New conditional, scenario-based, and non-conditional cumulative absolute velocity models for subduction tectonic settings

The PEER NGA-Sub ground-motion intensity measure database is used to develop new conditional ground-motion models (CGMMs), a set of scenario-based models, and non-conditional models to estimate the cumulative absolute velocity ([Formula: see text]) of ground motions from subduction zone earthquakes. In the CGMMs, the median estimate of [Formula: see text] is conditioned on the estimated peak ground acceleration ([Formula: see text]), the time-averaged shear-wave velocity in the top 30 m of the soil ([Formula: see text]), the earthquake magnitude ([Formula: see text]), and the spectral acceleration at the period of 1 s ([Formula: see text]). Multiple scenario-based [Formula: see text] models are developed by combining the CGMMs with pseudo-spectral acceleration ([Formula: see text]) ground-motion models (GMMs) for [Formula: see text] and [Formula: see text] to directly estimate [Formula: see text] given an earthquake scenario and site conditions. Scenario-based [Formula: see text] models are capable of capturing the complex ground-motion effects (e.g. soil non-linearity and regionalization effects) included in their underlying [Formula: see text]/[Formula: see text] GMMs. This approach also ensures the consistency of the [Formula: see text] estimates with a [Formula: see text] design spectrum. In addition, two non-conditional [Formula: see text] GMMs are developed using Bayesian hierarchical regressions. Finally, we present comparisons between the developed models. The comparisons show that if non-conditional GMMs are properly constrained, they are consistent with scenario-based GMMs. The [Formula: see text] GMMs developed in this study advance the performance-based earthquake engineering practice in areas affected by subduction zone earthquakes.

Hazard characterization for alternative intensity measures using the total probability theorem

Since their inception in the 1980s, simplified procedures for the analysis of liquefaction hazards have typically characterized seismic loading using a combination of peak ground acceleration and earthquake magnitude. However, more recent studies suggest that certain evolutionary intensity measures (IMs) such as Arias intensity or cumulative absolute velocity may be more efficient and sufficient predictors of liquefaction triggering and its consequences. Despite this advantage, widespread hazard characterizations for evolutionary IMs are not yet feasible due to a relatively incomplete representation of the ground motion models (GMMs) needed for probabilistic seismic hazard analysis (PSHA). Without widely available hazard curves for evolutionary IMs, current design codes often rely on spectral targets for ground motion selection and scaling, which are shown in this study to indirectly result in low precision of evolutionary IMs often associated with liquefaction hazards. This study presents a method to calculate hazard curves for arbitrary intensity measures, such as evolutionary IMs for liquefaction hazard analyses, without requiring an existing GMM. The method involves the conversion of a known IM hazard curve into an alternative IM hazard curve using the total probability theorem. The effectiveness of the method is illustrated by comparing hazard curves calculated using the total probability theorem to the results of a PSHA to demonstrate that the proposed method does not result in additional uncertainty under idealized conditions and provides a range of possible hazard values under most practical conditions. The total probability theorem method can be utilized by practitioners and researchers to select ground motion time series that target alternative IMs for liquefaction hazard analyses or other geotechnical applications. This method also allows researchers to investigate the efficiency, sufficiency, and predictability of new, alternative IMs without necessarily requiring GMMs.

Research on Improved Seismic Instrumentation System for Nuclear Power Plants

According to the requirements of nuclear safety regulations, nuclear power plants must be equipped with seismic instrumentation systems, which are mainly used for monitoring alarm and automatic shutdown alarm during an earthquake. Both the second and third generation NPPs adopt Peak Ground Acceleration (PGA). However, among the seismic acceleration characteristics, isolated and prominent single high frequency acceleration peaks have no decisive influence on the seismic response. Especially when the earthquake monitoring alarm is at 1 out of 7, it is likely to cause a false alarm or false shutdown. In addition, it usually takes one month or more for the NPPs to restart after the shutdown. In this paper, an improved seismic instrumentation system based on the existing system is proposed. For high intensity areas, three components resultant acceleration is used to judge the 2 out of 4 logic of the automatic seismic trip system(ASTS). For low intensity areas, the seismic failure level is evaluated quickly by using three components resultant acceleration, seismic instrument intensity, cumulative absolute velocity, floor response spectrum and other multi-parameters, avoiding unnecessary and long-term shutdown inspection.

Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features

Machine learning (ML)-aided structural health monitoring (SHM) can rapidly evaluate the safety and integrity of the aging infrastructure following an earthquake. The conventional damage features used in ML-based SHM methodologies face the curse of dimensionality. This paper introduces low dimensional, namely, cumulative absolute velocity (CAV)-based features, to enable the use of ML for rapid damage assessment. A computer experiment is performed to identify the appropriate features and the ML algorithm using data from a simulated single-degree-of-freedom system. A comparative analysis of five ML models (logistic regression (LR), ordinal logistic regression (OLR), artificial neural networks with 10 and 100 neurons (ANN10 and ANN100), and support vector machines (SVM)) is performed. Two test sets were used where Set-1 originated from the same distribution as the training set and Set-2 came from a different distribution. The results showed that the combination of the CAV and the relative CAV with respect to the linear response, i.e., RCAV, performed the best among the different feature combinations. Among the ML models, OLR showed good generalization capabilities when compared to SVM and ANN models. Subsequently, OLR is successfully applied to assess the damage of two numerical multi-degree of freedom (MDOF) models and an instrumented building with CAV and RCAV as features. For the MDOF models, the damage state was identified with accuracy ranging from 84% to 97% and the damage location was identified with accuracy ranging from 93% to 97.5%. The features and the OLR models successfully captured the damage information for the instrumented structure as well. The proposed methodology is capable of ensuring rapid decision-making and improving community resiliency.

Ground-Motion Intensity Measure Correlations on Interface and Intraslab Subduction Zone Earthquakes Using the NGA-Sub Database

ABSTRACT The NGA-Sub (subduction zone earthquake) database developed by the Pacific Earthquake Engineering Research center is used to derive new correlation coefficients for a number of ground-motion intensity measure (IM) parameters from ground motions in subduction zone earthquakes, considering both interface and intraslab tectonic settings. The IMs include peak ground acceleration, pseudospectral accelerations with periods from 0.01 to 10 s, Arias intensity, cumulative absolute velocity, peak ground velocity, and significant duration. Comparisons of the estimated correlation coefficients for ground motions from the interface and intraslab tectonic settings generally show a good agreement. Our estimations are also in good agreement with correlation coefficients estimated in previous studies that used ground motions from shallow crustal earthquakes, supporting the concept that any variation in correlation coefficients comes from spectral shape (i.e., the distribution of peaks and troughs) rather than tectonic region. This study also explores the influence of parameters such as magnitude, distance, and site conditions on the estimated correlation coefficients. We did not find apparent trends of the correlation coefficients with respect to these parameters. Finally, we propose analytical models to estimate correlation coefficients between the IMs explored in this study, considering both subduction interface and intraslab tectonic settings.

Magnitude Estimation and Onsite Earthquake Early Warning using Cumulative Absolute Velocity in Taiwan

<p>Real-time magnitude determination is one of the critical issues for earthquake early warning (EEW). Magnitude determination may have saturation situation using initial seismic signals after an earthquake occurrence. Previous studies utilized eventual cumulative absolute velocity (eCAV) to determine magnitude up to 9.0 without any saturation. However, to determine eCAV will be too late for EEW application. In order to shorten time to obtain eCAV, 4,754 strong motion records from 64 events with M<sub>L </sub>large than 5.5 in Taiwan are used to establish the relationship between eCAV and initial shaking parameters (initial CAV, initial cumulative absolute displacement, initial cumulative absolute integral displacement,  P<sub>d</sub> and  τ<sub>c</sub>) from 1 s to 20 s after P arrival. Our preliminary results show that eCAV can be estimated using initial shaking parameters. Logarithm linear correlation coefficients vary from 0.78 to 0.97 with standard deviations from 0.27 to 0.10 for time windows from 1 s to 20 s after P arrival. Eventually, we can timely estimate eCAV for magnitude determination as well as or on-site EEW purpose.</p>

Can geotechnical liquefaction indices serve as predictors of foundation settlement?

Geotechnical liquefaction indices, such as the liquefaction potential index, are commonly used as proxies for the risk of liquefaction-induced damage at site or regional scales. However, these indices were developed based on surficial manifestations of soil liquefaction in the free field, and, as such, they have been shown to correlate better with land damage than foundation damage. This study evaluates the ability of three geotechnical liquefaction indices to predict foundation settlement on liquefiable soils, as compared to both conventional ground motion intensity measures (IMs) and the term for site and ground motion effects in a probabilistic model specifically developed for foundation settlement. A new metric for the predictive ability of these measures, skill, is proposed to quantify the total uncertainty in settlement predictions using a given measure. The Ishihara-inspired liquefaction potential index is found to be the optimum index, and cumulative absolute velocity [Formula: see text] as predicted on outcropping rock is found to be the optimum IM. However, although both measures are regionally applicable, neither outperforms the site term from the probabilistic settlement model, which was developed using the same numerical database used in this study.