Ground Motion Research Articles

Three-Dimensional Numerical Analysis of Seismic Response of Steel Frame–Core Wall Structure with Basement Considering Soil–Structure Interaction Effects

In recent years, numerous studies highlighted the crucial role of the soil–structure interaction (SSI) in the seismic performance of basement structures. However, there remains a limited understanding of how this interaction affects buildings with basement structures under varying site conditions. Based on the three-dimensional (3D) numerical analysis method, the influence of the SSI on the seismic response of high-rise steel frame–core wall (SFCW) structures situated on shallow-box foundations were investigated in this study. To further investigate the effects of the SSI and site conditions, three types of soil profiles—soft, medium, and hard—were considered, along with a fixed-foundation model. The results were compared in terms of the maximum lateral displacement, inter-story drift ratio (IDR), acceleration amplification coefficient, and tensile damage for the SFCW structure under different site conditions, with both fixed-base and shallow-box foundation configurations. The findings highlight that the site conditions significantly affected the seismic performance of the SFCW structure, particularly in the soft soil, which increased the lateral deflection and inter-story drift. Moreover, compared with non-pulse-like ground motion, pulse-like ground motion resulted in a higher acceleration amplification coefficient and greater structural response in the SFCW structure. The RC core wall–basement slab junction was a critical region of stress concentration that exhibited a high sensitivity to the site conditions. Additionally, the maximum IDRs showed a more significant variation at incidence angles between 20 and 30 degrees, with a more pronounced effect at a seismic input intensity of 0.3 g than at 0.2 g.

The source characteristics and ground motion parameters measure with structural damages of Mw6.4, 2018 Hualien earthquake Taiwan from GPS data

On February 6, 2018, a moderate earthquake with a magnitude of ML6.2, known as the 0206 earthquake, struck Hualien in eastern Taiwan, resulting in significant destruction, 17 fatalities, and over 300 injuries. This research investigates the kinematic source model of the earthquake by inverting coseismic Global Positioning System (GPS) displacement recordings from the area surrounding the epicenter. The inverted source model indicates that most asperities above the hypocenter are minor, with large slips occurring at depths shallower than 10 km, which contributed to the severe damage in Hualien City. Additionally, we calculated the Coulomb stress changes at depths ranging from 6 km to 14 km, revealing that most aftershocks within a month occurred within a Coulomb stress range of >0.1 bar, extending from shallow to deep and from southwest to northeast of the epicenter. Notably, despite high Coulomb stress, few aftershocks were generated in the western part of the epicenter, suggesting a seismic gap that may lead to a more significant earthquake in the future. Two years later, two moderate earthquakes (ML=5) occurred at depths of 17.53 and 12.1 km in the region where Coulomb stress had increased, validating our predictions. Furthermore, by applying the derived seismic source model and a stochastic semi-empirical technique to assess five parameters related to building damage, we found that all damaged buildings were within the danger alert level, with some located along the Meilun Fault, indicating that the destruction was due to the combined effects of the 0206 earthquake and the fault's induced movement.

Optimized evaluation of ground motion intensity measures for the prefabricated cantilevered roadbed structure in mountainous areas

The prefabricated cantilevered roadbed structure (PCRS) is a novel design that addresses the significant excavation and embankment volume associated with traditional mountainous roadbeds. The seismic performance of it has not yet been fully investigated. Seismic fragility analysis is one of the main methods for assessing the damage pattern of a structure, and the ground motion intensity measure (IM) is a key parameter affecting the damage assessment results. To this end, a finite element model is first conducted based on structural load-bearing capacity verification experiments, relying on a prefabricated cantilevered roadbed structure in a mountainous region. Then, the nonlinear time-history analysis of the structure was carried out, and 20 common IMs were selected and compared from the perspectives of practicality, efficiency, proficiency, and sufficiency. Finally, the seismic fragility analysis of the structure is conducted based on the optimal IM. The analysis results indicate that the peak ground acceleration of response spectrum (PSA) is the optimal IM for such structures. Quadratic fitting exhibits higher accuracy compared to linear fitting. Among all components, fixed-end foundations are identified as the most vulnerable. Anchored reinforcement remains elastic throughout the seismic response calculations and suffers little damage.

Spatiotemporal Pattern Detection of Ground Deformations Induced by Extreme Rainfall using InSAR EGMS: The case of Cortina d’Ampezzo after Vaia Storm

Abstract. During October 2018 Vaia Storm occurred in the north of Italy, resulting in a huge amount of rainfall that was recorded. The focus here is given to Cortina d’Ampezzo area in the Veneto Region, due to the high importance of the area from touristic and sport perspectives and the presence of landslides. This study is devoted to investigating the impact of the severe sudden rainfall caused by this extreme atmospheric disturbance on the trends of ground deformations over this area, adopting a risk assessment perspective. The ground deformation Time Series (TS) have been derived from the European Ground Motion Service (EGMS) Ortho products derived from Sentinel-1 InSAR data. Then, according to the daily precipitation records and considering the time of this event as a turning temporal point, the TSs of all data points have been divided into pre-event and post-event phases. After obtaining the displacement rates in each phase, the quantified magnitude of changes in the pre-post rates has been calculated through a proposed simple formulation. The spatial distribution of the deformation parameters (i.e., pre-event displacement velocity and change in the pre-post displacement velocities) is found to be a practical tool for gaining comprehensive knowledge regarding the ground deformation and the effect of the extreme weather conditions on the displacement trends over the area. Furthermore, the positioning of the occurrence of deformation parameters has been compared to the landslide inventory of the area. The results showed that the extreme rainfall caused severe acceleration of displacements, more significant in horizontal East-West direction rather than vertical Up-Down direction. Some slight deceleration has been also detected. No initiation (or reactivation) of the stable zones was witnessed, and the portions with high magnitude of pre-event velocities are the ones whose displacement rates have been remarkably modified due to the severe event.

Ground motion model for acceleration response spectra using conditional-generative adversarial network

Ground motion model for acceleration response spectra using conditional-generative adversarial network

Probabilistic seismic demand analysis for bridges based on earthquake scenarios

The current probabilistic seismic demand analysis of structures predominantly considers the correlation between the ground motion intensity measure (IM) and structural seismic demand, but fails to effectively capture regional source characteristics. It is essential to integrate earthquake engineering with seismic response of structures using source parameters to quantify regional ground motion uncertainties and determine appropriate IMs. This study presented a probabilistic seismic demand analysis method for structures based on earthquake scenarios, characterized by 1) utilizing the stochastic finite-fault method to simulate ground motions at a specific site instead of employing multiple seismic waves from different source areas, and 2) incorporating the correlation between IMs and the key indicator of source parameters, stress drop, into the probabilistic seismic demand analysis. Taking a continuous girder bridge as an example, the performance of the stress drop and 14 individual IMs in predicting the longitudinal seismic demand and reflecting source characteristics was evaluated. The results revealed that it was difficult to establish a reliable relationship between the stress drop and structural seismic demand. Owing to the complexities of ground motion and structural parameters, the individual IMs were challenging to effectively capture their characteristics simultaneously. Therefore, composite IMs have been proposed to cover the characteristics of both the source and seismic demand. The optimal solutions were derived using a multi-objective genetic algorithm, which exhibited desirable capabilities in both aspects, providing a comprehensive guide for the seismic performance assessment of regional transportation networks.

Deep learning-based ground motion inversion through recursive structural acceleration response using DRA-LSTM Net

Known ground motion time history is crucial for the precise numerical analysis of structural response and evaluation of its safety. This study introduces the Deep Recursive Attentive Long Short-Term Memory Network (DRA-LSTM Net), a framework designed for the inversion of ground motion (GM) from recorded structural response (SR) during an earthquake. By integrating an attention mechanism in network along with a strategic recursive approach for dataset management, the DRA-LSTM Net achieves high precision in GM inversion. The method incorporates a novel pre-processed matrix format to capture unique recursive behaviour patterns in GM data, enhancing the model’s training, validation efficiency, and prediction accuracy on unseen test datasets. Case studies on single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) structures highlight the method’s potential for real-time and high-precision GM inversion. Additionally, transfer learning was implemented to fine-tune the pre-trained model with real-world sensor data and structural responses from different floors, demonstrating the model’s adaptability and ability to retain high prediction accuracy across diverse datasets. This approach is particularly useful for regions with limited seismic instrumentation but equipped with structural response sensors.

A comprehensive investigation into the effect of vertical component of ground motion on response of sliding unanchored blocks

This study aims to quantify the effect of the vertical component of ground motion on the seismic response of sliding rigid blocks, in terms of maximum and residual displacements. A multi-stripe analysis approach is employed to examine the response with and without the vertical component under various levels of ground motion intensity. First, a parametric study is conducted using a Coulomb friction model with a constant coefficient of friction that does not account for any dependence of the friction coefficient on velocity or pressure. It is shown that the effect of the vertical component on maximum and residual displacement is more pronounced for ground motions that can barely initiate sliding. Moreover, the influence of the vertical component on the residual sliding displacement is higher than that on the maximum displacement. Subsequently, to account for the friction coefficient dependence on pressure and sliding velocity, two interface cases (steel-steel, and concrete-concrete) are analyzed. It is found that even when a variable coefficient of friction is used, the overall trends observed for a constant coefficient of friction do not change.

Advantages of base isolation in reducing the reliability sensitivity to structural uncertainties of buildings

Base isolation is among the most effective techniques for safeguarding buildings against earthquakes. Similar to non-isolated buildings, the seismic performance of a base-isolated building can vary not only due to the inherent record-to-record variability in earthquake ground motions but also the non-negligible influence of uncertainties associated with structural properties. Introducing an isolation layer of isolation bearings and energy dissipators to the building further adds to the source of uncertainties, raising the question regarding the reliability of base isolated buildings. This study proposed a methodology that employs the probability density evolution method (PDEM) with local time grid refinement to evaluate the reliability of a base-isolated structure and its non-isolated counterpart under earthquakes of various magnitudes and fault distances. It allows for simultaneously considering the uncertainties of various sources in the reliability analyses with affordable computational cost, including those of the ground motions, of the structural nonlinearity, and of the ultimate states that define the reliability. The results show that the base-isolated building exhibits much higher reliability than its non-isolated counterpart in all cases even if the ultimate behavior such as the isolator fracture or the impact to the retaining wall is considered. The added source of uncertainties in the isolation layer does not exhibit a notable impact on the reliability. In contrast, the seismic reliability of base-isolated buildings is much less sensitive to structural uncertainties than that of their non-isolated counterparts.

Stochastic Generation of Peak Ground Accelerations Based on Single Seismic Event Data for Safety Assessment of Structures

The Korean Peninsula, characterized by low-to-moderate seismicity, faces a shortage of strong ground motion records, posing challenges for the seismic safety assessment of critical infrastructures. Given the rarity of large-magnitude earthquakes, generating a variety of earthquakes with rational values of Peak Ground Acceleration (PGA) is essential for robust seismic fragility and risk analysis. To address this, a new stochastic approach is proposed to simulate artificial earthquakes at multiple source-to-site distances and derive the probability distribution of PGA based on recorded data from a single seismic event. Two key source parameters, seismic moment and corner frequency, are treated as random variables with a negative correlation, reflecting their uncertainties and dependence on source-to-site distance. The Monte Carlo simulation with copula sampling of the key source parameters generates Fourier spectra for artificial earthquakes, which are transformed into the time domain to yield PGA distributions at various distances. A comparison with recorded data shows that the proposed method effectively simulates ground motion intensities, with no statistically significant differences between the simulated results and recorded data (p > 0.05). The present method of determining PGA distributions provides a robust framework to enhance seismic risk analysis for the safety assessment of structures.

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.

Estimation of Earthquake-Induced Direct Economic Losses of Portfolio Buildings Based on Seismic Fragility Surface

Regional seismic loss assessment is crucial for developing earthquake emergency plans, which can significantly reduce casualties and socioeconomic losses in urban communities. Due to the complex types of building structures in a region, it is not possible to accurately measure the damage state of building structures using a single scalar ground motion intensity measure (IM). To more accurately reflect the responses of structures under earthquake excitations, the seismic fragility surface models for 4 typical RC frame structures are developed based on the vector ground motion IMs. The results indicate that the fragility surface model offers a superior level of precision in capturing the inherent uncertainty of ground motion and subsequently determines a more precise probability of structural failure, outperforming traditional single scalar ground motion IM fragility curve models. This enhanced accuracy holds significant implications for regional loss assessment. To fully consider the spatial cross-correlation of vector ground motion IMs, based on the established ground motion database of 8778 records, principal component analysis (PCA) and geostatistical analysis methods are used to construct a spatial cross-correlation influence field that simultaneously considers multiple ground motion IMs to simulate more realistic earthquake scenarios. Considering the impact of uncertainty in loss ratio on earthquake-induced direct economic loss estimation results, the maximum entropy technique is used in conjunction with an improved Latin Hypercube Sampling (LHS) to achieve spatially uniform sampling of loss ratio (LR). This study proposes an assessment framework for regional seismic direct economic loss for portfolio buildings based on the seismic fragility surface model considering spatial cross-correlation and uncertainty of loss ratio. The proposed framework is verified by numerical examples, and it is proven that the regional portfolio buildings considering multiple ground motion IMs is more reliable and robust than those merely considering a single ground motion IM, and when spatial cross-correlation is not considered, the probability of serious economic losses will be underestimated.

Seismic drifts of buildings through deep neural networks circumventing incremental dynamic analysis: Aims and pitfalls for robust and reliable predictions

In the field of seismic response assessment, simplified procedures have long been pursued to circumvent the rigorous, state-of-the-art nonlinear response history analysis (NRHA) and incremental dynamic analysis (IDA) that typically require tedious computations. To this end, this study investigates artificial neural networks (ANN) as prediction models to bypass IDA and quickly and reliably determine the structural drift responses of buildings, without employing surrogate models or analysis techniques that lower the quality of the estimates. Three designs of such prediction models are identified in terms of their scope, implementation and corresponding database, commonly employed in research, while identifying common pitfalls in their design. The investigations involve ten steel-frame multi-story structures, considering cyclic and in-cycle material deterioration and P-Delta phenomena. More than 17-thousand recorded ground motions (GM) are employed to perform IDA on these frame-building models, resulting in a unique database of responses ranging from linear to collapse, with more than 3-million NRHA. Based on this database, a prediction model is developed for each building, capable of predicting the IDA under a GM excitation not included in the database. Additionally, another prediction model is developed that can predict the IDA of a building under a GM excitation, both of which are not included in the database. These investigations show that ANN are excellent tools to circumvent IDA and capture the record-to-record uncertainty by rapidly and reliably predicting structural drifts ranging from linear responses to the collapse limit state, while building-to-building predictions are additionally feasible if the employed database is appropriate.

Development of a 3D Geologic Model Used in the Seismic Hazard and Liquefaction Hazard Analysis of Madison County, Tennessee

ABSTRACT Madison County of western Tennessee is approximately 100 km southeast of the New Madrid seismic zone and, thus, is subject to potentially dangerous seismic shaking and liquefaction events. Holocene floodplain alluvium and associated Pliocene/Pleistocene terraces of the Forked Deer and Hatchie Rivers cover a large part of Madison County. Underlying these unconsolidated sediments are approximately ∼650 m of poorly consolidated Paleogene and Cretaceous terrestrial and marine sediments, which are underlain by Paleozoic limestone bedrock. Uniform regional geology used in the 2014 United States Geological Survey seismic hazard maps assumes a homogenous seismic velocity structure above bedrock. Three-dimensional geologic mapping of the ∼700 m of Quaternary, Neogene, Paleogene, and Cretaceous sediments conducted in this study provides a more detailed velocity structure, resulting in a more accurate and detailed map of the expected ground motions in Madison County during future earthquakes. In addition, the surface geologic map reveals regions that are susceptible to liquefaction. This paper discusses the methods used to construct the surface geology map, subsurface structure contour maps, isopach maps, and 3D geologic model used in the seismic hazard and liquefaction hazard analyses of Madison County, Tennessee, and summaries of the results of these analyses are presented.

Effects of human and tectonic activities on groundwater in the upper Yellow River terraces of the loess Plateau

Terraces of the Yellow River are among the most crucial geomorphological landforms pertaining to human survival on the Loess Plateau. After more than half a century of surface flood irrigation, rapid rises in groundwater levels have led to frequent geological disasters, causing a rapid short-term evolution of Yellow River terraces, and an increasingly serious contradiction between this rapid evolution and human survival. However, the process by which water transfers through the thick loess vadose zone and causes a rapid groundwater response within months or years remains controversial. Using the Heitai platform of Yellow River terrace IV as an example, we found, on the basis of regional geological surveys, that at least 112 tectonically-induced cracks have developed in this area. We contend that the superimposed effects of earthquake ground motions from historical and ancient earthquakes since the Late Pleistocene have driven the development of such densely distributed cracks in the loess layer. These cracks, together with a series of fault planes generated by NE-directed extrusive stress at the regional scale, constitute a potential network of preferential channels for water transport within the Heitai platform. Combined with the results of a large-scale in situ ponding test and electrical resistivity tomography, we found that this network of cracks helps to establish catchment areas within the loess layer, thereby increasing soil saturation at a more extensive spatial scale, which may then increase the overall movement velocity of the wetting front. We semi-quantified the efficiency of the crack network in enhancing the recharge of surface water to groundwater, and suggested that the impact of human and tectonic activities substantially shortens the response time of groundwater to surface water, with the reduced time far exceeding one order of magnitude. The results of a field investigation of structural traces and terrace groundwater after the Ms 6.2 Jishishan earthquake on 18 December 2023 further emphasize that a causal mechanism of human and tectonic activities leading to the rapid short-term evolution of groundwater distribution patterns may be universally applicable to all Yellow River terraces on the Loess Plateau. This study mitigates the long-standing controversy concerning the mode of surface water infiltration, including piston flow and preferential flow, and the infiltration medium by which rapid surface water recharge to groundwater occurs in loess areas over short time periods.

Response of Base-Isolated Building with Secondary System under Earthquakes and Pulse-Type Ground Motions

Response of Base-Isolated Building with Secondary System under Earthquakes and Pulse-Type Ground Motions

Seismic input method coupling ground motion and fault dislocation for near-fault engineering sites

Seismic input method coupling ground motion and fault dislocation for near-fault engineering sites

Development of a novel rotational metallic damper as a dissipative connector in rocking steel core systems: Cyclic behavior and seismic demand

Rocking systems with stiff rocking cores have been proposed as a promising solution for mitigating concentrations of story drift and enhancing structural seismic resilience. This study introduces a novel rotational metallic damper, termed the rotational steel rod damper (RSRD), developed for use in rocking steel core systems to improve energy dissipation capacity. The configuration, working mechanism, and application of the RSRD in rocking steel core systems are described. Three critical energy-dissipating elements, low-yield-point steel rods with different edge shapes, were isolated from the RSRD and tested to validate the efficacy of an optimal hourglass shape, defined through cubic equations, in enhancing low-cycle-fatigue performance. Two RSRD specimens were then tested and analyzed, focusing on failure modes, cyclic behavior, rotational strength, and energy dissipation. Both specimens demonstrated positive and stable hysteretic performance and energy dissipation capacity, with maximum equivalent viscous damping ratios of 0.51 and 0.53. Subsequently, structural models of seven steel frames were developed in OpenSees for case studies. Nonlinear response history analyses were performed under 20 ground motion records scaled to DBE and MCE levels. Dynamic behaviors, particularly the seismic demand on RSRDs, were examined. Numerical results confirmed the efficacy of the RSRD in mitigating inter-story drift. The location of the pivoting point of the rocking core significantly influenced the rotational demand on the RSRD. The proposed RSRD presents an innovative energy-dissipating alternative for the development of rocking steel core systems.

Effect of Pulse-like Ground Motions with Various Pulse Indicators on the Seismic Fragility of Bridges

Effect of Pulse-like Ground Motions with Various Pulse Indicators on the Seismic Fragility of Bridges

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.