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A comprehensive methodology for the validation of simulated ground motions is presented. The suggested methodology can be geared toward any ground-motion simulation method and seismic response assessment, in a target engineering application. The methodology is founded on the comparison between conforming groups of ground-motion waveforms from recordings and simulations and their effect on a representative collection of structures that represent the engineering application. The comparison considers the statistics of earthquake scenarios at the level of the event and site parameters, the resulting waveform characteristics, and the subsequent structural responses. Regression models are developed at three levels (between structural responses and waveform characteristics, structural responses and event and site parameters, and waveform characteristics and event and site parameters). Similarities between the models from groups of recorded and simulated ground motions guide the validation process. The validation methodology is applied to CyberShake (v.15.12) simulations and for the estimation of the column drift ratio of a bridge structure. It is shown that CyberShake (v.15.12) can be used to assess the median seismic response of the used bridge. Some discrepancies between simulations and recordings are observed, which could be attributed to the basin and site-response models used for simulations. Further implementation and refinement of the suggested methodology are recommended to make broader conclusions.

There is a need for benchmarking and validating simulated ground motions in order for them to be utilized by the engineering community. Such validation may be geared towards a specific ground motion simulation method, a target engineering application, and a specific location; the validation presented herein focuses on a bridge engineering application in southern California. Catalogs of simulated ground motions representing a 200,000-year forecast are selected from the Southern California Earthquake Center CyberShake version 15.12 database for five sites in Southern California (~20,000 unscaled ground motions per site). They are used in Non-Linear Time History Analysis (NLTHA) of four Ordinary Standard Bridge structures. For each site, these data are used to obtain simulation-based Engineering Demand Parameter (EDP) hazard curves. These are compared against EDP hazard curves that are constructed using conventional methods based on empirical models, i.e., using recorded ground motions through Incremental Dynamic Analysis and integration over the Intensity Measure (IM) hazard curve. The two sets of simulation-based and conventional EDP hazard curves are compared at various return periods. To further account for the differences between simulated and recorded ground motions, direct comparisons are also made between IM hazard curves for simulated and recorded catalogs, as well as the EDP versus IM data obtained from NLTHA of the bridges. We observe that CyberShake simulates motions that yield similar EDP values compared to empirical data for shorter return periods. For longer return periods, however, EDPs from the simulation-based analysis tend to be lower than the EDPs obtained from utilizing recorded ground motions for short-period bridges, while the opposite is the case for long-period bridges. It is recommended that validation efforts go beyond IM levels and also include comparisons of the relations between IMs and EDPs. Finally, site-specific relations are proposed that correlate the ratio between the two types of EDPs (simulation-based and conventional) with the hazard level, shallow site condition, and site basin depth.

The design practice of Box-Girder Seat-Type (BGST) bridges in the Western U.S. is continuously evolving based on the results of advanced modeling and analysis techniques. This is mainly to help engineers and researchers to better understand the behavior of BGST bridges during seismic excitations. Within this backdrop, this study fills the gaps in the current knowledge of assessing the combined effect of strong motion duration and spectral shape on the response of bridges using a comprehensive set of numerical simulations and statistical analyses. Three-dimensional finite element models of two real BGST bridges are analyzed using a large set of ground motions obtained from crustal sources and subduction sources. By means of Step-wise regression – and other statistical procedures – the sensitivity of bridge response parameters to various ground motion parameters including Arias Intensity (Ia), RotD50 spectral acceleration at the bridge's first natural period (Sa(T1)), Significant Duration (D5-95), mid-frequency (f), the derivative of the mid-frequency (f’) and time at 30% of cumulated Arias Intensity (tmid) are evaluated. Results indicate that in the case of ground motions arising from shallow crustal sources, Ia and Sa(T1) are the best predictors of the bridge response, and strong motion duration (D5-95) has no statistically meaningful impact on the response of bridges. However, it is observed that the D5-95 of the ground motions ascending from the subduction sources highly affects the bridge response; utilizing D5-95 alongside Sa(T1), or Ia, can significantly increase the accuracy of bridge response estimates. Hence, it is concluded that D5-95 is not an important ground motion intensity measure for ground motion selection for bridges located in areas with crustal earthquakes. In contrast, D5-95 is important in subduction zone ground motions and must be given proper consideration in the design and analysis of BGST bridges.

This study presents a critical assessment of the reliability of current base plate connections in steel special moment resisting frames (SMRFs). Using a probabilistic outlook, this research evaluates the reliability of exposed column base (ECB) connections in SMRFs designed based on the current seismic design provisions; it suggests (and implements) a statistical approach to compute resistance factors for three modes of failure (concrete bearing, base plate yielding at tensile interface, and anchor bolt fracture) of ECB connections to achieve a target reliability index, β, of 4.5. Since ECB connections are limited to short buildings, therefore, this study is conducted on two-story and four-story SMRFs which are analyzed using a suite of 120 ground motions originating from strike-slip and reverse faults. ECB connections for the two-story building are designed to simulate pinned connection, while the bases of the four-story building represent moment connections. Detailed methodology for calculating the β of ECB connections is presented considering the three limit states in a moment–axial load interaction curve. Results indicate that the implementation of current seismic provisions results in β ~ 3.3 for non-moment resisting ECB connections for all tried combinations of resistance factors. For moment resisting ECB connections, however, only the designs based on a resistance factor for concrete bearing failure mode less than the current 0.65 result in an acceptable reliability factor of β > 4.5.

Various earthquake early warning (EEW) methodologies have been proposed globally for speedily estimating information (i.e., location, magnitude, ground‐shaking intensities, and/or potential consequences) about ongoing seismic events for real‐time/near real‐time earthquake risk management. Conventional EEW algorithms have often been based on the inferred physics of a fault rupture combined with simplified empirical models to estimate the source parameters and intensity measures of interest. Given the recent boost in computational resources, data‐driven methods/models are now widely accepted as effective alternatives for EEW. This study introduces a highly accurate deep‐learning‐based computational framework named ROSERS (i.e., Real‐time On‐Site Estimation of Response Spectra) to estimate the acceleration response spectrum (Sa(T)) of the expected on‐site ground‐motion waveforms using early non‐damage‐causing early p‐waves and site characteristics. The framework is trained using a carefully selected extensive database of recorded ground motions. Due to the well‐known correlation of Sa(T) with structures' seismic response and resulting damage/losses, rapid and accurate knowledge of expected on‐site Sa(T) values is highly beneficial to various end‐users to make well‐informed real‐time and near‐real‐time decisions. The framework is thoroughly assessed and investigated through multiple statistical tests under three historical earthquake events. These analyses demonstrate that the overall framework leads to excellent prediction power and, on average, has an accuracy above 85% for hazard‐consistent early‐warning trigger classification. [ABSTRACT FROM AUTHOR]

Modern seismic hazard analysis utilizes various ground motion prediction equations (GMPEs) to estimate intensity measures (IMs) such as spectral acceleration (Sa), Arias intensity (Ia), significant duration (D5‐95), and cumulative absolute velocity (CAV) as scalar parameters. The use of these GMPEs leads to independent estimations of IMs for the same earthquake scenario. Since the IMs belonging to the same earthquake scenario are naturally correlated, the GMPEs used for characterization of seismic hazard are expected to incorporate such correlation structure. Particularly, Sa corresponding to different periods of a single‐degree‐of‐freedom oscillator is estimated by GMPEs that do not explicitly account for internal correlations between the Sa at various periods for the same ground motion. The current approach to incorporate such a correlation for Sa spectrum is through a post‐processing technique developed by Baker and Jayaram; their method relates the spectral accelerations of different periods using functional forms describing linear correlations. However, the proposed correlation functions can be further improved and extended to more accurately estimate the spectrum of near‐fault high‐magnitude ground motions using high order dependencies among the spectral accelerations. This study proposes a generalized ground motion prediction model (GGMPM) using a hybrid recurrent neural network (RNN) framework that can be used to estimate a 29 × 1 correlated vector (denoted as IM) of RotD50 Sa at 26 periods and geometric means of Ia, D5‐95, and CAV using a set of seismic source and site parameters as inputs. This is an improvement to the current body of knowledge because the high order dependencies between the individual components of IM are incorporated. A RNN framework is developed that estimates the median vector of IM. The discrepancy between the IM estimated using the RNN framework and the IM computed from recorded motions is further minimized using the covariance matrix adaptation evolution strategy (CMA‐ES), which is a non‐convex optimization method. The residuals of the RNN framework are used to construct the inter‐event and the intra‐event covariance matrices to account for the inter‐event and intra‐event variabilities of the ground motions. Hence, given the source and site parameters, the RNN‐framework returns a median prediction of the IM, which is then combined with estimated inter‐event and intra‐event covariance matrices to obtain the probabilistic estimation of IM. Furthermore, this GGMPM framework is compared against various currently used GMPEs, and the results of these comparisons demonstrate that the proposed GGMPM leads to improved predictions while maintaining the internal dependencies of the IM components. [ABSTRACT FROM AUTHOR]