Validation of hazard-compatible stochastic ground motion model modification techniques

An important consideration for the adoption of stochastic ground motion models in performance-based earthquake engineering applications is that the probability distribution of target intensity measures from the developed suites of time-histories is compatible with the prescribed hazard at the site and structure of interest. The authors have recently developed a computationally efficient framework to modify existing stochastic ground motion models to facilitate such a compatibility. For a given seismicity scenario, the framework identifies the modified stochastic ground motion model that can sufficiently match the prescribed hazard while maintaining similarity to regional physical ground motion model characteristics. This paper extends this effort through a validation study. Suites of recorded and stochastic ground motions, whose spectral acceleration statistics match the mean and variance of target spectra within a period range of interest, are utilized as input to perform response history analysis of inelastic single-degree-of-freedom case-study systems. The resultant engineering demand parameters distributions are then compared to assess the effect of the proposed modification

Modification of stochastic ground motion models for matching target intensity measures

Stochastic ground motion models produce synthetic time‐histories by modulating a white noise sequence through functions that address spectral and temporal properties of the excitation. The resultant ground motions can be then used in simulation‐based seismic risk assessment applications. This is established by relating the parameters of the aforementioned functions to earthquake and site characteristics through predictive relationships. An important concern related to the use of these models is the fact that through current approaches in selecting these predictive relationships, compatibility to the seismic hazard is not guaranteed. This work offers a computationally efficient framework for the modification of stochastic ground motion models to match target intensity measures (IMs) for a specific site and structure of interest. This is set as an optimization problem with a dual objective. The first objective minimizes the discrepancy between the target IMs and the predictions established through the stochastic ground motion model for a chosen earthquake scenario. The second objective constraints the deviation from the model characteristics suggested by existing predictive relationships, guaranteeing that the resultant ground motions not only match the target IMs but are also compatible with regional trends. A framework leveraging kriging surrogate modeling is formulated for performing the resultant multi‐objective optimization, and different computational aspects related to this optimization are discussed in detail. The illustrative implementation shows that the proposed framework can provide ground motions with high compatibility to target IMs with small only deviation from existing predictive relationships and discusses approaches for selecting a final compromise between these two competing objectives

Hazard-compatible modification of stochastic ground motion models

A computationally efficient framework is presented for modification of stochastic ground motion models to establish compatibility with the seismic hazard for specific seismicity scenarios and a given structure/site. The modification pertains to the probabilistic predictive models that relate the parameters of the ground motion model to seismicity/site characteristics. These predictive models are defined through a mean prediction and an associated variance, and both these properties are modified in the proposed framework. For a given seismicity scenario, defined for example by the moment magnitude and source-to-site distance, the conditional hazard is described through the mean and the dispersion of some structure-specific intensity measure(s). Therefore, for both the predictive models and the seismic hazard, a probabilistic description is considered, extending previous work of the authors that had examined description only through mean value characteristics. The proposed modification is defined as a bi-objective optimization. The first objective corresponds to comparison for a chosen seismicity scenario between the target hazard and the predictions established through the stochastic ground motion model. The second objective corresponds to comparison of the modified predictive relationships to the pre-existing ones that were developed considering regional data, and guarantees that the resultant ground motions will have features compatible with observed trends. The relative entropy is adopted to quantify both objectives, and a computational framework relying on kriging surrogate modeling is established for an efficient optimization. Computational discussions focus on the estimation of the various statistics of the stochastic ground motion model output needed for the entropy calculation

Shake table testing of a tuned mass damper inerter (Tmdi)-equipped structure and nonlinear dynamic modeling under harmonic excitations

This paper presents preliminary experimental results from a novel shaking table testing campaign investigating the dynamic response of a two-degree-of-freedom (2DOF) physical specimen with a grounded inerter under harmonic base excitation and contributes a nonlinear dynamic model capturing the behavior of the test specimen. The latter consists of a primary mass connected to the ground through a high damping rubber isolator (HDRI) and a secondary mass connected to the primary mass through a second HDRI. Further, a flywheel-based rack-and-pinion inerter prototype device is used to connect the secondary mass to the ground. The resulting specimen resembles the tuned mass damper inerter (TMDI) configuration with grounded inerter analytically defined and numerically assessed by the authors in a number of previous publications. Physical specimens with three different inerter coefficients are tested on the shake table under sine-sweep excitation with three different amplitudes. Experimental frequency response functions (FRFs) are derived manifesting a softening nonlinear behavior of the specimens and enhanced vibration suppression with increased inerter coefficient. Further, a 2DOF parametric nonlinear model of the specimen is established accounting for non-ideal inerter device behavior and its potential to characterize experimental response time-histories, FRFs, and force-displacement relationships of the HDRIs and of the inerter is verified

A multiscale method for optimising surface topography in elastohydrodynamic lubrication (EHL) using metamodels

The frictional performance of a bearing is of significant interest in any mechanical system where there are lubricated surfaces under load and in relative motion. Surface topography plays a major role in determining the coefficient of friction for the bearing because the size of the fluid film and topography are of a comparable order. The problem of optimising topography for such a system is complicated by the separation in scales between the size of the lubricated domain and that of the topography, which is of at least one order of magnitude or more smaller. This paper introduces a multiscale method for optimising the small scale topography for improved frictional performance of the large scale bearing. The approach fully couples the elastohydrodynamic lubrication at both scales between pressure generated in the lubricant and deformation of the bounding surfaces. Homogenised small scale data is used to inform the large scale model and is represented using Moving Least Squares metamodels calibrated by cross validation. An optimal topography for a minimum coefficient of friction for the bearing is identified and comparisons made of local minima in the response, where very different topographies with similar frictional performance are observed. Comparisons of the optimal topography with the smooth surface model demonstrated the complexity of capturing the non-linear effect of topography and the necessity of the multiscale method in capturing this. Deviations from the smooth surface model were quantified by the metamodel coefficients and showed how topographies with a similar frictional performance have very different characteristics

Markov chain Monte Carlo with Gaussian processes for fast parameter estimation and uncertainty quantification in a 1D fluid‐dynamics model of the pulmonary circulation

The past few decades have witnessed an explosive synergy between physics and the life sciences. In particular, physical modelling in medicine and physiology is a topical research area. The present work focuses on parameter inference and uncertainty quantification in a 1D fluid‐dynamics model for quantitative physiology: the pulmonary blood circulation. The practical challenge is the estimation of the patient‐specific biophysical model parameters, which cannot be measured directly. In principle this can be achieved based on a comparison between measured and predicted data. However, predicting data requires solving a system of partial differential equations (PDEs), which usually have no closed‐form solution, and repeated numerical integrations as part of an adaptive estimation procedure are computationally expensive. In the present article, we demonstrate how fast parameter estimation combined with sound uncertainty quantification can be achieved by a combination of statistical emulation and Markov chain Monte Carlo (MCMC) sampling. We compare a range of state‐of‐the‐art MCMC algorithms and emulation strategies, and assess their performance in terms of their accuracy and computational efficiency. The long‐term goal is to develop a method for reliable disease prognostication in real time, and our work is an important step towards an automatic clinical decision support system

Validation of stochastic ground motion model modification by comparison to seismic demand of recorded ground motions

An important consideration for the adoption of stochastic ground motion models in performance-based earthquake engineering applications is that the probability distribution of target intensity measures from the developed suites of time-histories is compatible with the prescribed hazard at the site and structure of interest. The authors have recently developed a computationally efficient framework to modify existing stochastic ground motion models to facilitate such a compatibility. This paper extends this effort through a validation study by comparing the seismic demand of recorded ground motions to the demand of stochastic ground motion models established through the proposed modification. Suites of recorded and stochastic ground motions, whose spectral acceleration statistics match the mean and variance of target spectra within a period range of interest, are utilized as input to perform response history analysis of inelastic single-degree-of-freedom (SDoF) case-study systems. SDoF systems with peak-oriented hysteretic behavior, strain hardening, and (potentially) degrading characteristics, experiencing different degree of inelastic response, are considered. Response is evaluated using the peak inelastic displacement and the hysteretic energy given by the work of the SDoF restoring force as engineering demand parameters (EDPs). The resultant EDP distributions are compared to assess the effect of (and validate) the proposed modification. It is shown that the proposed modification of stochastic ground motion models can provide results that are similar to these from recorded ground motion suites, improving any (in some cases large) discrepancies that exist for the initial, unmodified stochastic ground motion model