Ground Motion Data Research Articles

Seismological and engineering characteristics of strong motion data from 24 and 26 September 2019 Marmara Sea earthquakes

An MW 4.5 earthquake took place on September 24, 2019 in the Marmara Sea. Two days after, on September 26, 2019, Marmara region was rattled by an MW5.7 earthquake. With the intention of compiling an ample strong ground motion data set of recordings, we have utilized the stations of Istanbul Earthquake Rapid Response and Early Warning System operated by the Department of Earthquake Engineering of Boğaziçi University and of the National Strong Motion Network operated by AFAD. Altogether 438 individual records are used to calculate the source parameters of events; namely, corner frequency, radius, rupture area, average source dislocation, source duration and stress drop. Some of these parameters are compared with empirical relationships and discussed extensively. Duration characteristics are analysed in two steps; first, by making use of the time difference between P-wave and S-wave onsets and then, by considering S-wave durations and significant durations. It is observed that they yield similar trends with global models. PGA, PGV and SA values are compared with three commonly used ground motion prediction models. At distances closer than about 60 km, observed intensity measures mostly conform with the GMPE predictions. Beyond 60 km, their attenuation is clearly faster than those of GMPEs. A frequency-dependent Q model is developed by using both events. Its consistency with existing regional models is confirmed.

Supershear shock front contribution to the tsunami from the 2018 Mw 7.5 Palu, Indonesia earthquake

SUMMARY Hazardous tsunamis are known to be generated predominantly at subduction zones. However, the 2018 Mw 7.5 Palu (Indonesia) earthquake on a strike-slip fault generated a tsunami that devastated the city of Palu. The mechanism by which this tsunami originated from such an earthquake is being debated. Here we present near-field ground motion (GPS) data confirming that the earthquake attained supershear speed, i.e. a rupture speed greater than the shear wave speed of the host medium. We subsequently study the effect of this supershear rupture on tsunami generation by coupling the ground motion to a 1-D non-linear shallow-water wave model accounting for both time-dependent bathymetric displacement and velocity. With the local bathymetric profile of Palu bay around a tidal station, our simulations reproduce the tsunami arrival and motions observed by CCTV cameras. We conclude that Mach (shock) fronts, generated by the supershear speed, interacted with the bathymetry and contributed to the tsunami.

GMPE-consistent hard-rock site adjustment factors for Western North America

Empirical ground-motion prediction equations (GMPEs) such as the Next Generation Attenuation-West2 (NGA-West2) GMPEs are limited in the number of recordings on hard-rock stations used to develop the models. Therefore, the site response scaling in the GMPEs cannot be reliably extrapolated to hard-rock conditions. The state of practice for the development of hard-rock adjustment factors involves the use of analytical methods that typically assign small values to the small-strain damping parameter ([Formula: see text]) for hard-rock sites resulting in large scaling factors at short periods. Alternatively, the hard-rock scaling factors developed in Ktenidou and Abrahamson (KA16) based on empirical ground-motion data are used. These empirical factors, developed for a broad rock site category, show that the average hard-rock scaling factors observed in ground-motion data are small in amplitude contrary to the large factors typically obtained from analytical studies. The empirically derived KA16 factors also suffer from limitations due to the relatively small number of rock sites in the data set and do not distinguish between different hard-rock conditions. To address the shortcomings in the current state of practice, we present a methodology to develop linear site adjustment factors to adjust the NGA-West2 GMPEs from V S30 of 760 m/s to target hard-rock site conditions with V S30 ranging from 1000 to 2200 m/s. These factors are analytically derived using the inverse random vibration theory (IRVT) approach of Al Atik et al. but with inputs constrained using the empirical KA16 factors and normalized to the scaling of the NGA-West2 GMPEs for V S30 of 1000 m/s. The proposed factors merge the results of the NGA-West2 site response scaling for V S30 ≤ 1000 m/s with the KA16 hard-rock category factors to produce a site factor model that is a continuous function of V S30. The epistemic uncertainty of these factors is evaluated.

Ground motion variability in Israel from 3-D simulations of <i>M</i> 6 and <i>M</i> 7 earthquakes

Abstract. In Israel, due to low seismicity rates and a sparse seismic network, the temporal and spatial coverage of ground motion data is insufficient to estimate the variability in moderate–strong (M>6) ground motions required to construct a local ground motion model (GMM). To fill this data gap and to study the ground motion variability in M>6 events, we performed a series of 3-D numerical simulations of M 6 and M 7 earthquakes. Based on the results of the simulations, we developed a parametric attenuation model (AM) and studied the residuals between simulated and AM peak ground velocities (PGVs) and the single station variability. We also compared the simulated ground motions with a global GMM in terms of PGV and significant duration (Ds 595). Our results suggested that the AM was unable to fully capture the simulated ground motion variability mainly due to the incorporation of super-shear rupture and effects of local sedimentary structures. We also showed that an imported GMM considerably deviates from simulated ground motions. This work sets the basis for future development of a comprehensive GMM for Israel, accounting for local source, path, and site effects.

The Potential of Using Fiber Optic Distributed Acoustic Sensing (DAS) in Earthquake Early Warning Applications

ABSTRACT As the seismological community embraces fiber optic distributed acoustic sensing (DAS), DAS arrays are becoming a logical, scalable option to obtain strain and ground-motion data for which the installation of seismometers is not easy or cheap, such as in dense offshore arrays. The potential of strain data in earthquake early warning (EEW) applications has been recently demonstrated using records from borehole strainmeters (BSMs). However, current BSM networks are sparse, installing more BSMs is expensive and often impractical, and BSMs have the same limitations in offshore environments as other traditional seismic instruments. Here, we aim to provide a road map about how DAS data could be used in existing EEW applications, using the ShakeAlert EEW System for the West Coast of the United States as an example. We review the data requirements for EEW systems, examine ways in which strain-derived ground-motion data can be incorporated into such systems without significant modifications, and determine what is still needed for full utilization of DAS data in these applications. Importantly, EEW algorithms require ground-motion amplitude information for rapid earthquake source characterization; thus, accurate strain amplitude observations, not only phase information, are necessary for deriving these ground-motion metrics from DAS data. To obtain high-quality ground-motion observations, EEW-compatible DAS arrays need to be multicomponent, well coupled, and low noise. We suggest ways to achieve such data requirements using existing DAS technology and discuss areas in which further research is needed to optimize DAS array performance for EEW.

Region-specific linear site amplification model for peaty organic soil sites in Hokkaido, Japan

We develop a site amplification model for a regional geologic condition that includes surficial deposits of peaty organic soils in Hokkaido, Japan. We use ground motion data from national Japanese networks along with local data from the study region. We apply a non-reference site approach to infer site effects from misfits of reference ground motion models to data from 10 subduction zone earthquakes. Application of this approach requires removal of source-specific biases and careful consideration of source-to-site path effects. These considerations are essential to avoid mapping source- or path-related model misfits into estimates of site response. We consider three subduction ground motion models as reference models. By paying special attention to the conditions for which the path models are effective, and making adjustments for between-island path misfits (Hokkaido to Honshu and vice versa), we identify regional site effects that are insensitive to the ground motion models used in their derivation. Observed site responses are characterized by strong resonances at first-mode site frequencies, and as a result the regional model is conditioned on peak frequency from horizontal-to-vertical spectral ratios.

Soil Profile Database and Site Classification for National Strong-Motion Stations in Western China

Abstract As part of a broader effort to develop ground-motion data resources for China in a manner consistent with Next Generation Attenuation projects, we develop a database of geotechnical soil profiles and shear-wave velocity (VS) logs for sites having ground-motion accelerometers (this database is referred to as soil profile database [SPD]), and then use that data to develop site parameters. The site information is compiled for stations within the National Strong Motion Observation Network System (NSMONS) of China. This effort is focused on four provinces in western China (Yunnan, Sichuan, Gansu, and Xinjiang), which contribute 871 of 2023 NSMONS stations throughout China. Of the 871 sites, the SPD provides site information for 678 sites, or 78% of the total. To develop the SPD, geotechnical logs and/or VS profiles are compiled for 637 sites. This information is derived from previously unpublished reports and is assembled here in a uniform format and partially disseminated (only approximate site locations are provided). The remaining 41 sites in the SPD have confirmed bedrock conditions at the ground surface. Site metadata and parameters are compiled into a database, included as a supplemental material to this article, that includes the depth to the first occurrence of 0.5 km/s shear-wave velocity (Z0.5), three time-averaged shear-wave velocity parameters (VSe, VS20, VS30), and site classifications based on the Chinese building seismic design code and National Earthquake Hazards Reduction Program Provisions. For sites with VS profiles that do not extend to 30 m, we present region-specific recommendations for extrapolating time-averaged velocities from the profile depth to 30 m. For sites with geotechnical logs but no profiles, we present procedures to estimate VS profiles from which the earlier parameters can be derived.

Nonlinear site response at liquefiable sites: Insights from downhole seismic observations

The nonlinear behavior of shallow soil plays an important role in modifying surface ground motions. We analyze the nonlinear site response at liquefiable sites using the compiled long-term ground motion data (ranging from 1992 to 2020 with over 2000 earthquake events) at four high-quality downhole arrays that have liquefied during at least one earthquake. Two nonlinear event parameters, the percentage of nonlinearity (PNL) and the frequency shift parameter (fsp), are used to quantify the modification of site response of strong events to that of weak events. Site-specific regression analyses are performed to correlate nonlinear event parameters to incident motion parameters. Results indicate that site response at liquefiable sites varies from linear to nonlinear to liquefied states as the incident motion intensity changes, and the occurrence of liquefaction could enhance the amplification of low-frequency and very-high-frequency ground motions. As strong-motion observations for liquefiable sites are very limited, the findings of this work are of significance for further study of strong-motion seismology at liquefiable sites.

Joint analysis of resident complaints, meteorological, acoustic, and ground motion data to establish a robust annoyance evaluation of wind turbine emissions

In order to advance the expansion of onshore wind energy, wind turbines (WTs) must be erected in often complex terrain. The orography and the associated meteorological conditions, as well as the complex propagation of sound and ground motion waves can lead to increased annoyance of residents.Here a joint investigation of residential noise reports, meteorological, acoustic and ground motion data together with operational parameters of a wind farm in southern Germany is presented, with the objective to assess the annoyance of residents affected by WT emissions. Once strongly annoyed residents had been identified, simultaneous measurements were conducted over a 2-month period while residents used a noise reporting app to document their annoyance. A combination of the data shows that WT-related signals can be detected by acoustic and ground motion measurements in the vicinity of the WTs and, to a lesser extent, at the residential sites. In addition, background noise can be identified well. The app data indicates higher complaint rates in the early morning, evening and night hours. Changes in rotation rate seem to be the cause of annoyance as well as high rotation rates. These findings can be used to adjust WT operation in order to decrease immissions.

Study of seismic orientation of structure with bi-directional response analysis in the vicinity of branched fault earthquake rupture

Ground motions originating from earthquakes on complex fault systems have a wide range of spatial variability whose understanding is limited as a result of scarcity in near-field ground motions. The objective of this study is to employ computational seismology by using state of the art tool (SPECFEM3D) in simulating and understanding dynamic earthquake ruptures in a branch fault system. Employing physics-based dynamic rupture simulations not only aids to model complicated fault systems but also allows to gather ground motion data at the desired coordinates which can then be extended to evaluate the response of structures near the fault zone for various earthquake scenarios. In this study, synthetic ground motions are generated for two different cases of rupture propagation by modeling dynamic earthquakes for the left-lateral and right-lateral branched faults systems. In both cases, nucleation initiates in the main fault but differs in the continuous rupture that propagates along (i) the main fault (case-1), and (ii) the branch fault (case-2). Simulated time histories are extracted at certain stations for the near-field as well as far-field and a vectorization based on the orientation of the building is applied. Further, bi-directional responses and Maximum-Radial Spectral Acceleration (Max-RSA) of the structure are produced to study the importance of building orientation with respect to fault line for both rupture scenarios. It is observed that the influence of orientation is minimal for near-field stations (Set-A), perceptible for far-field stations (Set-B) and quite predominant for far-field stations along the strike of the branch fault (Set-C).

Seismic acceleration amplification factor for pin supported moment resisting RC frame structures for Chi-Chi earthquake

The Seismic forces, acting on the non-structural component, are different with respect to the lateral force observed by the different design codes. For remarking the behavior of the structural components during dynamic action, large numbers of research were done as compared to the secondary or non-structural components of the structures. The behavior of inertia force acting on the non-structural components changes along with the altitude of the structure. The inertia force existing on nonstructural components depends on the acceleration amplification factor. In this paper, five different RC moment-resisting frame models as 2,4,6,8 and 10 stories, with pin support conditions are considered. The linear time history method is used for the analysis of all RC frame models with different range (0.01g to 0.32g) of ground motion data. Determine the acceleration amplification factor for all these models and compared with the previously models. It is observed that no previous model performed satisfactory results. Therefore, to proposed the amplification model which not only depends on the height of the building, natural period of the building but also depends on the range of the Peak Ground Acceleration (PGA).The proposed model is compared with the previous renowned models, It is detected that the proposed model performs better results with respect to other models.

Automated Detection of Clipping in Broadband Earthquake Records

Abstract Because the amount of available ground-motion data has increased over the last decades, the need for automated processing algorithms has also increased. One difficulty with automated processing is to screen clipped records. Clipping occurs when the ground-motion amplitude exceeds the dynamic range of the linear response of the instrument. Clipped records in which the amplitude exceeds the dynamic range are relatively easy to identify visually yet challenging for automated algorithms. In this article, we seek to identify a reliable and fully automated clipping detection algorithm tailored to near-real-time earthquake response needs. We consider multiple alternative algorithms, including (1) an algorithm based on the percentage difference in adjacent data points, (2) the standard deviation of the data within a moving window, (3) the shape of the histogram of the recorded amplitudes, (4) the second derivative of the data, and (5) the amplitude of the data. To quantitatively compare these algorithms, we construct development and holdout datasets from earthquakes across a range of geographic regions, tectonic environments, and instrument types. We manually classify each record for the presence of clipping and use the classified records. We then develop an artificial neural network model that combines all the individual algorithms. Testing on the holdout dataset, the standard deviation and histogram approaches are the most accurate individual algorithms, with an overall accuracy of about 93%. The combined artificial neural network method yields an overall accuracy of 95%, and the choice of classification threshold can balance precision and recall.

NGA-Subduction research program

This article summarizes the Next Generation Attenuation (NGA) Subduction (NGA-Sub) project, a major research program to develop a database and ground motion models (GMMs) for subduction regions. A comprehensive database of subduction earthquakes recorded worldwide was developed. The database includes a total of 214,020 individual records from 1,880 subduction events, which is by far the largest database of all the NGA programs. As part of the NGA-Sub program, four GMMs were developed. Three of them are global subduction GMMs with adjustment factors for up to seven worldwide regions: Alaska, Cascadia, Central America and Mexico, Japan, New Zealand, South America, and Taiwan. The fourth GMM is a new Japan-specific model. The GMMs provide median predictions, and the associated aleatory variability, of RotD50 horizontal components of peak ground acceleration, peak ground velocity, and 5%-damped pseudo-spectral acceleration (PSA) at oscillator periods ranging from 0.01 to 10 s. Three GMMs also quantified “within-model” epistemic uncertainty of the median prediction, which is important in regions with sparse ground motion data, such as Cascadia. In addition, a damping scaling model was developed to scale the predicted 5%-damped PSA of horizontal components to other damping ratios ranging from 0.5% to 30%. The NGA-Sub flatfile, which was used for the development of the NGA-Sub GMMs, and the NGA-Sub GMMs coded on various software platforms, have been posted for public use.

HVSR-based Site Classification Approach Using General Regression Neural Network (GRNN): Case Study for China Strong Motion Stations

ABSTRACT Seismic site classification, which is fundamental for site-specific seismic hazard assessment, also plays an important role in accurate interpretation of ground motion data. However, detailed borehole information is not always available in many countries, e.g., China. Therefore, this study investigated application of the generalized regression neural network (GRNN) method to seismic site classification using China strong motion stations as example case. First, stations from KiK-net in Japan were classified based on their borehole information and individually assigned to I, II, III, and IV site classes as defined in Chinese seismic code. Then, mean horizontal-to-vertical spectral ratio (HVSR) curves for each site class were calculated as reference patterns. The overall recall rates for I, II, and III sites could reach 66.60%, 67.57%, and 68.42%, respectively, regarding use of KiK-net stations. The GRNN site classification scheme was validated using borehole information of K-NET stations, with recall rates for I and II site classes reaching 68% and 60%, respectively. Finally, based on HVSR curves calculated using strong ground motion data acquired during 2007–2015 in China, the site conditions of 167 National Strong Motion Observation Network System stations were estimated using the GRNN classification scheme. The results were partially validated using borehole information of 73 stations. The similarity between the mean HVSR curves and reference pattern curves indicated that the GRNN seismic site classification scheme is robust and could produce plausible results succinctly.

Ranking and Selection of Earthquake Ground-Motion Models Using the Stochastic Area Metric

Abstract We introduce the cumulative-distribution-based area metric (AM)—also known as stochastic AM—as a scoring metric for earthquake ground-motion models (GMMs). The AM quantitatively informs the user of the degree to which observed or test data fit with a given model, providing a rankable absolute measure of misfit. The AM considers underlying data distributions and model uncertainties without any assumption of form. We apply this metric, along with existing testing methods, to four GMMs in order to test their performance using earthquake ground-motion data from the Preston New Road (United Kingdom) induced seismicity sequences in 2018 and 2019. An advantage of the proposed approach is its applicability to sparse datasets. We, therefore, focus on the ranking of models for discrete ranges of magnitude and distance, some of which have few data points. The variable performance of models in different ranges of the data reveals the importance of considering alternative models. We extend the ranking of GMMs through analysis of intermodel variations of the candidate models over different ranges of magnitude and distance using the AM. We find the intermodel AM can be a useful tool for selection of models for the logic-tree framework in seismic-hazard analysis. Overall, the AM is shown to be efficient and robust in the process of selection and ranking of GMMs for various applications, particularly for sparse and small-sized datasets.

Improved correlation between foundation and free‐field ground motions through strong motion recordings and kinematic soil–structure interaction analyses

AbstractThe present study explores the effect of soil–structure interaction (SSI) on the foundation motion as recorded at accelerometric stations located at the basement of buildings through a large dataset employed from sites where both foundation and free‐field ground motion recordings are available. The significance of such instrumentation is highlighted, in terms of assessing the high frequency filtering that occurs at the foundation level and its potential implication on ground motion prediction models (GMPMs) that are produced with these datasets. Based on the recorded data, analytical expressions relating foundation, and free‐field motion intensity measures, which have been produced in a previous work, are verified. A sub‐structure analysis procedure is extended to include the site and building characteristics of the recorded data. Nonlinear regression analyses are performed, utilizing numerous analysis results, to derive improved, robust analytical expressions, as well as, to include building characteristics. Residual analysis is performed to assess possible bias due to other variables, as well. The produced analytical expressions are validated against the recorded data and compared to the existing expressions in terms of prediction errors. The produced analytical expressions can be utilized for correcting motions recorded at the basement level of buildings to obtain estimates of free‐field ground motions. The latter being “building‐free” is most appropriate for the development of new generation GMPMs that are not biased by the influence of kinematic and inertial interaction. The impact of correcting ground motion data through the proposed analytical expressions is discussed through their implementation on recently published strong motion dataset.

A gradient ascent algorithm based on possibilistic fuzzy C-Means for clustering noisy data

Real-world data are often corrupted by noise and outliers, which are originated from different procedures such as data collection, storage, and processing. Noise and outliers decrease the quality of clustering and lead to the inaccurate and misplaced cluster centers. In this paper, we propose a new algorithm called Improved Possibilistic Fuzzy C-Means (IPFCM) to cluster noisy data. First, initial cluster centers are calculated by Possibilistic Fuzzy C-Means (PFCM) which do not match dense regions of the data. Then, the domain is divided to some subdomains and each data point is assigned to a sub-domain. The cluster centers are iteratively moved towards high-density regions by maximizing a novel cluster validity index. In the proposed method, a Gaussian membership function is defined on each cluster to weight the data. Then, the sum of weights in each cluster is calculated. The product of these values is considered as the validity index. Since division of the domain is changed with moving the cluster centers, this procedure is repeated until the convergent criterion is satisfied. Cluster analysis performed on six synthetics, nine real benchmarks datasets shows the superiority of IPFCM over some previous clustering algorithms such as Fuzzy C-Means (FCM), PFCM, Kernel Fuzzy C-Means (KFCM), Noise Clustering (NC), and Generalized Entropy based Possibilistic Fuzzy C-Means (GEPFCM). The clustering results of near-fault ground motion data indicate that the cluster centers identified by IPFCM are well separated from each other, while those for PFCM are close to each other in some datasets. Moreover, the results show that the impact of noisy data on the proposed index and consequently cluster analysis decreases as the noisy data get away from the cluster centers which is one of the advantages of using IPFCM algorithm.

Landslide Forecast by Time Series Modeling and Analysis of High-Dimensional and Non-Stationary Ground Motion Data

High-dimensional, non-stationary vector time-series data are often seen in ground motion monitoring of geo-hazard events, e.g., landslides. For timely and reliable forecasts from such data, we developed a new statistical approach based on two advanced econometric methods, i.e., error-correction cointegration (ECC) and vector autoregression (VAR), and a newly developed dimension reduction technique named empirical dynamic quantiles (EDQ). Our ECC–VAR–EDQ method was born by analyzing a big landslide dataset, comprising interferometric synthetic-aperture radar (InSAR) measurements of ground displacement that were observed at 5090 time states and 1803 locations on a slope. The aim was to develop an early warning system for reliably forecasting any impending slope failure whenever a precursory slope deformation is on the horizon. Specifically, we first reduced the spatial dimension of the observed landslide data by representing them as a small set of EDQ series with negligible loss of information. We then used the ECC–VAR model to optimally fit these EDQ series, from which forecasts of future ground motion can be efficiently computed. Moreover, our method is able to assess the future landslide risk by computing the relevant probability of ground motion to exceed a red-alert threshold level at each future time state and location. Applying the ECC–VAR–EDQ method to the motivating landslide data gives a prediction of the incoming slope failure more than 8 days in advance.

Introducing ISMDq—A Web Portal for Real-Time Quality Monitoring of Italian Strong-Motion Data

Abstract We present the Istituto Nazionale di Geofisica e Vulcanologia Strong-Motion Data-quality (ISMDq)—a new automatic system designed to check both continuous data stream and event strong-motion waveforms before online publication. The main purpose of ISMDq is to ensure accurate ground-motion data and derived products to be rapidly shared with monitoring authorities and the scientific community. ISMDq provides data-quality reports within minutes of the occurrence of Italian earthquakes with magnitude ≥3.0 and includes a detailed daily picture describing the performance of the target strong-motion networks. In this article, we describe and discuss the automatic procedures used by ISMDq to perform its data-quality check. Before an earthquake, ISMDq evaluates the selected waveforms through the estimation of quality indexes employed to reject bad data and/or to group approved data into classes of quality that are useful to quantify the level of reliability. The quality indexes are estimated based on comparisons with the background ambient noise level performed both in the time and frequency domains. As a consequence, new high- and low-noise reference levels are derived for the overall Italian strong-motion network, for each station, and for groups of stations in the same soil categories of the Eurocode 8 (Eurocode 8 [EC8], 2003). In absence of earthquakes, 24 hr streaming of ambient noise recordings are analyzed at each station to set an empirical threshold on selected data metrics and data availability, with the goal to build a station quality archive, which is daily updated in a time span of six months. The ISMDq is accessible online (see Data and Resources) from August 2020, providing rapid open access to ∼10,000 high-quality checked automatically processed strong-motion waveforms and metadata, relative to more than 160 Italian earthquakes with magnitude in the 3.0–5.2 range. Comparisons between selected strong-motion data automatically processed and then manually revised corroborate the reliability of the proposed procedures.

Comparative Study of Seismic Acceleration Amplification Models for RC Frame Structures

As the current aspect, the nonstructural components (NSCs) linked with the structures are more affected during the seismic motion. It causes not only loss of the economy but also affected life. The various codal provision has been available for minimizing the damages of primary components, but for NSCs, a minimal requirement is functional. So that more investigation is required for understating the behavior of NSCs during the seismic motion. The research aims to understand the behavior of acceleration demand on NSCs in a building. Structures subjected to inertia forces due to earthquakes experience damage of nonstructural components (NSC). The inertia force acting the NSCs are related to acceleration amplification factor. For obtaining the peak horizontal floor acceleration with respect to tectonic ground motion, these factors are used. In this paper, mathematical models of the acceleration amplification factor defined as the peak floor acceleration with respect to peak ground acceleration, given by previous researchers, has been compared. For this 2,4,6,8 and 10 storey moment-resisting frame models considering 29 ground motion data ranging between 0.1g to 0.2g, is analyzed using linear time history method. The supports of the models are considered fixed. The ETABS software is used for the analysis of the models. To analyses the models, the modal mass participation ratio plays a significant role. ASCE 7-05 defines that the structure should be investigated and designed when the model mass participation ratio is equal to or more than 90 per cent. Based on the results, a comparison of the reported models is made. There is a strong need for further research to refine the models for the realistic prediction of acceleration amplification factor.