Ground Motion Characteristics Research Articles

Ground motion characteristics induced by high-magnitude events with different source mechanisms of longwall mining

Rockbursts have become one of the most serious hazards in underground mines worldwide. While the characteristics of ground motions before rockbursts (or high-magnitude events (HMEs)) have not been studied. In this study, the peak ground velocity (PGV) and cumulative absolute displacement (CAD) before HMEs were examined based on seismic monitoring in a coal mine. It’s found that the source mechanisms vary significantly across different zones of the longwall, attributed to the low correlation between rock fracture types, stress levels, and seismic intensity. This correlation is mainly related to the distribution of primary fractures within the rock mass. The non-overlap of CAD peak with HME and the induction of delayed rockbursts are mainly caused by the unavoidable creep and additional energy input into the coal mass under disturbances before instability. The energy conversion process of the entire system is determined to be controlled by the stiffness ratio between the roof-floor system and the loaded coal, in accordance with the concept of dynamic and static load superposition. A high static load, strong dynamic disturbances, and a low rigidity ratio between surrounding rock and coal are identified as the most hazardous factors.

Pulse-type seismic response prediction for uncertain structures based on the attention mechanism and wavelet decomposition augmented decomposition learning

Accurate prediction of structural responses under seismic action is important for the performance assessment of structures. However, depending on the variability of the structure (e.g., the design information) or ground motion characteristics (e.g., the presence of pulses), the seismic response characteristics of the may be significantly different. To overcome this problem, this study proposes a novel structural feature attention mechanism and wavelet decomposition augmented decomposition learning method (SFA-WD-DL) for predicting the time-series of the structural response under pulse-type ground motions. Unlike previous studies, the proposed method can be applied to structures with different shapes. Innovatively, a structural feature attention mechanism is embedded within a neural network model, enabling the consideration of different structural parameters. In addition, a wavelet decomposition-based velocity pulse identification method is combined with decomposition learning, using decomposed pulses and high-frequency features as inputs to the neural network model. This process simplifies pulse feature recognition task of the model. To avoid an unbalanced distribution of ground motion features affecting model performance, a balanced sampling strategy for ground motion datasets combining ground motion clustering and category reorganization methods is proposed, which effectively improves the model robustness. The accuracy and applicability of the proposed method are validated using a dataset containing reinforced concrete frame structures of different shapes and a pulse-type ground motion dataset containing 148 records. In addition, the importance levels of the structural features are obtained through the weight distribution of the structural feature attention mechanism. This indicates that the proposed SFA-WD-DL method is easily interpretable and can extract key features affecting the structural response.

Comparative Study of Semi-Active Control Effectiveness of Structures on Soft Soil Using MR Damper: Shaking Table Investigation

The effect of soil-structure interaction (SSI) cannot be neglected in semi-active vibration control of structures located on soft soil. To investigate the mitigating effect of magnetorheological (MR) damper on the semi-active control of structures considering the SSI, shaking table tests were conducted to evaluate the performance of the MR damper-based semi-active control system for structures on soft soil. In addition to a novel fuzzy control method based on online learning deep neural network (OL-DFNN), various control strategies, including passive OFF, passive ON, ON–OFF and deep neural network fuzzy control (DFNN) were systematically evaluated. The test results showed that the OL-DFNN control method exhibited superior adaptability to varying ground motion and structural dynamic characteristics compared to the other control methods. In particular, the OL-DFNN controller effectively mitigated the peak displacement and root mean square displacement, outperforming the passive OFF, passive ON, ON–OFF and DFNN control strategies. The test results highlight the effectiveness of the OL-DFNN control method in addressing the challenges posed by dynamic and time-varying characteristics in structural vibration control systems considering SSI.

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.

Seismic evaluation and comparison of ground motion characteristics in Kahramanmaras and Hatay provinces following the 2023 Pazarcik-Elbistan Earthquake sequences

Seismic evaluation and comparison of ground motion characteristics in Kahramanmaras and Hatay provinces following the 2023 Pazarcik-Elbistan Earthquake sequences

Kinematic rupture modeling of broadband ground motion from the 2022 MS6.9 Menyuan earthquake

AbstractWe propose a novel kinematic rupture modeling procedure for synthesizing broadband ground motions derived from the frequency-wavenumber integration algorithm. This procedure addresses two key issues in characterizing the rupture processes relevant to broadband seismic radiation: an accurate Green's function and a well-constrained kinematic source model. For the first issue, we derive the theoretical Green's function based on an improved dynamic stiffness matrix approach that effectively handles wave propagation in a 1D crustal velocity structure across a broad frequency band. For the second issue, we generate the hybrid source model that combines asperity slip and random slip over the fault plane to effectively implement constraints on the radiated energy during the whole rupture process. The accuracy and effectiveness of the proposed methodology are verified by comparing with the surface acceleration traces and Fourier spectra calculated by spectral element method. With the hybrid source model and crustal velocity structure applicable to the target area, the broadband (0–10 Hz) ground motion of the 2022 MS6.9 Menyuan earthquake is synthesized. The amplitude, duration, and frequency content of the synthetic motions are systematically compared with those of the available observed records and ground motion attenuation relationships, as well as the spatial distribution characteristics of the near-field ground motions from the earthquake scenarios are presented. In conclusion, the case study of the Menyuan MS6.9 earthquake demonstrates that the presented modeling procedure can estimate broadband ground motions rapidly and reliably from a physics-based kinematic rupture perspective.

Long-period ground motion simulation and pulse probability distribution characteristics of the Kumamoto MW 7.1 earthquake in Japan

The 2016 Kumamoto MW 7.1 earthquake in Japan caused severe structural damage. It produced valuable seismic records that offered an opportunity to investigate the characteristics of long-period ground motion and pulse probability distribution. This study works in three main parts. First, we use the finite difference method (FDM) to reproduce the long-period ground motions of the 2016 Kumamoto MW 7.1 earthquake. That provides a crucial foundation for the subsequent phases. We identify velocity pulses within both synthetic and observed waveforms. By rigorously analyzing these pulses, we unveiled a wealth of insights into the characteristics of velocity pulses, strengthening the influence of the rupture directivity effect. In particular, the northeast region of the rupture exhibited a significant concentration of pulsed attributes. Finally, we further focus on establishing statistical relationships between different pulse parameters. The study revealed that the pulse probability distribution model fit well with the observed pulse distribution in the parallel to the fault (FP) and normal to the fault (FN) directions. However, the model also faced challenges in the FN direction due to the mechanism of velocity pulses and altered rupture propagation direction. Crucially, the study established that velocity pulses primarily cluster within a fault distance of less than 30 km, and the relationship between pulse peak value and fault distance followed an exponential trend. The study underscores the imperative of further refining the probability distribution model of velocity pulses, making it more robust and widely applicable. Furthermore, the correlation between velocity pulses and seismic parameters requires further validation through seismic records. This study sets the stage for a deeper understanding of earthquake-induced ground motion and pulse characteristics, contributing to the ongoing efforts in seismic risk assessment.

Statistical Analysis of Characteristic Parameters and Probability Distribution of Near-Fault Velocity Pulses—A Case Study on the 1999 Mw 7.6 Chi-Chi Earthquake

Abstract The 1999 Mw 7.6 Chi-Chi earthquake in the Taiwan region generated valuable ground motions, providing an opportunity for studying the characteristic parameters and distribution of near-fault velocity pulses. Using the finite-difference method, we built a source model, simulated the 1999 Mw 7.6 Chi-Chi earthquake ground motions, and obtained synthetic velocity waveforms consistent with the observed waveforms. On this basis, we analyzed the distribution of velocity pulses in the near-fault region and compared it with the pulse probability distribution (PPD) curve of the near-fault velocity pulse. We found that the complex rupture process of the Mw 7.6 Chi-Chi earthquake resulted in velocity pulses still being recorded in Miaoli and Xinzhu. In addition, we analyzed the relationship between pulse period, pulse peak, and fault distance. The pulse peak indicates a clear attenuation trend with increasing fault distance (Rrup) and no statistical relationship between the pulse period and Rrup. More velocity pulses in normal-fault components reveal the reverse fault of the Chi-Chi earthquake. Finally, structures with natural periods within the 1–7 s are more susceptible to resonance from near-fault velocity pulses, and it is necessary to take appropriate seismic measures. This study lays the foundation for a deeper understanding of the ground motion and pulse characteristics caused by earthquakes and contributes to sustained efforts in seismic hazard assessment.

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 nonlinear structural pulse-like seismic response prediction method based on pulse-like identification and decomposition learning

Abstract Accurate prediction of structural responses under seismic action is important for the performance evaluation of structures. However, depending on the differences in the characteristics of ground motions, particularly whether they contain impulses, the seismic-response characteristics of structures may differ significantly. Hence, a new method, i.e., WD–AttDL, is proposed in this study to predict structural response under pulse-like ground motions. This method innovatively combines a wavelet decomposition-based velocity pulse-identification method with decomposition learning, where decomposed pulses and high-frequency features are used as inputs to the neural-network model, thus simplifying the identification of pulse features for the model. The decomposition learning module integrates different types of submodules, such as CNN feature-extraction, LSTM temporal-learning, and self-attention mechanism submodules. To reduce the nonlinear time-range analysis calculations required to generate training data, a screening method for impulsive ground motions based on time-series feature clustering is proposed. The accuracy and applicability of the proposed method are verified by numerically simulating three different cycles of reinforced-concrete frame structures and a three-dimensional masonry structure. Compared with existing structural seismic-response models, WD–AttDL synergistically integrates different modules and thus offers a higher prediction accuracy. In particular, it reduces the peak error of the predicted response, which is important for the evaluation of structural performance. Additionally, based on the response predicted by WD–AttDL, a vulnerability analysis of the structure under pulse-like seismic effects can be performed rapidly and accurately.

SITE-SPECIFIC DISAGGREGATION ANALYSIS OF RECENT EARTHQUAKE ON THE AMBON COAST

Seismic activity in the eastern region of Indonesia, particularly in Ambon, has been a matter of great concern because of its high tectonic activity. The devastating magnitude 6.5 earthquake in September 2019, which resulted in fatalities and significant damage, highlighted the region’s vulnerability and emphasized the importance of understanding the area’s seismic hazards. Although previous studies have been conducted, this study aims to fill critical gaps in the literature by using the latest seismic data until 2022 and updated attenuation equations. Disaggregation analysis is employed to identify ground motion characteristics, that are crucial for calculating synthetic ground motion acceleration. Under risk-targeted maximum considered earthquake (MCER) conditions, the study results show a peak ground acceleration of 0.444 g, with spectral acceleration at 0.2 s reaching 1.093 g, which decreases to 0.350 g at 1.0 s. Notably, background earthquake sources play a dominant role in contributing to the region’s high seismic hazard potential. The results of this study highlight the urgency of updating Indonesia’s national seismic hazard map. The study also recommends future studies to explore additional parameters of newly discovered faults to enhance understanding of fault movements and their impacts in the study area.

Dynamic rupture modeling in a complex fault zone with distributed and localized damage

Active fault zones have complex structural and geometric features that are expected to affect earthquake nucleation, rupture propagation with shear and volumetric deformation, and arrest. Earthquakes, in turn, dynamically activate co-seismic off-fault damage that may be both distributed and localized, affecting fault zone geometry and rheology, and further influencing post-seismic deformation and subsequent earthquake sequences. Understanding this co-evolution of fault zones and earthquakes is a fundamental challenge in computational rupture dynamics with consequential implications for earthquake physics, seismic hazard and risk. Here, we implement a continuum damage-breakage (CDB) rheology model in our MOOSE-FARMS dynamic rupture simulator to investigate the interplay between bulk damage and fault motion on the evolution of dynamic rupture, energy partitioning, and ground motion characteristics. We demonstrate several effects of damage (accounting for distributed cracking) and breakage (accounting for granulation) on rupture dynamics in the context of two prototype problems addressed currently in the 2D plane-strain setting: (1) a single planar fault and (2) a fracture network. We quantify the spatio-temporal reduction in wave speeds associated with dynamic ruptures in each of these cases and track the evolution of the original fault zone geometry. The results highlight the growth and coalescence of localization bands as well as competition between localized slip on the pre-existing faults vs. inelastic deformation in the bulk. We analyze the differences between off-fault dissipation through damage-breakage vs. plasticity and show that damage-induced softening increases the slip and slip rate, suggesting enhanced energy radiation and reduced energy dissipation. These results have important implications for long-standing problems in earthquake and fault physics as well as near-fault seismic hazard, and they motivate continuing towards 3D simulations and detailed near-fault observations to uncover the processes occurring in earthquake rupture zones.

Kinematic Source Properties of the 2023 Mw 7.7 Türkiye Earthquake Inferred from Near-Fault Strong Ground Motions

Abstract The 2023 Mw 7.7 Türkiye earthquake provided densely observed near-fault ground-motion data, which enabled us to investigate their characteristics and generations. This study aimed to obtain an intuitive understanding of the characteristics of near-fault strong ground motions and their relationship with kinematic source properties using a simple source model. Synthetic ground motions were calculated using the discrete wave number method and compared with observed ground motions. The observed strong motions were used after a time correction based on the first P-wave arrival. Results showed that near-fault pulse-like velocities (0.02–0.6 Hz) were generally explained by the simple kinematic source model that assumed a continuous rupture propagation along the faults. Comparisons at individual sites indicate the various rupture properties of the fault segments. Near-fault ground motions along the Narli segment were explained by the fault plane along the surface trace and not by aftershock distribution. A supershear rupture propagation speed is necessary for a part of the Amanos segment. Rupture stops and restarts were implied near the stepover along the Amanos segment. Empirical site factors estimated by generalized inversion techniques did not show peaks around the frequency of observed large velocity pulses, indicating that the velocity pulses were mainly generated by the source. The model bias and uncertainties were also examined to supplement the simplified subsurface structure used in the simulation. Our results will help to associate the qualitative and quantitative aspects of the kinematic source process with near-fault strong ground motions.

Characteristics of strong ground motions and structural damage patterns from the February 6th, 2023 Kahramanmaraş earthquakes, Türkiye

Characteristics of strong ground motions and structural damage patterns from the February 6th, 2023 Kahramanmaraş earthquakes, Türkiye

Generation of synthetic spectrum-compatible bi-directional ground motions with specific directionality

Ground motions compatible with the maximum-direction response spectrum (RotD100) condition are emphasized in performance-based design and seismic risk assessment of civil structures in different regions. Due to the complex nature and uncertainty of earthquakes, the comprehensive inclusion of essential characteristics of ground motions is crucial. Recently, the directionality effect of horizontal bi-directional ground motions, representing the variation of the response spectral ordinate amplitude along various directions, on the seismic performance assessment of certain structures or key structural elements has received increasing attention in seismic analysis. However, from the seismic design perspective, there is still a lack of a method that can generate bi-directional ground motions comprehensively reflecting the directionality effect. This study focuses on the correction of ground motion directionality when generating synthetic bi-directional ground motions for a given target response spectrum. An efficient and integrated algorithm is proposed. To demonstrate the applicability of the proposed algorithm, three different target RotD100 response spectra are determined according to modern seismic design codes, and 20 pairs of bi-directional ground motions are generated using the proposed algorithm for each target spectrum. It is shown that with the proposed algorithm, the generated motions present a close match to the target RotD100 response spectrum and meet the specific directionality requirement. In addition, due to the iterative modification of the envelope function, the energy of the generated ground motion is also consistent with the scaled natural records.

Reconstructing Impact of the 1867 Ionian Sea (Western Greece) Earthquake by Focusing on New Contemporary and Modern Sources for Building Damage, Environmental and Health Effects

The 4 February 1867 Cephalonia (Western Greece) earthquake is the largest in the Ionian Islands and one of the largest in the Eastern Mediterranean. However, it remained one of the least studied historical events. For reconstructing this earthquake, we reevaluated existing knowledge and used new contemporary and modern sources, including scientific and local writers’ reports and books, local and national journals, newspapers, and ecclesiastical chronicles. The extracted information covered the earthquake parameters, population impact, building damage, and earthquake environmental effects (EEEs). The earthquake parameters included the origin time and duration of the main shock, epicenter location, precursors, aftershocks, and characteristics of the earthquake ground motion. The population impact involved direct and indirect health effects and population change. Building data highlighted the dominant building types and the types, grades, and distribution of damage. The EEEs included ground cracks, landslides, liquefaction, hydrological anomalies, and mild sea disturbances. Field surveys were also conducted for validation. The quantitative and qualitative information enabled the application of seismic intensity scales (EMS-98, ESI-07). The study concluded that since the affected areas were mainly composed of post-alpine deposits and secondarily of clay–clastic alpine formations with poor geotechnical properties, they were highly susceptible to failure. Effects and maximum intensities occurred in highly susceptible areas with a rich inventory.

The probabilistic performance-based seismic assessment of concrete bridge piers with SMASC reinforcing bars: Effect of pulse-like ground motion and vertical load-to-capacity ratio

Concrete bridge piers are critical components of bridge structures and their performance under seismic loading is of utmost importance. Traditional reinforced concrete bridge piers have shown limitations in terms of residual deformations and seismic resilience. This has led researchers to explore alternative reinforcement materials, such as Shape Memory Alloy (SMA) coupled with steel reinforcing bars, which have demonstrated promising attributes like energy dissipation as well as self-centering capacity. This study aims to fill this gap by evaluating the performance of concrete bridge piers with SMA-Steel coupled (SMASC) reinforcing bars under various intensities of vertical gravity loads and the action of pulse-like ground motion components, throughout a probabilistic framework. To this end, a group of bridge piers with different reinforcement types, including pure steel and SMASC are considered. These piers are subjected to 55 near-fault (NF) pulse-like records as well as 32 far-fault (FF) ground motions, throughout the Incremental Dynamic Analysis (IDA). The influence of distinct frequency components is analyzed by decomposing NF records into low-frequency pulses and high-frequency residual components. Also, the role of pulse to the 1st modal period of the piers (Tp/T1) is investigated by evaluating the piers’ response under the action of NF records, which were clustered into four groups. Results were assessed by evaluating the intensity measure and capacity of the studied piers at the desired performance objectives according to the FHWA manual. Moreover, the mean annual frequencies of exceeding performance limit states and the confidence levels of meeting performance objectives are studied. The results of the study indicate that the dominance of the component of NF ground motions depends on factors such as the intensity of gravity loads, ground motion characteristics, and the Tp/T1 ratio. The components of NF records within certain clusters of the Tp/T1 ratio are necessary for accurate response assessment, while FF records can be used for conservative design purposes, depending on the level of ground motion intensity and the intensity of applied gravity load. The SMASC-reinforced piers with specific λ factors (i.e. λ = 0.5 or λ = 1.0) and low intensity of gravity loads lead to a higher (1.6 times higher) mean annual frequency of exceeding a limit state, compared to pure steel rebars. Also, the confidence levels for meeting performance objectives vary depending on the ground motions, but, as gravity load intensity increases, confidence levels decrease, particularly for piers with a λ factor of 0.5.

Probabilistic damage evaluation of isolated steel box-girder bridge excited by near-field earthquakes

Modern civilization regards bridges as lifelines, and their failure due to significant earthquakes in the past has raised concerns about their seismic vulnerability. The effect of near-fault earthquakes on the bridge vulnerability is a significant problem that has not been well explored. The present work utilizes fragility analysis to assess the probabilistic seismic risk evaluation of a continuous isolated steel box girder bridge exposed to near-field ground motions (NFGMs). Probabilistic seismic demand models (PSDMs) of the bridge are explored to reliably associate key demand and intensity measures to capture the seismic performance of the system. This study examines a steel box girder bridge in Guwahati, India, isolated with lead rubber (LRB) bearings and a friction pendulum system (FPS). To achieve a comparable seismic response, each bearing system is designed for a similar isolation period. The PGV/PGA ratio is a useful metric for classifying ground motion suites and estimating their frequency content by classifying the input excitations into low- and high-frequency motions. To perform probabilistic seismic risk assessments, two ensembles of forward directivity records with PGV: PGA >160 cm/s/g (high frequency) and PGV: PGA <160 cm/s/g (low frequency) are considered. Moreover, one ensembles of near-field incorporating the fling effect and far-field are also utilized. The failure probability under the seismic hazards for isolated bridges is assessed utilizing different damage measures (DM) such as maximum displacement ductility of pier (DDP), displacement of deck (MDD), drift ratio of pier (PDR), and rotational ductility (RDP). The near-field earthquake damage probability is significantly impacted by the PGV/PGA ratio, as a high PGV/PGA ratio results in a greater probability of damage than a low PGV/PGA ratio. In conclusion, guidelines are provided that will help the design engineers for designing bridges with LRB and FPS isolators for seismic isolation in the event of varying ground motion characteristics.

Optimum design parameters for double concave friction pendulum system considering ground motion characteristics and friction heating

Optimum design parameters for double concave friction pendulum system considering ground motion characteristics and friction heating

Seismic response prediction and fragility assessment of high-speed railway bridges using machine learning technology

Nowadays, reliable and accurate prediction of the seismic response of high-speed railway bridges and then rapid assessment of their seismic fragility is crucial for the risk assessment of regional high-speed railway bridges. However, the traditional seismic fragility assessment based on nonlinear time history analysis (NTHA) has a characteristic of high computational cost. Therefore, this study explores different machine learning (ML) methods to develop a reliable rapid seismic fragility assessment framework for high-speed railway bridges. Taking a typical high-speed railway continuous (HRC) bridge as a case, a large number of numerical model and ground motion pairs considering various structural and ground motion features are established. Then, the two types of models including six popular ML methods are investigated, in which Lasso regression, artificial neural network (ANN) and support vector machine (SVM) are single models, and random forest (RF), extreme gradient boosting (XGBoost) and light gradient boosting machine (Light GBM) are ensemble models. A database containing a large number of seismic responses of different components is established through the NTHA to develop prediction models, and the optimal hyperparameters of different ML models are determined by ten-fold cross-validation and grid search. The study results show that the ensemble model can reliably predict the seismic response of the HRC bridge compared with the single model, and the XGBoost model is recommended for seismic fragility assessment. On this basis, the optimal ML model obtained by the performance evaluation is applied to develop seismic fragility curves of the HRC bridge, which are compared with the fragility curves based on NTHA. The results indicate that the XGBoost model can effectively replace the traditional NTHA for rapid seismic fragility assessment of the HRC bridge, which can achieve a sufficient assessment level while saving calculation costs.