Ductility‐dependent intensity measure that accounts for ground‐motion spectral shape and duration

The calculated nonlinear structural responses of a building can vary greatly, even if recorded ground motions are scaled to the same spectral acceleration at a building's fundamental period. To reduce the variation in structural response at a particular ground‐motion intensity, this paper proposes an intensity measure (IM_comb) that accounts for the combined effects of spectral acceleration, ground‐motion duration, and response spectrum shape. The intensity measure includes a new measure of spectral shape that integrates the spectrum over a period range that depends on the structure's ductility. The new IM is efficient, sufficient, scalable, transparent, and versatile. These features make it suitable for evaluating the intensities of measured and simulated ground motions. The efficiency and sufficiency of the new IM is demonstrated for the following: (i) elastic‐perfectly plastic single‐degree‐of‐freedom (SDOF) oscillators with a variety of ductility demands and periods; (ii) ductile and brittle deteriorating SDOF systems with a variety of periods; and (iii) collapse analysis for 30 previously designed frames. The efficiency is attributable to the inclusion of duration and to the ductility dependence of the spectral shape measure. For each of these systems, the transparency of the intensity measure made it possible to identify the sensitivity of structural response to the various characteristics of the ground motion. Spectral shape affected all structures, but in particular, ductile structures. Duration only affected structures with cyclic deterioration. Copyright © 2015 John Wiley & Sons, Ltd.     

A simple ground‐motion prediction model for cumulative absolute velocity and model validation

Cumulative absolute velocity (CAV) is an important ground motion intensity measure used in seismic hazard analysis. Based on the Next Generation Attenuation strong motion database, a simple ground‐motion prediction equation is proposed for the geometric mean of as‐recorded horizontal components of CAVs using mixed regression analysis. The proposed model employs only four parameters and has a simple functional form. Validation tests are conducted to compare the proposed model with the recently developed Campbell–Bozorgnia (CB10) model using subsets of the strong motion database, as well as several recent earthquakes that are not used in developing the model. It is found that the predictive capability of the proposed model is comparable with the CB10 model, which employs a complex functional form and more parameters. The study also corroborates previous findings that CAV has higher predictability than other intensity measures such as the peak ground acceleration. The high predictability of CAV warrants the use of the proposed simple model as an alternative in seismic hazard analysis. Copyright © 2012 John Wiley & Sons, Ltd.     

Ground‐motion prediction by a non‐parametric approach

Recently, several new ground‐motion prediction equations (GMPEs) have been developed in the U.S.A. (the NGA project) and elsewhere. Unfortunately, the predictions obtained by using different models still differ considerably, although starting from the same database. In this paper, a non‐parametric approach, called the Conditional Average Estimator (CAE) method, has been used for ground‐motion prediction. The comparison between the CAE results and the predictions obtained by five NGA and one European model suggest that the model predictions depend substantially on the selection of the effective database and on the adopted functional form. Both decisions rely to some extent on judgement, and their influence is especially important at short distances from the source. The differences between the results obtained from the European and NGA databases seem to be of the same or even smaller magnitude than the differences observed between different NGA models, at least at short and moderate distances. Aftershocks in the database generally decrease the median values and increase dispersion. The non‐parametric CAE method has proved to be a simple but powerful tool for ground‐motion prediction, especially in a research environment. It can be used for quick predictions with different databases and different input parameters within the range of available data. It is easy to add to or remove data from the database, and to check the influence of additional input parameters. With availability of high quality data, the non‐parametric approach will become more reliable and more attractive also for practical applications. Copyright © 2010 John Wiley & Sons, Ltd.     

川滇地区地震动预测模型及其对2021年漾濞6.4级地震的适用性

随着川滇地区强震记录的不断增加,为了建立更符合该区域地震动特征的预测模型,文中基于该区域现有的地震动数据,通过随机效应回归模型建立适用于川滇地区的地震动预测模型;2021年5月21日,云南省大理州漾濞县发生6.4级地震,为了分析文中预测模型对漾濞地震的适用性,首先根据预测模型的适用范围选取合适的漾濞地震数据,计算真实记...     

Validation of (not‐historical) large‐event near‐fault ground‐motion simulations for use in civil engineering applications

Ground‐motion simulations generated from physics‐based wave propagation models are gaining increasing interest in the engineering community for their potential to inform the performance‐based design and assessment of infrastructure residing in active seismic areas. A key prerequisite before the ground‐motion simulations can be used with confidence for application in engineering domains is their comprehensive and rigorous investigation and validation. This article provides a four‐step methodology and acceptance criteria to assess the reliability of simulated ground motions of not historical events, which includes (1) the selection of a population of real records consistent with the simulated scenarios, (2) the comparison of the distribution of Intensity Measures (IMs) from the simulated records, real records, and Ground‐Motion Prediction Equations (GMPEs), (3) the comparison of the distribution of simple proxies for building response, and (4) the comparison of the distribution of Engineering Demand Parameters (EDPs) for a realistic model of a structure. Specific focus is laid on near‐field ground motions (<10km) from large earthquakes (M_w7), for which the database of real records for potential use in engineering applications is severely limited. The methodology is demonstrated through comparison of (2490) near‐field synthetic records with 5 Hz resolution generated from the Pitarka et al (2019) kinematic rupture model with a population of (38) pulse‐like near‐field real records from multiple events and, when applicable, with NGA‐W2 GMPEs. The proposed procedure provides an effective method for informing and advancing the science needed to generate realistic ground‐motion simulations, and for building confidence in their use in engineering domains.     

Information theory measures for the engineering validation of ground‐motion simulations

This short communication introduces a quantitative approach for the engineering validation of ground‐motion simulations based on information theory concepts and statistical hypothesis testing. Specifically, we use the Kullback‐Leibler divergence to measure the similarity of the probability distributions of recorded and simulated ground‐motion intensity measures (IMs). We demonstrate the application of the proposed validation approach to ground‐motion simulations computed by using a variety of methods, including Graves and Pitarka hybrid broadband, the deterministic composite source model, and a stochastic white noise finite‐fault model. Ground‐motion IMs, acting as proxies for the (nonlinear) seismic response of more complex engineered systems, are considered herein to validate the considered ground‐motion simulation methods. The list of considered IMs includes both spectral‐shape and duration‐related proxies, shown to be the optimal IMs in several probabilistic seismic demand models of different structural types, within the framework of performance‐based earthquake engineering. The proposed validation exercise (1) can highlight the similarities and differences between simulated and recorded ground motions for a given simulation method and/or (2) allow the ranking of the performance of alternative simulation methods. The similarities between records and simulations should provide confidence in using the simulation method for engineering applications, while the discrepancies should help in improving the tested method for the generation of synthetic records.     

Markov chain Monte Carlo ground‐motion selection algorithms for conditional intensity measure targets

Two new algorithms are presented for efficiently selecting suites of ground motions that match a target multivariate distribution or conditional intensity measure target. The first algorithm is a Markov chain Monte Carlo (MCMC) approach in which records are sequentially added to a selected set such that the joint probability density function (PDF) of the target distribution is progressively approximated by the discrete distribution of the selected records. The second algorithm derives from the concept of the acceptance ratio within MCMC but does not involve any sampling. The first method takes advantage of MCMC's ability to efficiently explore a sampling distribution through the implementation of a traditional MCMC algorithm. This method is shown to enable very good matches to multivariate targets to be obtained when the numbers of records to be selected is relatively large. A weaker performance for fewer records can be circumvented by the second method that uses greedy optimisation to impose additional constraints upon properties of the target distribution. A preselection approach based upon values of the multivariate PDF is proposed that enables near‐optimal record sets to be identified with a very close match to the target. Both methods are applied for a number response analyses associated with different sizes of record sets and rupture scenarios. Comparisons are made throughout with the Generalised Conditional Intensity Measure (GCIM) approach. The first method provides similar results to GCIM but with slightly worse performance for small record sets, while the second method outperforms method 1 and GCIM for all considered cases.     

A generalized conditional intensity measure approach and holistic ground‐motion selection

A generalized conditional intensity measure (GCIM) approach is proposed for use in the holistic selection of ground motions for any form of seismic response analysis. The essence of the method is the construction of the multivariate distribution of any set of ground‐motion intensity measures conditioned on the occurrence of a specific ground‐motion intensity measure (commonly obtained from probabilistic seismic hazard analysis). The approach therefore allows any number of ground‐motion intensity measures identified as important in a particular seismic response problem to be considered. A holistic method of ground‐motion selection is also proposed based on the statistical comparison, for each intensity measure, of the empirical distribution of the ground‐motion suite with the ‘target’ GCIM distribution. A simple procedure to estimate the magnitude of potential bias in the results of seismic response analyses when the ground‐motion suite does not conform to the GCIM distribution is also demonstrated. The combination of these three features of the approach make it entirely holistic in that: any level of complexity in ground‐motion selection for any seismic response analysis can be exercised; users explicitly understand the simplifications made in the selected suite of ground motions; and an approximate estimate of any bias associated with such simplifications is obtained. Copyright © 2010 John Wiley & Sons, Ltd.     

Development of regional simulation of seismic ground‐motion and induced liquefaction enhanced by GPU computing

Much research has been conducted for physics‐based ground‐motion simulation to reproduce seismic response of soil and structures precisely and to mitigate damages caused by earthquakes. We aimed at enabling physics‐based ground‐motion simulations of complex three‐dimensional (3D) models with multiple materials, such as a digital twin (high‐fidelity 3D model of the physical world that is constructed in cyberspace). To perform one case of such simulation requires high computational cost and it is necessary to perform a number of simulations for the estimation of parameters or consideration of the uncertainty of underground soil structure data. To overcome this problem, we proposed a fast simulation method using graphics processing unit computing that enables a simulation with small computational resources. We developed a finite‐element‐based method for large‐scale 3D seismic response analysis with small programming effort and high maintainability by using OpenACC, a directive‐based parallel programming model. A lower precision variable format was introduced to achieve further speeding up of the simulation. For an example usage of the developed method, we applied the developed method to soil liquefaction analysis and conducted two sets of simulations that compared the effect of countermeasures against soil liquefaction: grid‐form ground improvement to strengthen the earthquake resistance of existing houses and replacement of liquefiable backfill soil of river wharves for seismic reinforcement of the wharf structure. The developed method accelerates the simulation and enables us to quantitatively estimate the effect of countermeasures using the high‐fidelity 3D soil‐structure models on a small cluster of computers.     

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

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 (‘generalized conditional intensity measure‐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. Copyright © 2015 John Wiley & Sons, Ltd.     

Conditional spectrum‐based ground motion selection. Part I: Hazard consistency for risk‐based assessments

The conditional spectrum (CS, with mean and variability) is a target response spectrum that links nonlinear dynamic analysis back to probabilistic seismic hazard analysis for ground motion selection. The CS is computed on the basis of a specified conditioning period, whereas structures under consideration may be sensitive to response spectral amplitudes at multiple periods of excitation. Questions remain regarding the appropriate choice of conditioning period when utilizing the CS as the target spectrum. This paper focuses on risk‐based assessments, which estimate the annual rate of exceeding a specified structural response amplitude. Seismic hazard analysis, ground motion selection, and nonlinear dynamic analysis are performed, using the conditional spectra with varying conditioning periods, to assess the performance of a 20‐story reinforced concrete frame structure. It is shown here that risk‐based assessments are relatively insensitive to the choice of conditioning period when the ground motions are carefully selected to ensure hazard consistency. This observed insensitivity to the conditioning period comes from the fact that, when CS‐based ground motion selection is used, the distributions of response spectra of the selected ground motions are consistent with the site ground motion hazard curves at all relevant periods; this consistency with the site hazard curves is independent of the conditioning period. The importance of an exact CS (which incorporates multiple causal earthquakes and ground motion prediction models) to achieve the appropriate spectral variability at periods away from the conditioning period is also highlighted. The findings of this paper are expected theoretically but have not been empirically demonstrated previously. Copyright © 2013 John Wiley & Sons, Ltd.     

Skyline‐based ground motion selection method for nonlinear time history analysis of building structures

This paper develops a novel ground motion selection procedure for nonlinear time history analysis of critical structures. The skyline query originated from computer science is first introduced, including its concept and related algorithms. Then, the ground motion selection procedure based on skyline query is developed. Meanwhile, a new five‐dimensional vector‐valued intensity measure is defined as a critical ingredient of the selection procedure to measure the damage potential of ground motions. Finally, the process of the selection procedure is illustrated through examples of three shear models, and its efficiency is also validated. Through the examples of three shear models, the ground motion selection procedure based on skyline query proposed in this paper is proven to be capable of selecting a limited set of ground motions with high damage potentials for the nonlinear time history analysis purpose. Copyright © 2012 John Wiley & Sons, Ltd.     

The effect of causal parameter bounds in PSHA‐based ground motion selection

In this paper the effect of causal parameter bounds (e.g. magnitude, source‐to‐site distance, and site condition) on ground motion selection, based on probabilistic seismic hazard analysis (PSHA) results, is investigated. Despite the prevalent application of causal parameter bounds in ground motion selection, present literature on the topic is cast in the context of a scenario earthquake of interest, and thus specific bounds for use in ground motion selection based on PSHA, and the implications of such bounds, is yet to be examined. Thirty‐six PSHA cases, which cover a wide range of causal rupture deaggregation distributions and site conditions, are considered to empirically investigate the effects of various causal parameter bounds on the characteristics of selected ground motions based on the generalized conditional intensity measure (GCIM) approach. It is demonstrated that the application of relatively ‘wide’ bounds on causal parameters effectively removes ground motions with drastically different characteristics with respect to the target seismic hazard and results in an improved representation of the target causal parameters. In contrast, the use of excessively ‘narrow’ bounds can lead to ground motion ensembles with a poor representation of the target intensity measure distributions, typically as a result of an insufficient number of prospective ground motions. Quantitative criteria for specifying bounds for general PSHA cases are provided, which are expected to be sufficient in the majority of problems encountered in ground motion selection for seismic demand analyses. Copyright © 2016 John Wiley & Sons, Ltd.     

Seismic response analysis using characteristic ground motion records for risk‐based decision‐making (3R method)

The concept of intensity‐based assessment for risk‐based decision‐making is introduced. It is realized by means of the so‐called 3R method (response analysis, record selection and risk‐based decision‐making), which can be used to check the adequacy of design of a new building or of the strengthening of an existing building by performing conventional pushover analysis and dynamic analysis for only a few ground motions, which are termed characteristic ground motions. Because the objective of the method is not a precise assessment of the seismic risk, a simple decision model for risk acceptability can be introduced. The engineer can decide that the reliability of a no‐collapse requirement is sufficient when collapse is observed in the case of less than half of, for example, seven characteristic ground motions. From the theoretical point of view, it is shown that the accuracy of the method is acceptable if the non‐linear response history analyses are performed at a low percentile of limit‐state intensity, which is also proven by means of several examples of multi‐storey reinforced concrete frame buildings. The 3R method represents a compromise between the exclusive use of either pushover analysis or dynamic analysis and can be easily introduced into building codes provided that its applicability is further investigated (e.g. asymmetric structures and other performance objectives) and that the procedure for the selection of characteristic ground motions is automated and readily available to engineers (www.smartengineering.si).     

Conditional spectrum‐based ground motion selection. Part II: Intensity‐based assessments and evaluation of alternative target spectra

In a companion paper, an overview and problem definition was presented for ground motion selection on the basis of the conditional spectrum (CS), to perform risk‐based assessments (which estimate the annual rate of exceeding a specified structural response amplitude) for a 20‐story reinforced concrete frame structure. Here, the methodology is repeated for intensity‐based assessments (which estimate structural response for ground motions with a specified intensity level) to determine the effect of conditioning period. Additionally, intensity‐based and risk‐based assessments are evaluated for two other possible target spectra, specifically the uniform hazard spectrum (UHS) and the conditional mean spectrum (CMS, without variability).It is demonstrated for the structure considered that the choice of conditioning period in the CS can substantially impact structural response estimates in an intensity‐based assessment. When used for intensity‐based assessments, the UHS typically results in equal or higher median estimates of structural response than the CS; the CMS results in similar median estimates of structural response compared with the CS but exhibits lower dispersion because of the omission of variability. The choice of target spectrum is then evaluated for risk‐based assessments, showing that the UHS results in overestimation of structural response hazard, whereas the CMS results in underestimation. Additional analyses are completed for other structures to confirm the generality of the conclusions here. These findings have potentially important implications both for the intensity‐based seismic assessments using the CS in future building codes and the risk‐based seismic assessments typically used in performance‐based earthquake engineering applications. Copyright © 2013 John Wiley & Sons, Ltd.     

Semi‐empirical model for site effects on acceleration time histories at soft‐soil sites. Part 1: formulation and development

A criterion is developed for the simulation of realistic artificial ground motion histories at soft‐soil sites, corresponding to a detailed ground motion record at a reference firm‐ground site. A complex transfer function is defined as the Fourier transform of the ground acceleration time history at the soft‐soil site divided by the Fourier transform of the acceleration record at the firm‐ground site. Working with both the real and the imaginary components of the transfer function, and not only with its modulus, serves to keep the statistical information about the wave phases (and, therefore, about the time variation of amplitudes and frequencies) in the algorithm used to generate the artificial records. Samples of these transfer functions, associated with a given pair of soft‐soil and firm‐ground sites, are empirically determined from the corresponding pairs of simultaneous records. Each function included in a sample is represented as the superposition of the transfer functions of the responses of a number of oscillators. This formulation is intended to account for the contributions of trains of waves following different patterns in the vicinity of both sites. The properties of the oscillators play the role of parameters of the transfer functions. They vary from one seismic event to another. Part of the variation is systematic, and can be explained in terms of the influence of ground motion intensity on the effective values of stiffness and damping of the artificial oscillators. Another part has random nature; it reflects the random characteristics of the wave propagation patterns associated with the different events. The semi‐empirical model proposed recognizes both types of variation. The influence of intensity is estimated by means of a conventional one‐dimensional shear wave propagation model. This model is used to derive an intensity‐dependent modification of the values of the empirically determined model parameters in those cases when the firm‐ground earthquake intensity used to determine these parameters differs from that corresponding to the seismic event for which the simulated records are to be obtained. Copyright © 2004 John Wiley & Sons, Ltd.     

Hybrid platform for vibration control of high‐tech equipment in buildings subject to ground motion. Part 2: analysis

The experimental results of using a hybrid platform to mitigate vibration of a batch of high‐tech equipment installed in a building subject to nearby traffic‐induced ground motion have been presented and discussed in the companion paper. Based on the identified dynamic properties of both the building and the platform, this paper first establishes an analytical model for hybrid control of the building‐platform system subject to ground motion in terms of the absolute co‐ordinate to facilitate the absolute velocity feedback control strategy used in the experiment. The traffic‐induced ground motion used in the experiment is then employed as input to the analytical model to compute the dynamic response of the building‐platform system. The computed results are compared with the measured results, and the comparison is found to be satisfactory. Based on the verified analytical model, coupling effects between the building and platform are then investigated. A parametric study is finally conducted to further assess the performance of both passive and hybrid platforms at microvibration level. The analytical study shows that the dynamic interaction between the building and platform should be taken into consideration. The hybrid control is effective in reducing both velocity response and drift of the platform/high‐tech equipment at microvibration level with reasonable control force. Copyright © 2003 John Wiley & Sons, Ltd.     

Evaluation of ASCE 7 equations for designing acceleration‐sensitive nonstructural components using data from instrumented buildings

This study uses instrumented buildings and models of code‐based designed buildings to validate the results of previous studies that highlighted the need to revise the ASCE 7 F_p equation for designing nonstructural components (NSCs) through utilizing oversimplified linear and nonlinear models. The evaluation of floor response spectra of a large number of instrumented buildings illustrates that, unlike the ASCE 7 approach, the in‐structure and the component amplification factors are a function of the ratio of NSC period to the supporting building modal periods, the ground motion intensity, and the NSC location. It is also shown that the recorded ground motions at the base of instrumented buildings in most cases are significantly lower than design earthquake (DE) ground motions. Because ASCE 7 is meant to provide demands at a DE level, for a more reliable evaluation of the F_p equation, 2 representative archetype buildings are designed based on the ASCE 7‐16 seismic provisions and exposed to various ground motion intensity levels (including those consistent with the ones experienced by instrumented buildings and the DE). Simulation results of the archetype buildings, consistent with previous numerical studies, illustrate the tendency of the ASCE 7 in‐structure amplification factor, [1 + 2(z/h)] , to significantly overestimate demands at all floor levels and the ASCE 7 limit of to in many cases underestimate the calculated NSC amplification factors. Furthermore, the product of these 2 amplification factors (that represents the normalized peak NSC acceleration) in some cases exceeds the ASCE 7 equation by a factor up to 1.50.     

3‐D uncertainty‐based topographic change detection with structure‐from‐motion photogrammetry: precision maps for ground control and directly georeferenced surveys

Structure‐from‐motion (SfM) photogrammetry is revolutionising the collection of detailed topographic data, but insight into geomorphological processes is currently restricted by our limited understanding of SfM survey uncertainties. Here, we present an approach that, for the first time, specifically accounts for the spatially variable precision inherent to photo‐based surveys, and enables confidence‐bounded quantification of 3D topographic change. The method uses novel 3D precision maps that describe the 3D photogrammetric and georeferencing uncertainty, and determines change through an adapted state‐of‐the‐art fully 3D point‐cloud comparison (M3C2), which is particularly valuable for complex topography. We introduce this method by: (1) using simulated UAV surveys, processed in photogrammetric software, to illustrate the spatial variability of precision and the relative influences of photogrammetric (e.g. image network geometry, tie point quality) and georeferencing (e.g. control measurement) considerations; (2) we then present a new Monte Carlo procedure for deriving this information using standard SfM software and integrate it into confidence‐bounded change detection; before (3) demonstrating geomorphological application in which we use benchmark TLS data for validation and then estimate sediment budgets through differencing annual SfM surveys of an eroding badland. We show how 3D precision maps enable more probable erosion patterns to be identified than existing analyses, and how a similar overall survey precision could have been achieved with direct survey georeferencing for camera position data with precision half as good as the GCPs'. Where precision is limited by weak georeferencing (e.g. camera positions with multi‐metre precision, such as from a consumer UAV), then overall survey precision can scale as n^‐½ of the control precision (n = number of images). Our method also provides variance–covariance information for all parameters. Thus, we now open the door for SfM practitioners to use the comprehensive analyses that have underpinned rigorous photogrammetric approaches over the last half‐century. Copyright © 2017 John Wiley & Sons, Ltd.