Ground Motion Characteristics Research Articles

Machine learning prediction models for ground motion parameters and seismic damage assessment of buildings at a regional scale

This study examines the feasibility of using a machine learning approach for rapid damage assessment of reinforced concrete (RC) buildings after the earthquake. Since the real-world damaged datasets are lacking, have limited access, or are imbalanced, a simulation dataset is prepared by conducting a nonlinear time history analysis. Different machine learning (ML) models are trained considering the structural parameters and ground motion characteristics to predict the RC building damage into five categories: null, slight, moderate, heavy, and collapse. The random forest classifier (RFC) has achieved a higher prediction accuracy on testing and real-world damaged datasets. The structural parameters can be extracted using different means such as Google Earth, Open Street Map, unmanned aerial vehicles, etc. However, recording the ground motion at a closer distance requires the installation of a dense array of sensors which requires a higher cost. For places with no earthquake recording station/device, it is difficult to have ground motion characteristics. For that different ML-based regressor models are developed utilizing past-earthquake information to predict ground motion parameters such as peak ground acceleration and peak ground velocity. The random forest regressor (RFR) achieved better results than other regression models on testing and validation datasets. Furthermore, compared with the results of similar research works, a better result is obtained using RFC and RFR on validation datasets. In the end, these models are utilized to predict the damage categories of RC buildings at Saitama University and Okubo Danchi, Saitama, Japan after an earthquake. This damage information is crucial for government agencies or decision-makers to respond systematically in post-disaster situations.

Characteristics of torsional ground motions obtained for Turkey and their appraisal against Eurocode 8

In current engineering practice, the torsional effects of ground motions are only considered implicitly through “accidental eccentricity coefficients". In seismic design codes, these coefficients account for not only the unintended (or uncertain) asymmetric distributions of structural mass and stiffness in the plan but also the torsional components of ground motions. The torsional effects may develop due to wave passage effects or the incoherency of translational ground motions along the foundation systems. While it is possible to obtain torsional components of ground motions directly from seismic records with modern instruments, such sensors are uncommon. Therefore, several numerical procedures have been proposed in the literature for estimating torsional components of ground motions from translational measurements. In this study, the Single Station Procedure (SSP)—based on the deconstruction of translational ground motions into body waves and the subsequent reconstruction of rotational ground motions—is employed to obtain torsional ground motions for seven different seismic events in Turkey. Additionally, the corresponding accidental eccentricity coefficients that can cause the same response modification with torsional ground motions are determined. First, a conventional procedure is utilized to obtain the corresponding accidental eccentricity coefficients. Various drawbacks of the conventional procedure, such as negative eccentricity coefficients, are addressed. Then, a relatively obscure definition of accidental eccentricity is revived to propose a novel procedure for determining accidental eccentricity coefficients. It is observed that the proposed procedure yields higher values for the accidental eccentricity coefficients than the conventional procedure. It also observed that the torsional spectrum provided in Eurocode 8 provisions requires higher eccentricity values than those implied by the recorded ground motions from Turkey.

Constant damage inelastic permanent period shift ratios spectra

The phenomenon of elongation of the structural period after earthquake excitation has been widely observed, and elongated period obtained after earthquake excitations can be used for post-earthquake assessment. The inelastic permanent period shift ratio, an indicator representing the degree of stiffness degradation and closely relating to the damage state, is defined as the ratio of the elongated period Tin of single-degree-of-freedom (SDOF) systems after earthquake excitations to the initial period Tel. The objective of this paper is to investigate the inelastic permanent period shift ratios under constant damage performances through nonlinear time-history analysis of SDOF systems, and the modified Park-Ang damage index is employed to characterize the structural damage. Inelastic permanent period shift ratio spectra are statistically constructed considering both the ground motion characteristics (such as site conditions, velocity pulse, magnitude, closet distance, directivity effects, significant duration and frequency contents) and structural parameters (including damage states, hysteretic models, ultimate deformation capacity, energy dissipation factor and postyield ratio). The results showed that the permanent period shift ratio is a reliable damage indicator for assessment of global damage states. The influence caused by ground motions on the estimation of the mean of the permanent period elongation ratio under constant damage performances is limited, while the effects caused by different structural damage and deformation capacity can reach up to approximately 70% and 33%, respectively. An expression for the relationship between the damage index and the permanent period shift ratio is established. Ignoring the differences of structural performances, using the period shift ratios to directly assess the global damage states will lead to erroneous results.

Ground-Motion Intensity Measures for the Seismic Response of the Roof-Isolated Large-Span Structure

Ground-motion intensity measures (IMs), which quantify and describe the characteristics of earthquake ground motion, are of utmost importance in the assessment of seismic risk and the design of resilient structures with large spans. The appropriate selection of a ground-motion IM is crucial in establishing a reliable and robust correlation between seismic hazards and structural demands. The current study presents a novel ground-motion IM that incorporates the influence of multiple vibration modes and period elongation resulting from isolation based on the velocity spectrum. A comprehensive study has been conducted to examine the efficiency of 37 different ground-motion IMs on a roof-isolated large-span structure with engineering demand parameters (EDPs), using far-field ground-motion data. The initial examination of the proposed intensity measure involves a planar lumped-mass model. Subsequently, a numerical model of a large-span roof-isolated structure, specifically the Beijing Workers’ Stadium, is constructed and examined. The results suggest that the proposed intensity measure (IM) demonstrates satisfactory adequacy and achieves optimal efficiency when considering three different engineering demand parameters among 37 other ground-motion intensity measures.

Analysis of ground motion characteristics and near-fault effects for the September 5, 2022, MS6.8 Luding earthquake

Analysis of ground motion characteristics and near-fault effects for the September 5, 2022, MS6.8 Luding earthquake

Rapid Seismic Damage Assessment of RC Bridges Considering Time–Frequency Characteristics of Ground Motions

Rapid Seismic Damage Assessment of RC Bridges Considering Time–Frequency Characteristics of Ground Motions

S-wave velocity structures and ground motion characteristics beneath valley bottom lowlands: A case study from the lowlands along the Kanda and Furukawa rivers incising the Musashino Upland, Tokyo, central Japan

S-wave velocity structures and ground motion characteristics beneath valley bottom lowlands: A case study from the lowlands along the Kanda and Furukawa rivers incising the Musashino Upland, Tokyo, central Japan

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.

Deep learning‐based response spectrum analysis method for building structures

AbstractThe response spectrum method has gained widespread acceptance in practical applications owing to its favorable compromise between accuracy and practical efficiency. The method predicts the peak responses of multi‐degree‐of‐freedom (MDOF) systems by combining modal responses. The Square Root of the Sum of Squares (SRSS) and Complete Quadratic Combination (CQC) rules are commonly used for modal combinations. However, it has been widely known that these rules have limitations in accurately predicting responses influenced by higher modes and cross‐modal correlations. To improve the accuracy of the response spectrum analysis method for building structures, this paper proposes a Deep learning‐based modal Combination (DC) rule by introducing modal contribution coefficients predicted by a deep neural network (DNN) model. The DC rule enhances prediction accuracy by considering the characteristics of ground motion and the dynamic properties of a structural system. The DC rule provides more accurate predictions than the conventional rules, particularly for irregular response spectra and responses affected by higher modes. The efficiency and applicability of the DC rule are demonstrated by numerical investigations of multistory shear buildings and steel frame structures with regular and irregular shapes. The source codes, data, and trained models are available for download at https://github.com/tyongkim/ERD2.

Duration Characteristics of Strong Ground Motions Recorded during the 6 February 2023 M 7.8 Pazarcık, Kahramanmaraş, Türkiye, Earthquake

Abstract The seismograms recorded at a total of 71 strong ground-motion stations (SGMS) located within 100 km of the fault rupture were used to investigate the durational variability observed during the 6 February 2023 M 7.8 Pazarcık, Kahramanmaraş, Türkiye, earthquake. More specifically, significant duration (D) and equivalent number of uniform stress cycles (N) estimated using these accelerograms were compared with the ones predicted by Cetin et al. (2021) and Davatgari-Tafreshi and Bora (2023) models, respectively. To facilitate the comparisons, residuals were estimated and presented with reference to (1) SGMS’ geographical locations; (2) recorded peak ground acceleration (PGA) intensities; (3) distance to the fault rupture, or RJB; (4) site stiffness; and (5) angular orientation of the stations relative to the fault rupture. The assessment results reveal that during the Pazarcık event the duration parameters of D5−75, D5−95, and N were recorded as 23 s, 40 s, and 25 cycles, respectively, on average. Hatay and Kahramanmaraş emerge as the metropolitan cities with the highest PGA intensities, coupled with above-the-average significant durations. A negative correlation is evident between the duration and PGA intensity levels for stations located on the Anatolian side of the rupture, particularly pronounced for the significant duration parameters. The N, D5−75, and D5−95 values appear to be unaffected by RJB distances within 10 km, after which they exhibit an increasing trend. The significant durations were evaluated to be longer at softer soil sites. As the azimuth angle θ increases, D and N were observed to increase.

Failure Probability and Economic Loss Assessment of a High-Rise Frame Structure under Synthetic Multi-Dimensional Long-Period Ground Motions

Multiple research studies and seismic data analyses have shown that multi-directional long-period ground motion affects crucial and intricate large-scale structures like oil storage containers, long-span bridges, and high-rise buildings. Seismic damage data show a 3–55% chance of long-period ground motion. To clarify, the chance of occurrence is 3% in hard soil and 83% in soft soil. Due of the above characteristics, the aseismic engineering field requires a realistic stochastic model that accounts for long-period multi-directional ground motion. A weighted average seismic amplification coefficient selected NGA database multi-directional long-period ground motion recordings for this study. Due to the significant low-frequency component in the long-period ground motion, this research uses empirical mode decomposition (EMD) to efficiently decompose it into a composite structure with high- and low-frequency components. Given the above, further investigation is needed on the evolutionary power spectrum density (EPSD) functions of high- and low-frequency components. Analyzing the recorded data will reveal these functions and their corresponding parameters. Proper orthogonal decomposition (POD) is needed to simulate samples of high- and low-frequency components in different directions. These samples can be combined to illustrate multi-directional long-period ground motion. Representative samples exhibit the seismic characteristics of long-period multi-directional ground motion, as shown by numerical examples. This proves the method’s engineering accuracy and usefulness. Moreover, this study used incremental dynamic analysis (IDA) to apply seismic vulnerability theory. This study investigated whether long-period ground motions in both x and multi-directional directions could enhance the seismic response of a high-rise frame structure. By using this method, a comprehensive seismic economic loss rate curve was created, making economic loss assessment clearer. This study shows that multi-directional impacts should be included when studying seismic events and calculating structure economic damages.

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.

Modeling and Prediction of Nonstationary Ground Motions as Time–Frequency Images

Proper modeling of nonstationary characteristics of ground motions is critical in estimating the seismic performance of structures. The importance of ground motion nonstationarity is being increasingly recognized in seismic design codes and provisions. This paper develops a new method for predicting seismic ground motions in the joint time–frequency domain based on recorded ground motions. We treat wavelet energy maps of acceleration records as images and extract essential patterns from them using principal component analysis. These patterns are then linked to the seismic source, path, and site variables using general regression neural network. The resulting model predicts an image representing the expected evolutionary power spectrum conditioned on the input variables. An example application is presented using records from the Next Generation Attenuation of Ground Motions Database of the Pacific Earthquake Engineering Center. As opposed to conventional ground motion prediction models, the proposed approach retains the time domain characteristics of ground motions. The results show that the proposed model can predict significant patterns in the seismic energy distribution, acceleration time series, and 5% damped elastic spectral accelerations.

Impacts of ground motion characteristics on liquefaction triggering and lateral displacement of a sloping ground

Impacts of ground motion characteristics on liquefaction triggering and lateral displacement of a sloping ground

Influence of ground motion characteristics on liquefaction-induced pipe uplift

Influence of ground motion characteristics on liquefaction-induced pipe uplift

Analysis of Strong Ground Motion Characteristics for the Jishishan MS6.2 Earthquake December 18, 2023

The MS6.2 earthquake occurred in Jishishan, Gansu Province, China, on December 18, 2023. Based on some strong seismic acceleration time histories captured by some stations of China National Strong Earthquake Observation Network and National Earthquake Early Warning Network, the spatial distribution characteristics, rupture directivity effect, hanging wall and footwall effect, response spectrum characteristics, and attenuation of the peak ground acceleration (PGA) were analyzed in this paper. It can be used as reference for the site determination of post‐earthquake reconstruction and seismic design of engineering structures. The analysis shows that the spatial distribution of strong ground motion of the Jishishan MS6.2 earthquake is mainly controlled by the distribution of causative fault. Near the fault, the effect of rupture directivity is obvious. The effect of hanging wall and footwall significantly affects the distribution of PGAs for this earthquake, causing a pronounced asymmetric pattern about PGAs relative to the fault, with slower attenuation on the hanging wall compared to the footwall. The type of site obviously affects the characteristics of response spectrum. The ground motion intensity observed at the epicenter of this earthquake significantly exceeded the levels of rare and extremely rare seismic events in the current seismic design codes. The attenuation relationship of PGA obtained by regression analysis shows that ones in the 5th generation Chinese ground motion parameter zoning map for the Qinghai‐Tibet region underestimate the horizontal PGA.

Single-pulse-like and double-pulse-like characteristics of near-fault ground motions

An approach is proposed for quantitatively identifying the number of one-cycle pulses of ground motions. The one-cycle pulse signals are derived from the original velocity time series using the low-pass acausal fourth-order Butterworth filter and the zero-point method, and the adaptive cut-off frequency of the filter is determined by the number of extreme points in the extracted pulse signals. Then, according to the relative energy and the comprehensive evaluation index of the extracted pulse signals, ground motions are classified into four classes: nonpulse-like, single-pulse-like, double-pulse-like, and ambiguous. The statistical relationships between pulse characteristics and source parameters, including moment magnitudes, site conditions, and rupture distances, are discussed. In addition, the relationship between the first-arriving-pulse signal and the last-arriving-pulse signal in double-pulse-like ground motion is investigated. Finally, this approach analyzes the distribution of double-pulse-like and single-pulse-like ground motions recorded during the 1999 Chi-Chi earthquake.

Study on the seismic characteristics of piers considering site conditions and hydrodynamic effect

Sea-crossing bridges face complex site environments compared to onshore bridges, with the marine environment significantly influencing seismic responses. Despite this, current seismic design for these bridges relies on onshore earthquake records. Therefore, investigating the impact of seawater layers and site conditions on sea-crossing bridge seismic performance is crucial. This paper investigates the influence of characteristics of offshore ground motion, site conditions, and hydrodynamic effect on the seismic performance of piers. Initially, based on the validated finite element model, 5 offshore and onshore strong ground motion records from K-NET were selected for assessing the seismic performance of piers. Subsequently, the effects of the water depth and site conditions on the seismic responses of piers were investigated. Finally, the effects of the section size, section shape, and boundary conditions on the piers were investigated. The result shows that the seismic response of piers under offshore ground motion exceeds that under onshore ground motion. In addition, the seismic response of the pier increases with greater water depth, while they exhibit a slight decrease with increasing soil depth. Notably, the larger the section size, result in higher the hydrodynamic pressure, and square piers experience greater hydrodynamic pressure compared to circular piers.

Seismic behaviour of the curved bridge with friction pendulum system

ABSTRACT The friction pendulum system (FPS) is a crucial tool for seismic protection. However, effectiveness in curved bridges, especially controlling torsional moments (TMs), has not been thoroughly assessed. This study evaluates how well a continuously curved bridge equipped with FPS performs under various earthquake loadings. The study assesses FPS efficiency, considering factors like multidirectional impacts, seismic motion strength, incidence angle variations, and ground motion characteristics. Finite element modeling of the bridge structure was conducted using SAP2000v20 Finite Element Analysis software. Three earthquake scenarios (near fault, far fault and forward directivity) were considered. The study also examines the bidirectional behavior’s impact on the bearing and pier interaction. A sensitivity analysis was performed for the bridge response concerning bearing design parameters for each earthquake scenario. The results show that FPS bearings significantly reduce the bridge’s seismic response. Bidirectional excitation was found to be critical for curved bridges, increasing displacement compared to unidirectional motion. Under unidirectional excitation, the isolated bridge with FPS reduces TMs by approximately 26–54%. In bidirectional excitation, FPS remains effective, reducing TMs by about 14–43%. This study underscores the importance of FPS in enhancing seismic resilience, particularly in controlling torsional responses in complex, curved bridges in varying seismic conditions.

Trend Classification of InSAR Displacement Time Series Using SAE–CNN

Multi-temporal Interferometric Synthetic Aperture Radar technique (MTInSAR) has emerged as a valuable tool for measuring ground motion in a wide area. However, interpreting displacement time series and identifying dangerous signals from millions of InSAR coherent targets is challenging. In this study, we propose a method combining stacked autoencoder (SAE) and convolutional neural network (CNN) to classify InSAR time series and ease the interpretation of movements. The InSAR time series are classified into five categories, including stable, linear, accelerating, deceleration, and phase unwrapping error (PUE). The accuracy of labeled samples reaches 95.1%, reflecting the performance of the proposed method. This method was applied to the InSAR results for Kunming extracted from 171 ascending Sentinel-1 images from January 2017 to September 2022. The classification map of the InSAR time series shows that stable coherent points dominate around 79.28% of the area, with linear patterns at 10.70%, decelerating at 5.30%, accelerating at 4.72%, and PUE patterns at 3.60%. The results demonstrate that this method can distinguish different ground motion features and detect nonlinear deformation signals on a large scale without human intervention.