya

Goda

2013

Soil Dynamics and Earthquake Engineering

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Section: Introductionmentioning

confidence: 83%

Section: Assessment Of the Unsupported Pile Length D Lmentioning

confidence: 99%

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Probabilistic buckling analysis of axially loaded piles in liquefiable soils

Bhattacharya

Goda

2013

Soil Dynamics and Earthquake Engineering

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“…Because of the regional scale of this analysis though, site-specific soil profiles (as distinct from V S profile) are not taken into account in determining whether a soil layer is liquefiable. Goda et al (2011) suggest the use of "typical" soil profiles to determine the liquefaction susceptibility of a soil layer at a regional scale. Borehole data at sites close to the 13 V S profile sites are available from the Canterbury Geotechnical Database (2013c).…”

Section: Forecast Model Inputsmentioning

confidence: 99%

Evaluating simplified methods for liquefaction assessment for loss estimation

Kongar

Rossetto

Giovinazzi

2017

Nat. Hazards Earth Syst. Sci.

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Abstract. Currently, some catastrophe models used by the insurance industry account for liquefaction by applying a simple factor to shaking-induced losses. The factor is based only on local liquefaction susceptibility and this highlights the need for a more sophisticated approach to incorporating the effects of liquefaction in loss models. This study compares 11 unique models, each based on one of three principal simplified liquefaction assessment methods: liquefaction potential index (LPI) calculated from shear-wave velocity, the HAZUS software method and a method created specifically to make use of USGS remote sensing data. Data from the September 2010 Darfield and February 2011 Christchurch earthquakes in New Zealand are used to compare observed liquefaction occurrences to forecasts from these models using binary classification performance measures. The analysis shows that the best-performing model is the LPI calculated using known shear-wave velocity profiles, which correctly forecasts 78 % of sites where liquefaction occurred and 80 % of sites where liquefaction did not occur, when the threshold is set at 7. However, these data may not always be available to insurers. The next best model is also based on LPI but uses shear-wave velocity profiles simulated from the combination of USGS V S30 data and empirical functions that relate V S30 to average shear-wave velocities at shallower depths. This model correctly forecasts 58 % of sites where liquefaction occurred and 84 % of sites where liquefaction did not occur, when the threshold is set at 4. These scores increase to 78 and 86 %, respectively, when forecasts are based on liquefaction probabilities that are empirically related to the same values of LPI. This model is potentially more useful for insurance since the input data are publicly available. HAZUS models, which are commonly used in studies where no local model is available, perform poorly and incorrectly forecast 87 % of sites where liquefaction occurred, even at optimal thresholds. This paper also considers two models (HAZUS and EPOLLS) for estimation of the scale of liquefaction in terms of permanent ground deformation but finds that both models perform poorly, with correlations between observations and forecasts lower than 0.4 in all cases. Therefore these models potentially provide negligible additional value to loss estimation analysis outside of the regions for which they have been developed.

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“…In these approaches the standard liquefaction assessment follows a PSHA in which the joint probability distribution of the two key input parameters of the conventional liquefaction assessment, PGA and earthquake moment magnitude for earthquake scenarios, is determined. Fully probabilistic methods for liquefaction potential evaluation (Goda et al, 2011) should take into account uncertainties in both ground motion (i.e. earthquake occurrence and ground-motion intensity) and soil resistance (i.e.…”

Section: Deterministic Vs Probabilistic Approachesmentioning

confidence: 99%