Ground Motion Selection Research Articles

Seismic Response of Open Ground Story Reinforced Concrete Buildings, Considering with Soil-Structure Interaction

This study investigates the seismic behavior of open-ground story (OGS) reinforced concrete buildings with soil-structure interaction (SSI) using SAP2000. The study mainly focused on mid-rise (8-storey) and low-rise (4-storey) OGS buildings, incorporating different masonry infill materials such as brick, concrete blocks, and autoclaved aerated concrete (AAC) blocks. Selected ground motions from the Bhuj, El Centro, and Chi-Chi earthquakes were utilized to simulate varying seismic intensities and frequency content. Critical parameters such as top-story displacement and inter-story drift are analyzed to assess the structural safety and serviceability of RC buildings. The results demonstrate that both OGS coupled with SSI and masonry infill type significantly affect the displacement and drift, with mid-rise buildings showing more pronounced impacts. Furthermore, ground motion characteristics are critical in producing different response patterns. The study highlights the importance of considering SSI, infill materials, and ground motion characteristics in the design of OGS buildings to enhance earthquake resilience and reduce seismic damage.

Structural failure analysis with CMS-based ground motion selection using innovative cost function and weight factors

Structural failure analysis with CMS-based ground motion selection using innovative cost function and weight factors

Amplification factors of intensity measures for site-specific PSHA using spectrally equivalent pulse-like and non-pulse-like ground motion records

Proper representations of ground motion characteristics and prediction of rock-to-ground amplification factors (AFs) of intensity measures (IMs) are crucial in site-specific probabilistic seismic hazard analysis (PSHA). By using spectrally equivalent pulse-like and non-pulse-like ground motion suites within the convolution-based PSHA framework, this paper aims to (i) investigate the amplification patterns and AF prediction efficiency for 16 typical IMs; and (ii) evaluate the sensitivities of AF and PSHA to the pulse-characteristic, rock-IM type, and AF functional form. The results reveal that IMs are generally amplified, whereas de-amplification occurs at soft sites for IMs largely dominated by high-frequency content (e.g., Arias intensity). Specific rock-IMs for predicting AFs are recommended through efficiency criterion. The results for pulse-like and non-pulse-like suites are found to be not significantly different; hence, including pulse-like records is generally not essential in convolution-based PSHA as long as the bedrock spectra designed with pulse-type features have been matched. Meanwhile, the similar AF predictions from the linear and nonlinear models indicate that the choice of functional form is of secondary importance. Instead, the selection of rock-IM is of higher importance, especially for soft sites and high-frequency dominated IMs. This study could provide useful insights into ground motion selection and AF prediction for site-specific PSHA.

A vector‐valued ground motion intensity measure for base‐isolated buildings in far‐field regions

AbstractIn this study, the effects of the spectral acceleration at the superstructure first‐mode period on the isolator displacement are investigated for far‐field ground motions. For this purpose, two different base‐isolated models are subjected to 165 far‐field ground motions. It is demonstrated that considering the spectral acceleration at the superstructure first‐mode period, besides that at the effective period, improves the estimation accuracy of isolator displacement. ASCE 7‐22 modifies the scaling period range to consider the superstructure first mode period and proposes the new period range from the superstructure first‐mode period to the 1.25 times effective period. In the ASCE 7‐22, the same weight factor is used for the whole period range. However, the present study shows that adding the superstructure first‐mode related period range with appropriate weight factor to the effective period‐based scaling range decreases the dispersion of isolator displacement in the nonlinear response history analyses (NRH). Then, to overcome the spectral shape effects on the fragility curves, a vector‐valued intensity measure parameter is obtained by combining spectral acceleration at the effective period and reduced spectral acceleration at the superstructure first‐mode period. The optimum contribution factor for the spectral acceleration at the superstructure first‐mode period is defined as the ratio of the superstructure first‐mode period to the effective period. The article shows that the proposed parameter is efficient and sufficient to be used as an intensity measure for far‐field ground motions. Furthermore, regression analysis results indicate that this vector‐valued intensity measure parameter correlates well with the isolator displacement. Further, the article shows that using the proposed IM parameter in the fragility curves makes the collapse margin ratio of these curves less sensitive to the spectral shape of the selected ground motions.

Application of Metaheuristic Algorithms in Ground Motion Selection and Scaling for Time History Analysis of Structures

Application of Metaheuristic Algorithms in Ground Motion Selection and Scaling for Time History Analysis of Structures

Amplitude and duration hazard-consistent ground-motion selection for seismic risk assessment in Mexico City

The emphasis of seismic design regulations on applying nonlinear dynamic analyses (NDAs) promotes using accelerograms that characterize site-specific ground motions. Commonly, amplitude levels of such accelerograms are defined by a target spectrum that could be based on a uniform hazard spectrum (UHS), which is determined by a probabilistic seismic hazard analysis (PSHA) and represents a response spectrum with ordinates having an equal probability of being exceeded within a given return period, Tr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${T}_{r}$$\\end{document}. Conversely, the definition of ground-motion duration levels is not yet properly defined in current regulations to select accelerograms. Thus, adhering to data handling as that for amplitude ground-motion parameters, this study motivates executing PSHAs to define hazard-consistent levels for the ground-motion duration. That is, accelerograms can be selected to match both amplitude and duration ground-motion levels associated with Tr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${T}_{r}$$\\end{document}. Further, fragility functions conditional on Tr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${T}_{r}$$\\end{document} that cover typical performance objectives can be developed using sets of hazard-consistent accelerograms to implement, e.g., multiple stripe analyses (MSAs). To demonstrate the importance of choosing fully hazard-consistent accelerograms to perform NDAs, this study includes the displacement- and energy-based seismic-response evaluation of a steel frame building located at different soil-profile sites in Mexico City. Sets of fully hazard-consistent accelerograms and solely amplitude-based hazard-consistent accelerograms were artificially generated per site for values of Tr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${T}_{r}$$\\end{document} up to 5000 years. Results indicate that the probability of failure can be underestimated if the ground-motion duration is unvaried in MSAs, e.g., structural damage caused by 50-year return-period or higher events can be more noticeable when fully hazard-consistent accelerograms take place.

Utilization of stochastic ground motion simulations for scenario-based performance assessment of geo-structures

Probabilistic seismic performance assessments of engineered structures can be highly sensitive to the seismic input excitation and its variability. In the present study, the scenario-based performance assessment recommended by Federal Emergency Management Agency (FEMA) P-58 guidelines is adopted to estimate seismic fragility of concrete dams for various seismic hazard scenarios. Due to the scarcity of recorded ground motions and thereby their poor representation of uncertainties, stochastic ground motion simulation methods are utilized to obtain the required input excitations. Moreover, to understand the uncertainty in ground motion simulation models, two broadband stochastic simulation models are used to generate input excitations representing six seismic hazard scenarios defined by earthquake magnitude, source-to-site distance, and soil conditions.Optimal intensity measure parameters for each scenario are identified using a systematic procedure that considers criteria such as efficiency, practicality, proficiency, sufficiency, and hazard compatibility. Fragility curves and surfaces are derived using the cloud analysis technique, taking into account various damage measures and limit state functions. The study finds that the derived fragility curves are particularly sensitive to the selection of earthquake scenarios, the choice of records, and the methods used to calculate fragility curves, with less sensitivity observed to different engineering demand parameters. Given this sensitivity, particularly to ground motion selection, the study highlights the necessity of incorporating both model-to-model variability (epistemic uncertainty) and record-to-record variability (aleatory uncertainty), alongside the established material and modeling uncertainties, in the probabilistic seismic assessment.

Testing the applicability of ground motion prediction equations for the Hainaut region (Belgium) using intensity data

In regions where strong earthquakes occurred before the deployment of dense seismic and accelerometric networks, intensity datasets can help select appropriate ground motion prediction equations (GMPEs) for seismic hazard studies. This is the case for the Hainaut seismic zone, which was one of the most seismically active zones in and around Belgium during the twentieth century. A recent reassessment of the intensity dataset of the area showed that intensities in this region attenuate much faster with distance than in other parts of northwestern Europe. Unfortunately, this characteristic has not yet been taken into account in current hazard maps for Belgium and northern France. Based on this dataset, we evaluate the goodness of fit of published GMPEs with intensities in Hainaut by means of a ground-motion-to-intensity conversion equation (GMICE) and according to different metrics (Likelihood, Log-likelihood and Euclidean-based Distance Ranking) published in literature. We also introduce a new measure to specifically evaluate the distance trend. Our results show that none of the tested GMPEs convincingly fits the intensity dataset, in particular the fast attenuation with distance. Nevertheless, applying the few GMPEs that show a reasonable fit in seismic hazard computations, we observe a decrease of the influence of the Hainaut seismicity on hazard maps for Belgium and northern France. This result is compatible with the earthquake intensity observations for the last 350 years in this part of Europe.

Seismic design-assisted-by-testing approach for racks with dissipative baseplates in the cross-aisle direction

A design-assisted-by-testing strategy is proposed for the upright frames of adjustable pallet racks. This methodology defines a procedure that aims to locate marked post-elastic behavior at the floor-to-upright connections of lateral resisting frames in the cross-aisle direction. Such connections are intended to enhance the performance of the cross-aisle frames when subjected to dynamic forces in their plane. The chosen baseplate connection is experimentally tested using monotonic and cyclic protocols at the Steel Structures Laboratory of the National Technical University of Athens (Greece). Then, a numerical model of the baseplate is calibrated using literature parameter definitions and hence validated using physical data. Additionally, the dynamic response of two upright frames is numerically investigated: one with traditional hinge connections and another with dissipative ones. This study indicates that the proposed procedure successfully enhances ductility, resulting in a significant reduction (up to 50%) in upright axial forces compared to the conventional configuration. Finally, the open-source code for ground motion selection, together with its database, is released.

Empirical correlations between ground motion intensity measures from the NGA‐West2 database and their use in vector generalized conditional intensity measures

AbstractThis paper examines the empirical correlations between 14 intensity measures (IMs) describing the frequency content, amplitude, cumulative effects, and duration aspects of ground motion based on the NGA‐West2 database. The correlation results in this paper are compared with the results of previous models based on the NGA‐West1 database, and the previous correlation coefficient models are updated and extended. The comparison results show that the trend of the correlation coefficients of the model established in this paper is essentially consistent with previous models based on the NGA‐West1 database, with most correlation coefficients observed in this paper being slightly lower than in previous studies. In addition, this paper extends the generalized conditional intensity measure (GCIM) ground motion selection method so that it can consider multiple conditional IMs (VGCIM), and gives full theoretical details. The differences between the theories of VGCIM and GCIM are discussed, and several possible application scenarios of VGCIM are illustrated. An example application of VGCIM is shown, and the results show that there are deviations between the target IMs conditional distribution constructed after considering multiple conditional IMs and considering one IM that is sufficient to make an impact in the ground motion selection. Finally, the effect of the correlation coefficient model on the IMs conditional distributions generated based on GCIM and VGCIM is discussed.

Implications of input ground-motion selection techniques on site response analyses for different tectonic settings

This study investigates how current practices of input ground-motion selection influence site response analysis results and their variability, when considering different tectonic settings. Study sites in Seattle and Boston are chosen to represent tectonic settings with contributions to the seismic hazard from shallow crustal and subduction events, as well as stable continental regions, respectively. Selected input ground-motion suites for one-dimensional site response analysis represent variations in the target spectrum definition, spectral period of interest, seismic source, and ground-motion database. When directly incorporating different types of seismic sources (e.g. shallow crustal versus subduction) into target spectrum definitions and selecting ground motions from the corresponding databases (i.e. consistent with such seismic sources), differences on the estimated site response and its variability are observed. These effects are captured by spectral amplification factors and nonspectral intensity measures (significant duration and Arias intensity) and become particularly apparent for subduction zones. The variability in spectral amplification factors stemming from ground-motion selection techniques is found to be also a function of the characteristics of the site, becoming higher near the fundamental period of the site. Estimated responses at stiffer sites are more significantly influenced by ground-motion selection techniques, whereas the onset of nonlinear soil behavior at softer sites can reduce such variability.

Optimal selection and scaling of ground motion records compatible with input energy and acceleration spectra

AbstractNonlinear response history analysis is the primary tool for risk‐targeted design and seismic performance evaluation of structures. These analyses require the selection of a set of ground motions that satisfy predetermined conditions such as spectral acceleration. Numerous efforts have been made so far to obtain ground motion records which are expected to represent possible earthquakes. Even though spectral acceleration‐based ground motion scaling is a common procedure, recent studies showed that structural response can be better represented through the energy content of the records. To this end, this study aims to develop an energy and acceleration spectra‐compatible record selection and scaling methodology to achieve higher efficiency and lower bias in the predicted structural response. The efficiency of the proposed method is evaluated through the standard deviations of the computed story drifts of benchmark structures resulting from the records processed by either the proposed or commonly used methods. The results demonstrated that considering input energy together with spectral acceleration for the selection and scaling of the records can considerably reduce the bias in structural response, especially for structures located on stiff soils.

Selection of the structural severest design ground motions based on big data and random forest

When calculating the time history of the seismic response, the conventional approach only processes a limited number of ground motions because of the elevated computational costs associated with nonlinear time history analysis and the lack of useable earthquake data. To solve this problem, a selection method for the severest design ground motions based on big data and Random Forest is proposed. Firstly, to address the issue of insufficient data, the Stanford Earthquake Dataset including over one million ground motions, was obtained and processed into the data format that can be used in earthquake engineering. Then, a structural response prediction algorithm based on Random Forest is suggested to substitute the nonlinear time history analysis, and compared to Support Vector Machine and Extreme Gradient Boosting. Finally, under all ground motions in the Stanford Earthquake Dataset, the proposed model is used to predict the structural response, include a reinforcement concrete isolated continuous girder bridge and a reinforcement concrete six-layer frame. The severest design ground motions for both structures were determined based on ranked calculated structural response. The conclusions of this study are as follows: The processed dataset provides a solution for implementing intelligent algorithms in the field of seismic engineering; Random Forest optimized through Bayesian optimization performs well in predicting structural demand measures, demonstrating superior generalization performance with R2 of 0.918 and root mean square error of 0.214, compared to other models. For actual civil structures, the proposed method can determine the structural severest ground motions at two different fortification standards.

Utility of Stochastic Ground Motion Models in Conditional Spectrum-Based Selection Consistent with the Causal Earthquakes

ABSTRACT Conditional Spectrum (CS), characterized by a conditional mean and conditional standard deviation, serves as a target spectrum linking seismic hazard information to ground motion selection for seismic demand analysis. Current Ground Motion Selection and Modification (GMSM) methods aim to align with this target spectrum but face challenges due to the limited availability of recorded ground motions and potential modifications, leading to difficulties in ensuring selected ground motions accurately reflect site characteristics. Addressing this issue, the study presents an algorithm to select hazard-consistent CS-based records utilizing a stochastic ground motion model with a dual objective: (1) alleviate the need for scaling during the selection process, and (2) select records consistent with the target hazard and the contributing causal scenarios. To achieve both objectives, the study generates a database of hazard-targeted ground motions utilizing the kriging surrogate with scenarios sampled from the disaggregation matrix. Then, a postprocessing step explicitly considers causative parameters in the selection process. The algorithm’s potential to select site-specific ground motions is demonstrated using sites in the Western United States, providing insights into the computational cost and accuracy. Additionally, statistical comparisons are conducted to explore circumstances where similar hazard consistency can be achieved without the postprocessing step, reducing the overall computational cost. Finally, the selected ground motions are favorably compared to those selected through traditional conditional spectrum-based approaches based on their spectral shape and ground motion intensity measures.

Compatible Ground Motion Models for South Korea Using Moderate Earthquakes

Due to a heightened interest in the field of earthquakes after two moderately sized earthquakes occurred in Gyeongju and Pohang, this study explores which ground motion prediction equations are compatible for the South Korea region. Due to data availability, ground motions from five earthquakes of moderate magnitude were used for comparing against selected ground motion models. Median rotated response spectral ordinates at a period of 0.2 s were extracted from these ground motions, which served as a basis for comparison. Twelve ground motion models were considered from the Next Generation Attenuation West, West2, and East programs due to their extensive databases and robust analytical techniques. A comparison of relative residuals, z-score, and each event found that the subset of Next Generation Attenuation—East ground motion prediction equations did not perform as well as the suite of Next Generation Attenuation—West2 ground motion prediction equations, most likely due to the regional simulations involved in developing the database. Interestingly, the ground motion models that performed relatively well were from the set designed for rock conditions.

Effects of important characteristics of earthquake ground motions on probabilistic seismic demand assessment of long-span cable-stayed bridges

The uncertainties of earthquake ground motions have the most important effects on seismic responses of bridge structures, especially long-span bridges, because of complex and special dynamic properties. The nonlinear dynamic time-history analysis is conducted for a two-pylon long-span cable-stayed highway bridge by using real earthquake ground motions rationally selected. The correlation between the important characteristics of earthquake ground motions and the probabilistic seismic demand assessment of the cable-stayed bridge reveals that the geometric means and dispersions of response spectra from selected ground motions have very significant effects on mean values, dispersions and probabilistic distributions of seismic demands of long-span bridges. The spectral shape of geometric mean spectra in the period ranges with large cumulative modal mass participation factors that should be well matched to the target spectrum to improve the precision and computational efficiency of probabilistic seismic demand assessment. If earthquake ground motions are rationally selected, response spectral values at the periods with comparatively large modal participation mass ratios or PGA can be used as intensity measures and even provide more precise probabilistic seismic demand assessment than response spectral values at the fundamental periods.

An extended generalized conditional intensity measure method for aftershock ground motion selection

AbstractThis study proposes an extended generalized conditional intensity measure (EGCIM)‐based approach to selecting aftershock ground motions that match the target aftershock EGCIM distributions. The main purposes of the proposed methodology are threefold: (a) to consider the multiple characteristics of aftershock ground motions (e.g., amplitude, frequency contents, cumulative effects, and duration), (b) to account for the correlations between mainshocks (MS) and aftershocks (AS) as well as cross‐correlations between AS IMs, and (c) to associate with the conventional mainshock GCIM approach to enforce mainshock‐consistency for practical applications. For these purposes, three components of the EGCIM methodology for aftershock ground motion selection are utilized: (1) the multivariate aftershock EGCIM distributions of any vector of aftershock intensity measures (IMs) are constructed conditioned on two MS‐AS IMs, (2) the correlations between mainshocks and aftershocks as well as the cross‐correlations between AS IMs are considered, and (3) the random realizations of aftershock IMs are generated as a target using the aftershock EGCIM distributions to select aftershock ground motions from the specific database. The proposed methodology is applied to a case study in the LPCC station in New Zealand, whose aftershock EGCIM distributions of the considered 18 IMs are constructed for the case scenario. Among them, the correlations between mainshocks (MS) and aftershocks (AS) as well as the cross‐correlations between AS IMs are determined using the total residuals (epsilons) based on 662 real MS–AS ground‐motion pairs. Several examples are then discussed to illustrate the application of the proposed aftershock EGCIM approach, including the comparison of the aftershock acceleration spectrum with the ASK14 model and the effects of different components of the aftershock IM weight vector. Then, the proposed approach is compared with the conventional aftershock synthesis methods. It is observed that the conventional methods perform biased representations of different aftershock characteristics, of which the limitations are improved in the proposed approach. Moreover, a sensitivity study is performed for a typical midrise structure to investigate the effects of aftershock ground motion selection on the aftershock fragility analysis. The results indicate that the selection of aftershock records considering different AS characteristics has a notable impact on the aftershock fragility assessment. The uncertainties associated with record‐to‐record variations in aftershock fragility analysis can be effectively reduced by using the proposed selection approach that incorporates the comprehensive ground motion characteristics. The findings in this study promote the extension of the existing methods for aftershock ground motion selection, which can be applied to structural design or seismic risk analysis considering the aftershock effects.

Sensitivity of one-dimensional seismic ground response analyses of marine land reclamation profiles to the consideration of earthquake causal rupture parameters in selection of input ground motions

Sensitivity of one-dimensional seismic ground response analyses of marine land reclamation profiles to the consideration of earthquake causal rupture parameters in selection of input ground motions

Investigation of Ambient Seismic Noise in Different Region of Georgia

Georgia, like the whole South Caucasus, is a tectonically and structurally complex region. It is one of the active segments of the Alpine-Himalayan belt, therefore it is important to assess the seismic hazard for Georgia. At the regional scale, this assessment is evaluated by applying probabilistic seismic hazard analysis that identifies the annual probability of exceedance of various ground motion levels defined in terms of selected ground motion intensity measures, such as PGA or spectral accelerations (SA) corresponding to various return periods related to possible future earthquake scenarios for a site represented by soil classes A according to EC8, Euro-code 8-EN 1998-1 (1998). At the local scale, seismic hazard assessment is made by analyzing the geological, geomorphological, geotechnical and geophysical characteristics of the site, as it is well established that the incoming seismic motion can change in amplitude, frequency, and duration due the site-specific local characteristics. That is the subject of micro-zonation investigation. Site – specific local characteristics are presented by the following parameters: Dominant frequency, Vs,30 (average shear-wave velocity to a depth of 30 meters) and amplification factor. In this work, we presented results of geophysical survey assessing local site conditions by dominant frequency that allows identification of similar seismic response areas. For this purpose, seismic noise records have been used first time in Georgia.

An unsupervised machine learning approach for ground‐motion spectra clustering and selection

AbstractClustering analysis of sequence data continues to address many applications in engineering design, aided with the rapid growth of machine learning in applied science. This paper presents an unsupervised machine learning algorithm to extract defining characteristics of earthquake ground‐motion spectra, also called latent features, to aid in ground‐motion selection (GMS). In this context, a latent feature is a low‐dimensional machine‐discovered spectral characteristic learned through nonlinear relationships of a neural network autoencoder. Machine discovered latent features can be combined with traditionally defined intensity measures and clustering can be performed to select a representative subgroup from a large ground‐motion suite. The objective of efficient GMS is to choose characteristic records representative of what the structure will probabilistically experience in its lifetime. Three examples are presented to validate this approach, including the use of synthetic and field recorded ground‐motion datasets. The presented deep embedding clustering of ground‐motion spectra has three main advantages: (1) defining characteristics that represent the sparse spectral content of ground motions are discovered efficiently through training of the autoencoder, (2) domain knowledge is incorporated into the machine learning framework with conditional variables in the deep embedding scheme, and (3) the method results in a ground‐motion subgroup that is more representative of the original ground‐motion suite compared to traditional GMS techniques.