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The NGA-Subduction Project is a multi-year, multidisciplinary project with the goal of developing a ground motion database and ground motion models for global subduction zone earthquakes including those in Japan, Taiwan, Cascadia, Alaska, New Zealand, South America, and CentralAmerica. Our ground motion model development is currently at the stage of identifying regional trends in path terms. We use a combination of data inspection and regression techniques to distinguish path effects in the data, including differences between interface and inslab events, forearc/back-arc effects, regional effects, and azimuthal effects. Our approach to modeldevelopment is to first develop a path model capturing these effects, then to investigate source and site effects. The parameterization of functional form is guided in part by the scaling expected by a generic equivalent point-source stochastic model. We expect regionalization in path and will investigate further regionalization in site response and in overall model bias.

Abstract This technical report explains details on the results of the investigation to build a subsurface structural model and a selection of earthquake data at the target site of the blind prediction exercise for “The 6th International Association of Seismology and Physics of the Earth’s Interior/International Association of Earthquake Engineering International Symposium on the Effect of Surface Geology on Seismic Motion (ESG6).” The selection process of the target site in the Kumamoto Plain, Japan, in ESG6 was explained with a historical review of the blind prediction tests in the previous ESG conferences. We have collected existing subsurface structural and earthquake data and conducted geophysical and geotechnical surveys in and around the target site to generate important velocity structure and the earthquake data used in the blind test. Microtremor data were obtained in triangular arrays ranging in side lengths from 1 to 962 m, and active surface wave data were derived along a 36-m line at the site. These data were provided for the prediction of a subsurface structural model in the first step of the blind prediction exercise. We also conducted a velocity logging in a borehole to a bottom depth of 39 m at the site and laboratory tests of soil samples from the borehole. We constructed a velocity profile of the shallow and deep sedimentary layers from a combination of the geophysical and geotechnical data at the site, and validated it by comparing the characteristics of the ground motion data from the moderate event. This “preferred velocity model” was provided as a standard model to the participants in the second and third steps of the blind prediction test to predict the earthquake ground motions of a moderate event and the mainshock of the 2016 Kumamoto earthquake. Graphical Abstract

Abstract Rapid and accurate prediction of peak ground acceleration (PGA) is an important basis for determining seismic damage through on-site earthquake early warning (EEW). The current on-site EEW uses the feature parameters of the first arrival P-wave to predict PGA, but the selection of these feature parameters is limited by human experience, which limits the accuracy and timeliness of predicting peak ground acceleration (PGA). Therefore, an end-to-end deep learning model is proposed for predicting PGA (DLPGA) based on convolutional neural networks (CNNs). In DLPGA, the vertical initial arrival 3–6 s seismic wave from a single station is used as input, and PGA is used as output. Features are automatically extracted through a multilayer CNN to achieve rapid PGA prediction. The DLPGA is trained, verified, and tested using Japanese seismic records. It is shown that compared to the widely used peak displacement (Pd) method, the correlation coefficient of DLPGA for predicting PGA has increased by 12–23%, the standard deviation of error has decreased by 22–25%, and the error mean has decreased by 6.92–19.66% with the initial 3–6 s seismic waves. In particular, the accuracy of DLPGA for predicting PGA with the initial 3 s seismic wave is better than that of Pd for predicting PGA with the initial 6 s seismic wave. In addition, using the generalization test of Chilean seismic records, it is found that DLPGA has better generalization ability than Pd, and the accuracy of distinguishing ground motion destructiveness is improved by 35–150%. These results confirm that DLPGA has significant accuracy and timeliness advantages over artificially defined feature parameters in predicting PGA, which can greatly improve the effect of on-site EEW in judging the destructiveness of ground motion.

The seismic performance of arch bridges is dependent on the strength of bridges and the built-on soil site types. To investigate the influence of soil-structure interaction (SSI) on the seismic response of an arch-foot arch bridge with a sand-gravel soil site, several shaking-table tests for a 1/90 downscaled arch-foot model placed on sand-gravel soil were conducted based on the engineering prototype of the third Pingnan Bridge with a long-span concrete-filled steel-tube (CFST) arch bridge. The test results showed the following: (1) The seismic acceleration response of both the sand-gravel soil site and arch foot were amplified, which is related to both the non-linear dynamic behavior of sand-gravel soil and frequency-spectrum characteristics of the input seismic waves. (2) The sliding displacement of the arch foot under strong ground motions increased accompanied with a settlement of the soil surface. However, the safety risk of the arch foot may be underestimated in seismic design. (3) The dynamic shear stress-strain loops of sand-gravel soil exhibited a high-energy dissipation capacity, and the energy-transfer mechanism at the interface between the model soil and arch foot was integrated. The results obtained are expected to provide insights into the dynamic interaction behavior of the gravel soil and arch bridge systems and the seismic design of arch bridges built on sand-gravel soil sites in practical engineering. [ABSTRACT FROM AUTHOR]

To simulate the ground motions of 2D arbitrary sites subjected to plane SV waves, we put forward a numerical method combining the multi-transmitting formula (MTF) and spectral element method (SEM). The investigation results demonstrate a high simulation accuracy of the presented method. Most importantly, the MTF exhibits a notably superior stability performance in the SEM than in traditional finite-element method (FEM). Without any additional treatment, high-frequency instability does not occur when controlling the Courant number value C and the ratio between the MTF parameter S and C; the upper limit value of this ratio is significantly smaller than that of the SH-wave problem. In addition, a preliminary stability criterion for the MTF in SEM is given based on the homogeneous half-space model. For low-frequency stability, the occurrence time of instability is considerably lagged when no interventions are adopted. Moreover, the low-frequency instability can be readily eliminated by using smaller modification parameter in the SEM compared with the FEM. [ABSTRACT FROM AUTHOR]

Seismic response of cross interchange subway stations in soft soil is studied through dynamic time history analysis. The inhomogeneous deformation and internal forces of the structure are examined under moderate earthquakes, considering intersecting effects and ground motion frequency. Findings indicate that the intersecting part of two-story and three-story sections creates a rigid connection, increasing stiffness and unique deformation patterns. This leads to an abrupt distribution of internal forces and significantly affects lateral drift ratio, requiring careful consideration in seismic design. Low-frequency ground motions demonstrate distinct heterogeneity in racking deformation distribution. Insights contribute to improved seismic design of soft soil subway stations. [ABSTRACT FROM AUTHOR]

A five story (G + 4) residential structure is positioned on 15°, 30°, and 45° slopes to be explored in this study in terms of vulnegrability and compared to the same structure resting on flat ground 0°. The RC frame models were constructed in accordance with Eurocode 8 and then subjected through a series of 10 ground motion records. In accordance with Vision 2000 performance limit states (PLS), this research provided the use of nonlinear static analysis (NL-SA) and nonlinear dynamic analysis (NL-DA) methodologies to assess seismic risk performance and vulnerability of structures resting on various slope angles. Incremental Dynamic Analysis (IDA) and Pushover Analysis (POA) capacity curves, as well as the fragility curve and the collapse margin ratio, are generated for the four selected models (M0, M15, M30, M45). The results show that the RC frame model M45, located on a 45°-degree incline, is the most vulnerable to damage. The risk of damage to the RC frame M0 is minimal because it is not elevated off the ground. To this end, the relative degrees of vulnerability among the frames were determined to be as follows: M45 > M30 > M15 > M0. [ABSTRACT FROM AUTHOR]

Predictive displacement models (PDMs) based on the Newmark sliding block method have been widely applied in earthquake-induced landslide hazard assessment (ELHA). Pulse-like ground motions (PLGMs) can cause significant structural damage. In this study, four PDMs that consider the probability of occurrence of PLGMs (PP) were created for strike-slip and non-strike-slip events. The results demonstrate that the model with PP has greater accuracy in non-strike-slip events than the model without PP, however, the effect of PP may be disregarded in strike-slip events. This result is applicable to the ELHA and can provide a basis for post-earthquake emergency and geological disaster mitigation. [ABSTRACT FROM AUTHOR]

The damage assessment of numerous buildings after the earthquake is still a challenge by traditional methods as it requires a significant amount of time and resources for carrying out building-by-building seismic damage assessment. This study presents the machine learning (ML)-based damage assessment of buildings after an earthquake. The applicability of the machine learning model will be limited considering only structural properties or ground motion characteristics. Thus, in this study, the applicability of different machine learning techniques is explored using real-world earthquake damage datasets considering both the structural properties and ground motion characteristics. Initially, the entire dataset is used to train the ML models to classify the building's damage into five categories ranging from null to slight damage to collapse. Later, the dataset is divided into three traffic light damage tags: green, yellow, and red. The performance of the ML model to classify the building's damage into three damage tags is found better than that of classifying it into five damage categories. This research aims to be beneficial in planning and decision-making for emergency response and recovery following an earthquake. [ABSTRACT FROM AUTHOR]

The sliding base (SB) technique is an efficient and economical approach to mitigating seismic damage of low-rise buildings in rural areas. Based on the previous work on single-story SB structures, this study investigates the earthquake forces acting on multistory SB structures. Response history analyses of SB structures subjected to three-component earthquake excitations were performed. The computed results indicate that the increase in the number of stories tends to reduce the mean value of the peak base shear but has little influence on the coefficient of variation of the peak base shear. The peak story shears of a multistory SB structure subjected to an earthquake ground motion are reached at different time instants for different stories. The distributions of the equivalent lateral forces for SB structures with different story numbers follow the same trend, and the equivalent lateral forces tend to concentrate at the upper floors as the normalized peak ground acceleration and mass ratio increase. Simplified equations for computing the distribution of equivalent lateral forces were developed for design purposes. [ABSTRACT FROM AUTHOR]

Affected by the near-fault pulse-like ground motions, the tunnels emerged in adverse geology, especially in an inhomogeneous strata are more vulnerable. However, the quantitative index effects on tunnel response during pulse identification are unclear and the propagation features of the near-fault pulse-like waves in a soft soil interlayer site are seldom revealed. In view of this, an improved energy-based pulse identification was used in this study to quantitatively extract the potential pulse energies emerged in velocity time-histories of ground motions. Subsequently, a series of numerical simulations were carried out to consider the critical parameters of soft soil layer and input ground motions. Finally, the dynamic response of the tunnel and interaction differences of soil and tunnel subjected to three types of ground motions were revealed. The result showed that the near-fault pulse-like ground motions pose a commonly severe damage to the tunnel, especially in the high pulse period ground motions based on the energy-based pulse identification method. The pulse energy of ground motions is an effective pulse index to analyze the seismic effects on tunnel when pulse periods of two different ground motions are uniform, and the index affects the tunnel in dynamic internal forces. More importantly, the existence of soft soil layer severely affects the propagation of input ground motions. At frequency domain, the ground response increases at high frequency component in the near-fault pulse-like ground motion, while the response shows a decreasing at low frequency component. [ABSTRACT FROM AUTHOR]

The paper proposes the use of supervised machine learning (ML) methods for quickly predicting the seismic response of rocking systems when subjected to seismic excitations. Different supervised ML algorithms are discussed, while a relatively simple and a more sophisticated algorithm are examined in detail. Specifically, the two algorithms compared are the k-Nearest Neighbor (k-NN) and the Support Vector Machine (SVM). The performance of the ML models is demonstrated considering both sine pulses and different sets of natural ground motion records. The results are practically perfect for sine pulses, while accurate results were also obtained for the case of natural ground motions. The proposed ML-based tool allows to quickly assess the risk of damage for rocking systems, while it is also very important when a large number of rocking blocks have to be studied, e.g. in the case of a building's inventory. [ABSTRACT FROM AUTHOR]

Seismic risk assessment of building portfolios has been traditionally performed using empirical ground motion models, and scalar fragility and vulnerability functions. The advent of machine learning algorithms in earthquake engineering and ground motion modelling has demonstrated promising advantages. The aim of the present study is to explore the benefits of employing artificial neural networks (ANNs) in earthquake scenarios for spatially-distributed building portfolios. To this end, several recent major seismic events in the Balkan region were selected to assess damage and economic losses, considering different modelling approaches. For the assessment of the seismic demand, the ANN developed in the companion study and common ground motion models for Europe were adopted. For the vulnerability component, recent ANN models and existing scalar fragility and vulnerability functions for the Balkans were used. The estimates of all modelling cases were compared against the aggregated damage and economic loss data observed in the aftermath of these events. The findings of this study suggest that overall, the ANNs led to damage and economic loss estimates closer to the observations. [ABSTRACT FROM AUTHOR]

The calculation of the spatial distribution of ground motion is one of the most important steps in earthquake scenarios. The advancements in machine learning algorithms and the release of new ground motion data can enable improvements in the reliability and accuracy of this component. We developed an artificial neural network (ANN) ground motion model using a compiled database from two subsets with measured Vs30 from the Pan-European strong motion database and the NGA-West2 database. The ANN model employs five input parameters: moment magnitude (Mw), hypocentral depth, Joyner–Boore distance (Rjb), shear wave velocity in the top 30 m (Vs30), and faulting type. The outputs of the ANN are the RotD50 horizontal components of common intensity measures used in seismic risk assessment: PGA, PGD, PGV, Arias Intensity, and 5% damped spectral acceleration at 27 periods from 0.01 to 4.0 s. A mixed-effects modelling approach was followed to train the ANN and partition the ground motion variability into between-event and between-site terms. The input parameter scaling relationships were studied and demonstrated physically sound trends of the ground motion with respect to Mw scaling, distance attenuation, and site amplification. The predicted median response spectra for several combinations of Mw and Rjb were compared to common ground motion models for Europe, and the results are discussed. The developed ANN is used in a companion study to calculate the hazard footprints for several historical earthquakes in the Balkan region. [ABSTRACT FROM AUTHOR]

Within seismology, geology, civil and structural engineering, deep learning (DL), especially via generative adversarial networks (GANs), represents an innovative, engaging, and advantageous way to generate reliable synthetic data that represent actual samples' characteristics, providing a handy data augmentation tool. Indeed, in many practical applications, obtaining a significant number of high-quality information is demanding. Data augmentation is generally based on artificial intelligence (AI) and machine learning data-driven models. The DL GAN-based data augmentation approach for generating synthetic seismic signals revolutionized the current data augmentation paradigm. This study delivers a critical state-of-art review, explaining recent research into AI-based GAN synthetic generation of ground motion signals or seismic events, and also with a comprehensive insight into seismic-related geophysical studies. This study may be relevant, especially for the earth and planetary science, geology and seismology, oil and gas exploration, and on the other hand for assessing the seismic response of buildings and infrastructures, seismic detection tasks, and general structural and civil engineering applications. Furthermore, highlighting the strengths and limitations of the current studies on adversarial learning applied to seismology may help to guide research efforts in the next future toward the most promising directions. [ABSTRACT FROM AUTHOR]

Multi-camera systems for structure from motion (SfM) are widely deployed in many mapping applications. Existing solutions assume known rig calibration, synchronized frames among cameras, as well as overlapping field of views (FoVs). In this paper, we derive novel geometric constraints assuming minimal knowns about the multi-camera systems, to benefit low-cost and non-expert use cases where uncalibrated multi-camera systems with non-typical geometry setups present, i.e., no rig calibration, no overlapping FoVs. Assuming that these cameras are co-located and share the same motion of the platform, the proposed constraints utilize the parallelism and length proportionality of motion vectors of these co-located cameras and formulate them as translation constraints into the bundle adjustment (BA). The proposed constraints (called motion constraints) impose a first-order penalty to co-located cameras whose motion speeds and directions between frames do not match. With soft constraints, it can handle loosely synchronized frames (with an error within one second). The proposed constraints are integrated into the BA framework and experimented with different camera setups, i.e., on a group of casually co-located GoPro cameras with no rig calibration, and some with no overlapping views. Our results show that the constraints are extremely effective in improving the reconstruction and pose accuracy for ground motion images: in our self-collected open trajectories without loop closure, the proposed constraints are effective in correcting topographical errors (i.e., trajectory drifts) of the resulting models, and the dense point clouds achieve up to 11.34 m (86.12 %) of mean absolute error (MAE) improvement as compared to reference LiDAR point clouds; our results on KITTI-odometry and KITTI-360 datasets also show an improvement of up to 28.82 m (81.05 %) in terms of the root mean square error (RMSE) of absolute pose error (APE). We expect that the proposed constraints are significant not only as additional geometric constraints for image-based mobile mapping, but also will benefit the broader use of photogrammetry, since it empowers the possibility to harness the traditionally so-called low-quality stereo/multi-camera data (e.g., by non-photogrammetry citizen scientists) into improved 3D products. [ABSTRACT FROM AUTHOR]

As a famous deep and large fault in eastern China, the Tanlu Fault passes through Anhui, Jiangsu, and Shandong and into northeastern China. It is important to improve the understanding of seismic hazard assessments in areas near faults. We start a scenario earthquake simulation in the M7.5 earthquake potential area of the Xinyi-Sihong segment of the Tanlu Fault. The fault rupture length and width are constrained according to the scaling law of large intraplate earthquakes, the background normal stress is depth dependent, and the initial shear stresses are determined using trial and error by matching the earthquake magnitude. Considering the 120 km rupture length of the M7.5 earthquake, we compare the horizontal uniform stress model and self-similar stress perturbation model. Our findings reveal that the seismic source time function of the horizontal uniform stress model is similar to that of the Haskell model and that of the self-similar stress perturbation model is more similar to that of a real earthquake case. We compare the dynamic rupture simulation and ground motion results under four different stress conditions and find that the shorter the characteristic length of the self-similar function is, the rougher the initial stress. For the M7.5 earthquake with an epicenter in the vicinity of Suqian, the Xinyi-Tancheng segment, which is located in the IX-intensity zone north of the epicenter, vibrates more strongly on the northern side than on the southern side due to the influence of the low-velocity zone and the peak slip rate. The response spectra analysis at stations in the study area is useful for improving the earthquake resistance capability. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study carried out to develop a multi-severity casualty approach for predicting the number of fatalities and two levels of injuries resulting from an earthquake. The two levels of injuries are severity 1, which do not require hospitalization, and severity 2, which do require hospitalization. The method provides a probability distribution of casualty, which can assist policymakers and disaster management authorities in developing emergency response plans. The proposed approach takes into account two main sources of uncertainty: variations in ground motion values (by generating thousands of shaking maps considering the spatial correlation of intra-event uncertainty) and uncertainty in casualty rates (by using the fuzzy set theorem and random sampling). As a case study, the proposed method was applied to the Mosha fault seismic scenario (7.4 Mw) in Tehran, Iran. The results indicated that 200,300 people may lose their lives in that seismic scenario. Additionally, the number of injuries with severity 1 and severity 2 are estimated to be about 420,600 and 232,980, respectively. These numbers exceed the healthcare capacity in Tehran. The estimated number of fatalities was then compared with the results of some existing empirical models in the literature. This comparison revealed that the empirical models underestimate the number of fatalities due to various reasons, such as a lack of strong earthquakes in urban areas to be included in empirical models. Thus, the proposed approach has several advantages for estimating casualties in urban fabrics. [ABSTRACT FROM AUTHOR]

This paper estimates a correlation model and joint distribution model of spectral accelerations of vertical ground motions based on the NGA-West2 ground motion database. The correlation model calculated via BC16 vertical GMPE in this study is compared with those published models via other vertical NGA-West2 GMPEs. It is verified that the calculated residuals of vertical ground motions at multiple periods follow the multivariate normal distributions via both a qualitative validation method and a set of quantitative validation methods. The correlation model and the joint distribution model of vertical spectral accelerations are the theoretical basis for research on the joint occurrence of spectral accelerations of vertical ground motions, and an example of applied research on joint occurrence of spectral accelerations of vertical ground motions is studied: the conditional mean spectrum of vertical ground motions. Finally, the effects of different vertical correlation models and ground motion prediction equations on vertical conditional spectra are investigated for wide application of the correlation model and the joint distribution model of spectral accelerations of vertical ground motions. [ABSTRACT FROM AUTHOR]

For regional hazard analysis or engineering applications, it is necessary to simulate a group of correlated ground motions containing information from given recordings. A new method is presented to conditionally generate ground motions at unobserved points using actual recordings in this localized region. This method is based on an extension of the traditional conditional simulation using a wavelet packet. Utilizing the advantages of wavelet-packet transformation, the time and frequency characteristics of ground motions are effectively identified. Besides by decomposing the signal into non-overlapping sub-frequency bands, it is possible to conditionally simulating stationary, space-time, and Gaussian random fields. These simulated motions are consistent with the actual recordings in terms of temporal variations in the frequency domain and its design spectrum, and their coherence functions satisfy the target coherence values. Two numerical examples have been provided to illustrate the capability of the proposed method: the simulation of ensembles of earthquake accelerograms using actual recordings and blind tests in SMART-1 array. [ABSTRACT FROM AUTHOR]

Earthquake early warning systems assess the size of an earthquake based on initial ground motion and provide warnings before the arrival of large waves. We employ the iterative regression to simultaneously estimate the station correlations and the corresponding linear relations for magnitude. We report the standard error reductions of the magnitude estimation based on the initial peak P-wave acceleration (${P_a}$ P a ), velocity (${P_v}$ P v ), and displacement (${P_d}$ P d ) as $14.52\% $ 14.52 % , $7.63\% $ 7.63 % , and $7.58\% $ 7.58 % , respectively. Factor analysis of the stationary correction reveals its correlation with several factors. This study shows that station correction can help to improve the precision of magnitude estimation in the future. [ABSTRACT FROM AUTHOR]

Irazú and Turrialba are a twin volcanic complex that marks a distinct stop in volcanism along the Central America volcanic arc. We present a new traveltime velocity model of the crust beneath Irazú and Turrialba volcanoes, Costa Rica, and interpret it considering the results of previous ambient noise tomographic inversions. Data were acquired by a temporary seismic network during a period of low activity of the Irazú–Turrialba volcanic complex in 2018–2019. Beneath the Irazú volcano, we observe low P -wave velocities (V P = 5 km s−1) and low velocity ratios (V P/ V S = 1.6). In contrast, below the Turrialba volcano, we observe low S -wave velocities (V S = 3 km s−1) and a high V P/ V S (= 1.85) anomaly. We found that locations of low V P and V S anomalies (−15 %) correspond well with shear wave velocity anomalies retrieved from ambient noise tomography. At shallower depths, we observe high V P and V S anomalies (+15 %) located between the summits of the volcanoes. Subvertical velocity anomalies are also observed at greater depths, with high V P and V S anomalies appearing at the lower limits of our models. We propose a complex structure of an intermediate magmatic reservoir, presenting multiphase fluid states of a liquid-to-gas transition beneath Irazú and a juvenile store of magmatic fluid beneath Turrialba, while shallow fluid transport provides evidence of magmatic–hydrothermal interactions. [ABSTRACT FROM AUTHOR]

This paper discusses the results of 1∶25 scale shake table tests evaluating the seismic response of multiblock tower structures (MTSs) conceived as energy storage systems. The tests described here are a part of a comprehensive research campaign involving smaller physical models, computational model validation, and the theoretical background required to compare results across scales. The 6.46-m-high MTSs consisted of over 7,000 concrete blocks stacked vertically without any bonding agent, interacting only by friction and rocking. Three MTSs were tested under two different ground motions. Dynamic digital image correlation (DIC) and low-cost micro electrical mechanical system (MEMS) accelerometers were used for dynamic response measurements. Towers 1 and 3, subjected to repeated strong-intensity earthquake ground motions, collapsed during the third repetition due to the accumulation of residual displacements. Tower 2 was subjected to a single near-fault ground motion representing an extreme event and collapsed during the test. Different collapse mechanisms were identified in the test program. Data collected from individual blocks showed in-plane and out-of-phase block rotation and sliding, which contributed to the system's energy dissipation during the tests. [ABSTRACT FROM AUTHOR]

This paper presents an extensive numerical study on the seismic performance of steel eccentrically braced frames (EBFs) designed according to the ASCE 7-16 standard. A series of EBF building structures ranging from 2 to 12 stories located in downtown Los Angeles are designed with different yielding links, including conventional and replaceable links. The performance of the structures is investigated through nonlinear time-history analyses (NLTHAs) under a suite of 40 ground motions, selected and scaled to match the target uniform hazard spectrum. The peak deformations are first examined to assess the performance of the buildings as well as the design procedure. Residual deformations are also evaluated and critically compared with acceptable limits, to provide insight into the downtime and recovery time of EBFs after major earthquakes. Previous studies have demonstrated that the response of stable yielding systems to major earthquakes is often accompanied by significant residual deformations, and that residual drifts exceeding 0.5% can cause hindrance to the buildings occupants. In EBFs, in addition to residual drifts, residual link rotations are also relevant and expected to be more severe due to the localization of inelastic deformations in the yielding link. For replaceable modular yielding links, significant residual link rotations after major earthquakes will hinder repairs and the link replacement process, which is one of the important design objectives. The performance of EBFs is also compared with what was observed in previous studies for buckling restrained braced frames and special moment resisting frames. In the end, the results from the extensive NLTHAs are used to establish a relationship between peak drifts and peak link rotations in EBFs. [ABSTRACT FROM AUTHOR]

In recent decades, research on renewable energy has been boosted by the emerging awareness of energy security and climate change and their consequences, such as the global cost of adapting to the climate impacts. Both onshore and offshore wind turbine farms have been considered as one of the main alternatives to fossil fuels. Their development currently involves seismic-prone areas, such as the Californian coastline and East Asia, where the risk of soil liquefaction is significant. Onshore wind turbines (OWTs) typically are founded on shallow rafts. Their operation can be affected strongly by the simultaneous presence of intense earthquakes and wind thrust, which may cause remarkable permanent tilting and loss of serviceability. In these conditions, accurate evaluation of the seismic performance of these structures requires the development of well-validated numerical tools capable of capturing the cyclic soil behavior and the build-up and contextual dissipation of seismic-induced pore-water pressures. In this paper, a numerical model developed in OpenSees, calibrated against the results of dynamic centrifuge tests, was used to evaluate the influence of some ground motion intensity Measures of the seismic behavior of OWTs included the amplitude, frequency content, strong-motion duration, and Arias intensity (energy content) of the earthquake, together with the effect of a coseismal wind thrust, which is not well explored in the literature. The seismic performance of an OWT was assessed in terms of peak and permanent settlement and tilting, the latter of which was compared with the threshold of 0.5° typically adopted in practice. [ABSTRACT FROM AUTHOR]

To accurately and rapidly predict seismic responses, including the maximum displacement (MaxD) and maximum acceleration (MaxA), of the isolated structure considering the soil–structure interaction (SSI), five ensemble learning models, i.e. random forest (RF), gradient boosting regression tree (GBRT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) and stacking model, are constructed. Firstly, a total of 96 000 nonlinear time history analyses of the isolated structure considering the SSI are conducted with the aid of OpenSees. The generated database is used for training and testing ensemble learning models. The ensemble learning models have 12 input variables in four categories, i.e. ground motion parameters, structural parameter, isolation parameters and soil parameter, and two output variables, i.e. MaxD and MaxA. The study shows that all ensemble learning models have excellent prediction performance for both training and testing datasets. The determination coefficients are larger than 0.96 and root-mean-square errors (RMSEs) are relatively small. Among the five ensemble learning models, the stacking model exhibits the best performance. In addition, the calculation method of feature importance score for the stacking model is provided. According to the feature importance analysis, the ground motion parameters have greater impact on seismic responses than other three categories of inputs. Finally, six ground motions are randomly selected to verify the generalization ability of the proposed ensemble learning models. The results show that the stacking model has a favorable generalization ability with relatively small prediction errors. [ABSTRACT FROM AUTHOR]

The increasing use of inertial measurement units (IMU) in biomedical sciences brings new possibilities for clinical research. The aim of this paper is to demonstrate the accuracy of the IMU-based wearable Syde® device, which allows day-long and remote continuous gait recording in comparison to a reference motion capture system. Twelve healthy subjects (age: 23.17 ± 2.04, height: 174.17 ± 6.46 cm) participated in a controlled environment data collection and performed a series of gait tasks with both systems attached to each ankle. A total of 2820 strides were analyzed. The results show a median absolute stride length error of 1.86 cm between the IMU-based wearable device reconstruction and the motion capture ground truth, with the 75th percentile at 3.24 cm. The median absolute stride horizontal velocity error was 1.56 cm/s, with the 75th percentile at 2.63 cm/s. With a measurement error to the reference system of less than 3 cm, we conclude that there is a valid physical recovery of stride length and horizontal velocity from data collected with the IMU-based wearable Syde® device. [ABSTRACT FROM AUTHOR]

In order to avoid the casualties caused by damaged buildings during strong earthquakes, it's important and essential to understand the seismic design specifications of buildings. The motion of a building depends on the interferometry of building and ground motion, the coupling between building and ground, and the mechanical properties of the building. We applied deconvolution technique to the motion recorded to separate the building response and calculated the structural parameters (including traveling wave velocity, resonant frequency and attenuation parameter). Therefore, we can check medium's state by tracking these parameters to avoid damaged building collapse in the next mega-earthquake. We deployed a seismograph array in the building to record the earthquakes and few months of ambient noise data. In addition, we divided the structure into lower and higher floors to calculate the layering velocity. We found that the wave velocities of the lower and higher floors are significantly different but the former exhibit stable variation. For the monitoring phase, we tried different stacking time-lengths and calculated the related traveling wave velocities which are similar to the analysis in earthquakes. From the above results, we concluded that the physical parameters of the building can monitor the healthy condition of structure. Finally, we hope these results would be helpful to build a long-term monitoring of building's healthy status and the assessment of seismic hazard. [ABSTRACT FROM AUTHOR]

This paper focuses on building a ground motion model (GMM) using artificial neural network technique driven by a synthetically generated database in a data poor region. A hybrid simulated method considering nonlinear site response is used to generate potential ground motions in Tianjin area. The synthetic database containing a total of 9267 ground motion records from 93 simulated earthquake events are utilized to develop the model. A back propagation neural network developed by genetic algorithm (GA-BPNN) is used to obtain the optimal weights and biases that most fit the target dataset without overfitting. The structure of GA-BPNN model consists of 322 unknowns including weights and biases that connect input and output parameters. The model is developed to predict PGA and 5% damped spectral acceleration (periods from 0.01 to 10 s). The input parameters consist of moment magnitude (Mw), strike (φ), dip (δ), rake angle (λ), focal depth (h), epicenter distance (R) and azimuth (θ). The model performance is observed to be within the appropriate error limits. It is found that the model has the ability to capture the parametric variation relationship. Additionally, the synthetically based model is compared with empirically based GMM relations derived from NGA-West2, finding it has similar performance and behavior to leading GMMs. The model application in Tianjin area demonstrates that this paper provides a scheme to utilize synthetic database and build GMMs in data poor regions. [ABSTRACT FROM AUTHOR]

Climate warming is degrading palsa peatlands across the circumpolar permafrost region. Permafrost degradation may lead to ecosystem collapse and potentially strong climate feedbacks, as this ecosystem is an important carbon store and can transition to being a strong greenhouse gas emitter. Landscape-level measurement of permafrost degradation is needed to monitor this impact of warming. Surface subsidence is a useful metric of change in palsa degradation and can be monitored using interferometric synthetic-aperture radar (InSAR) satellite technology. We combined InSAR data, processed using the ASPIS algorithm to monitor ground motion between 2017 and 2021, with airborne optical and lidar data to investigate the rate of subsidence across palsa peatlands in northern Sweden. We show that 55 % of Sweden's eight largest palsa peatlands are currently subsiding, which can be attributed to the underlying permafrost landforms and their degradation. The most rapid degradation has occurred in the largest palsa complexes in the most northern part of the region of study, also corresponding to the areas with the highest percentage of palsa cover within the overall mapped wetland area. Further, higher degradation rates have been found in areas where winter precipitation has increased substantially. The roughness index calculated from a lidar-derived digital elevation model (DEM), used as a proxy for degradation, increases alongside subsidence rates and may be used as a complementary proxy for palsa degradation. We show that combining datasets captured using remote sensing enables regional-scale estimation of ongoing permafrost degradation, an important step towards estimating the future impact of climate change on permafrost-dependent ecosystems. [ABSTRACT FROM AUTHOR]

It is known that the site classifications are closely related to the damages caused by earthquakes in areas with increased seismic hazard. Additionally, another important parameter utilized to identify the damage is the Peak Ground Acceleration (PGA) value. While measurements and the GMPE are utilized to identify PGA values, site classification is usually conducted by using the Vs30 value. This study aims to identify the site classifications for Bursa province by using a different approach, namely, the H/V spectral ratio method based on the dominant periods. In this regard, 205 records belonging to 82 earthquakes recorded by 41 strong ground motion stations located in Bursa province were utilized. A mean H/V spectral ratio curve was developed for each station based on the Fourier and response spectra of these earthquake records. Generally, double or multiple peaks resulting from the site structure were observed in the H/V curves. Furthermore, for the station locations, the evaluations were conducted in accordance with the site classifications per the dominant period as it is suggested in the literature. The stations were identified as all of the site classifications suggested by (Zhao et al. Bull Seismol Soc Am 96:914–925, 2006), as SC-1, 2, 3 and 5 suggested by (Fukushima et al. J Earthquake Eng 11:712–724, 2007) and as CL I, II, III, IV and VII suggested by (Di Alessandro et al. Bull Seismol Soc Am 102:680–695, 2012). Additionally, various Spectral Acceleration estimations were made with different GMPE equations for scenario earthquakes, and the results were compared with the design spectra suggested by the Turkish Building Earthquake Code (TBEC 2018). As a result of the study, the H/V spectral curves were generated according to both Fourier and response spectra; using a great number of earthquake data, the hazard was assessed by the soil dominant period-based for the first time in Bursa province. [ABSTRACT FROM AUTHOR]

Numerous ground-motion models (GMMs) that predict the intensities of surface ground motions have been previously developed based on regression analysis (RA). This study develops GMMs to estimate 5% damped pseudo-spectral accelerations (PSAs) for 30 periods (0.01–7.0 s) for within-rock ground motions, based on machine learning (ML) methods (i.e., two ensemble methods (random forest (RF) and gradient boosting (GB)) and an artificial neural network (ANN)). GMMs are developed separately for four earthquake types (main and aftershocks of active crustal region events and those of subduction zone interface events), considering the differences in the characteristics of each earthquake type. We utilize 20,041 ground motions recorded at 575 borehole stations in Japan during 602 earthquakes with moment magnitudes greater than 5.0 and rupture distances shorter than 300 km. The prediction performances of GMMs based on RF, GB, ANN, and RA are evaluated by the standard deviations of the total, between-event, and within-event residuals. The GMMs based on the three ML methods (RF, GB, and ANN) perform better than the RA-based models. The RF-based GMMs resulted in the most accurate prediction of the PSAs of within-rock ground motions with a small bias and variance, which can enhance the seismic designs and seismic hazard assessments for underground structures. [ABSTRACT FROM AUTHOR]

The double-couple model of the earthquake source has been proven remarkably successful in explaining the radiation pattern in the low-frequency wavefield. However, the radiation pattern is distorted in the high-frequency wavefield. The frequency dependence of the radiation pattern is often interpreted as the path effects. The impact of earthquake sources on radiation patterns is uncertain. In our study, based on the fact that P waves vibrate along the radial direction and S waves vibrate along the transverse direction, the path effects modified model of radiation pattern is proposed based on the optimal ratio coefficient by measuring radial-to-transverse spectral ratios at multiple frequency ranges. Then, we found that the correlation coefficient decreases with the increase of frequency but remains stable with the increase of distance. This means the complexity of the earthquake source can distort the wavefield at higher frequencies and drive variability in the ground motion. [ABSTRACT FROM AUTHOR]

An optimisation-based calibration technique, using the area metric, is applied to determine the input parameters of a stochastic earthquake-waveform simulation method. The calibration algorithm updates a model prior, specifically an estimate of a region's seismological (source, path and site) parameters, typically developed using waveform data, or existing models, from a wide range of sources. In the absence of calibration, this can result in overestimates of a target region's ground motion variability, and in some cases, introduce biases. The proposed method simultaneously attains optimum estimates of median, range and distribution (uncertainty) of these seismological parameters, and resultant ground motions, for a specific target region, through calibration of physically constrained parametric models to local ground motion data. We apply the method to Italy, a region of moderate seismicity, using response spectra recorded in the European Strong Motion (ESM) dataset. As a prior, we utilise independent seismological models developed using strong motion data across a wider European context. The calibration obtains values of each seismological parameter considered (such as, but not limited to, quality factor, geometrical spreading and stress drop), to develop a suite of optimal parameters for locally adjusted stochastic ground motion simulation. We consider both the epistemic and aleatory variability associated with the calibration process. We were able to reduce the area metric (misfit) value by up to 88% for the simulations using updated parameters, compared to the initial prior. This framework for the calibration and updating of seismological models can help achieve robust and transparent regionally adjusted estimates of ground motion, and to consider epistemic uncertainty through correlated parameters. [ABSTRACT FROM AUTHOR]

With the development of performance-based seismic design, the residual displacements of structures have become a key factor in determining the post-earthquake functionality and post-earthquake repair cost. However, the factors affecting the residual displacement responses of the RC structure and the corresponding influence laws have not been uniformly understood. This paper systematically investigates the influence of structural properties and ground motion characteristics on the residual displacements of the RC bridge columns, especially the near-fault pulsed ground motion characteristics. To this end, the ultimate quasi-static residual displacement and the dynamic residual displacement are first defined and introduced. Then, Bouc–Wen models and the finite element models of RC columns are selected as target models, and quasi-static analyses and dynamic time history analyses of the two models are performed to obtain the residual displacement responses. The results show that the structural properties and ground motion characteristics significantly affect the residual displacement responses of the RC bridge columns. The influence laws of structural properties on residual displacement responses of different models obtained by using different analysis methods are inconsistent, and it is also differences in the influence laws when using different residual displacement indexes. Furthermore, the residual displacement responses of the RC columns subjected to near-fault pulsed ground motions are larger compared with ground motions without pulses, and they increase with the increasing of the pulse period. The symmetrical pulsed ground motions result in higher residual displacement demands compared with antisymmetric pulsed ground motions. [ABSTRACT FROM AUTHOR]

In order to develop the direct normalized inelastic displacement spectrum rather than the inelastic displacement ratio or reduction factor that is suitable for self-centering structures when subjected to near-fault pulse-like (NFPL) ground motions, the nonlinear time history analysis for a single degree of freedom (SDOF) system with a generalized flag-shaped (FS) hysteretic model is realized using Matlab software. A total of 85 NFPL records are arranged as the seismic input, the inherent viscous damping ratio, ξ, is respectively determined as 0.02 and 0.05 for typical steel and concrete structures, the variation range of all considered parameters, including the ductility, the vibration period, the energy dissipation coefficient and the post-yield stiffness ratio of the hysteretic model is specified, then the constant-ductility inelastic displacement spectra for different seismic records and different model parameters are calculated and statistically studied. It is found the normalization of the natural period and the displacement by the pulse period, namely T/Tp and xm/Tp2, can significantly reduce the variation of displacement spectra. The influence of the post-yield stiffness ratio ranging from 0 to 0.20 on the normalized spectra is slight and can be neglected. Besides, as T/Tp increases, the normalized displacement first increases and then slowly decreases, the normalized period corresponding to the peak displacement is approximately 1. When T/Tp is about less than 0.7, the displacement spectra are positively correlated with the ductility, μ, followed by a negative correlation between them as T/Tp grows larger. Moreover, increasing the energy dissipation coefficient will reduce the displacement response, but when the coefficient is greater than 1, its impact on the displacement is rather small. Finally, the equation of the normalized displacement spectra as a function of T/Tp and μ is proposed, in which an adjustment factor, Ωβ, is added to account for the influence of the energy dissipation coefficient on the displacement spectra. [ABSTRACT FROM AUTHOR]

The present study aims to analyze the correlation between ground motion parameters and energy demands of low-rise RC buildings without shear walls. Two regular 4- and 7-story residential buildings were seismically designed to represent low-rise RC buildings. In order to establish the requirements of single degree of freedom (SDOF) systems as well as multi degree of freedom (MDOF) systems, the dynamic features of "equivalent" SDOF systems were defined by using MDOF systems. The correlation of 20 ground motion parameters (GMPs) of 44 records with the energy demands obtained from a total of 176 nonlinear time history analyses was examined for the SDOF and MDOF systems within the scope of this study. The ground motion parameters (GMPs) were taken as intensity measures (IMs). In contrast, maximum input energy was used as a demand measure (DM) and these energy values were normalized with the masses of the buildings for cases, where the buildings were evaluated together. Parameters related to acceleration and velocity are generally found to yield better results in comparison to the ones related to frequency and displacement. Velocity Spectrum Intensity (VSI) and Arias Intensity (Ia) were obtained to have the highest correlation values as a single parameter. This study suggested new equations by combining multiple ground motion parameters for SDOF and MDOF systems to better reflect damage potential than a single parameter. The use of multiple parameters in combination results in better correlation coefficients. [ABSTRACT FROM AUTHOR]

Large parts of Asia, Europe, and the eastern USA have high population concentrations in moderate seismic regions (MSR), where very large earthquakes are rare. With rampant construction in the cities located in MSRs, a relatively moderate earthquake can turn into a catastrophe. Limiting the collapse has been the conventional performance objective for regions with high seismicity. However, this does not necessarily translate into a more desirable target of life-safety under design basis earthquake in MSRs. The present paper investigates the seismic performance of non-ductile reinforced concrete (RC) buildings that were common before the advent of modern design standards, and are found in very large numbers. An analytical model capturing the shear failure is employed using a drift-based limit state material. The results show that a hybrid index captures moderate damage more accurately than the prevalent drift-based index. The paper further shows that while a generic ground motion suite accurately estimates the median fragility, it fails to capture the uncertainty. A 7-story non-ductile RC archetype building was found to be at a collapse prevention risk of 2 × 10–4. The seismic risk deaggregation reveals that seismic risk consistency can be enhanced by explicit specification of the seismic forces for retrofitting. The study shows that the stringent seismic risk targets can be met by non-ductile buildings in MSR, provided they are designed or retrofitted with constraints on strong column weak beam factors. The study will facilitate in developing a scheme for prioritizing and devising the retrofit of pre-modern code and non-ductile deficient buildings. [ABSTRACT FROM AUTHOR]

The regional seismic risk assessment plays an important role in the disaster prevention and mitigation. The accurate time-history analysis for a region provides an important data base for regional seismic risk assessment. However, an accurate calculation method is limited, then, its accuracy still lacks validation. Meanwhile, there is also a lack of risk assessment methods and indicators applicable to the regional accurate time-history analysis, both of which hinder the seismic risk assessment. In this paper, a regional-scale time history analysis-driven method is proposed for the quantitative evaluation of regional seismic damage risk and verified with a real earthquake damage survey. First, the regional time history analysis method is introduced, including the automatic modeling, response calculating, result analysis and 3-D visualization modules. Second, a calculation method for seismic damage degrees (DD) of buildings is introduced. Subsequently, the probabilistic models of each DD under the ground motions with a specific intensity are calculated based on regional-scale nonlinear time history analysis. Next, the concept of the three-level seismic damage risk is offered, then a determining workflow of seismic damage risks is proposed based on the code-abiding three-level design requirement. Afterward, the regional seismic damage index is suggested for the quantitative evaluation of earthquake-induced damage risk. Finally, the actual seismic damage database of 192 buildings in epicenter of Ludian Earthquake is established through on-site investigations, then used to verify the regional simulation's accuracy. Consequently, it could be an aid to accurately present seismic damage distribution assessments and effectively develop seismic risk mitigation strategies. [ABSTRACT FROM AUTHOR]

This paper investigates the inelastic displacement ratios (IDRs) and displacement ductility demands of a wide range of single-degree-of-freedom (SDOF) systems subjected to pulse-like ground motions (GMs) of the 2023 Pazarcık (Kahramanmaraş) earthquake. A set of twenty-seven GMs characterized as pulse-like are utilized in the study. As-recorded velocity time histories of horizontal components are rotated over 90° at a step of 1° to attain the waveform with the largest peak ground velocity (PGV) over all horizontal orientations. Inelastic displacement ratio and displacement ductility spectra are computed through nonlinear response history analysis (RHA). Local amplifications of both spectra are observed at some periods. The results of this study show that large inelastic displacement and ductility demands are imposed on certain reinforced concrete (RC) structures. Finally, predictive models of the mean inelastic displacement ratio and mean ductility demand spectra (DDS) are developed based on the Gauss–Newton algorithm (GNA). The model provides a strong correlation between the computed and the estimated data, and sufficient convergence criteria. The results of this study collectively emphasize the necessity of integrating pulse-like GMs into future revisions of earthquake codes. [ABSTRACT FROM AUTHOR]

This study presents a neural network-based model for predicting linear quadratic regulator (LQR) weighting matrices for achieving a target response reduction. Based on the expected weighting matrices, the LQR algorithm is used to determine the various responses of the structure. The responses are determined by numerically analyzing the governing equation of motion using the state-space approach. For training a neural network, four input parameters are considered: the time history of the ground motion, the percentage reduction in lateral displacement, lateral velocity, and lateral acceleration, Output parameters are LQR weighting matrices. To study the effectiveness of an LQR-based neural network (LQRNN), the actual percentage reduction in the responses obtained from using LQRNN is compared with the target percentage reductions. Furthermore, to investigate the efficacy of an active control system using LQRNN, the controlled responses of a system are compared to the corresponding uncontrolled responses. The trained neural network effectively predicts weighting parameters that can provide a percentage reduction in displacement, velocity, and acceleration close to the target percentage reduction. Based on the simulation study, it can be concluded that significant response reductions are observed in the active-controlled system using LQRNN. Moreover, the LQRNN algorithm can replace conventional LQR control with the use of an active control system. [ABSTRACT FROM AUTHOR]

This study focuses on evaluating the performance of an aged heritage building made of unreinforced brick masonry under seismic conditions, both with and without retrofitting. To achieve this, an experimental examination was conducted using a scaled-down model of a single-story unreinforced building, constructed on a shake table at a 1:4 geometric scale. The responses of this model were recorded at the roof level, primarily in the form of displacements. Subsequently, the same scaled model was replicated in SAP2000 finite element (FE) software. The results obtained from the FE model closely matched the experimental findings, validating the computational approach. Following this validation, an existing masonry heritage school building situated at Aligarh Muslim University, Aligarh, was chosen for further investigation. In this subsequent analysis, natural frequencies and mode shapes were computed through model analysis, revealing closely spaced natural frequencies. To assess the seismic performance of the heritage building, a nonlinear time history analysis was conducted using actual ground motion data from the Koyna earthquake. The maximum compressive and tensile stresses generated numerically under both gravity and earthquake loading conditions were compared with permissible stresses in accordance with Indian masonry standard codes. The finite element model demonstrated that the building remains structurally sound under gravity loading, although some vulnerable areas were identified under earthquake loading conditions. To mitigate the stresses in these vulnerable zones, various retrofitting techniques were explored. Notably, the application of steel flats on floors and walls, along with GFRP sheeting, proved to be an effective solution, reducing stress levels by up to 90%. [ABSTRACT FROM AUTHOR]

Geometric complexities play an important role in the nucleation, propagation, and termination of strike-slip earthquake ruptures. The 2021 Mw7.4 Maduo earthquake rupture initiated at a large releasing stepover with a complex fault intersection. In the epicentral region, we conducted detailed mapping and classification of the surface ruptures and slip measurements associated with the earthquake, combining high-resolution uncrewed aerial vehicle (UAV) images and optical image correlation with field investigations. Our findings indicate that the coseismic ruptures present discontinuous patterns mixed with numerous lateral spreadings due to strong ground shaking. The discontinuous surface ruptures are uncharacteristic in slip to account for the large and clear displacements of offset landforms in the epicentral region. Within the releasing stepovers, the deformation zone revealed from the optical image correlation map indicates that a fault may cut diagonally across the pull-apart basin at depth. The left-lateral horizontal coseismic displacements from field measurements are typically ≤0.6 m, significantly lower than the 1–2.7 m measured from the optical image correlation map. Such a discrepancy indicates a significant proportion of off-fault deformation or the possibility that the rupture stopped at a shallow depth during its initiation phase instead of extending to the surface. The fault network and multi-fault junctions west and south of the epicenter suggest a possible complex path, which retarded the westward propagation at the initial phase of rupture growth. A hampered initiation might enhance the seismic ground motion and the complex ground deformation features at the surface, including widespread shaking-related fissures. [ABSTRACT FROM AUTHOR]

This research provides a practical and feasible retrofitting solution for soft story in reinforced concrete (RC) buildings with masonry infill walls for example in Nepal through numerical analysis. It is well understood that stiffness and strength of the soft story floor can be increased significantly by adding RC shear wall in the frame opening of the ground floor, making lateral deformation of that floor negligible. With this consideration, the authors have developed a numerical optimization framework for optimizing the RC shear wall in the open floor of soft story building incorporating socio-technical aspects from their previous publication [Timsina, K., Gadagamma, C. K., Numada, M. and Meguro, K. [2019b] "Development of a numerical optimization framework for solving soft-story problem in reinforced concrete frame buildings," Seisan-Kenkyu]. The objective function for numerical optimization is cost minimization with the constraints of usability, performance, and complexity from a detailed field study. In this paper, the numerical optimization framework is developed and solved using the sequential quadratic programing (SQP). The whole scheme has been incorporated inside the 2D-Applied Element Method (AEM). The retrofitting solution thus obtained from the numerical optimization has been analyzed using the incremental dynamic analysis to know the capacity of the frame structure in earthquake ground motion. It shows that the retrofitting method can significantly enhance the soft story frame's capacity and ensure the dwellers' interests are not compromised. [ABSTRACT FROM AUTHOR]

A 500-kV power transformer substation was focused on for seismic performance examination. Finite element models of the power transformer, isolation layers, site soil, bus bars and support structure were established under various analysis conditions. By inputting typical ground motions into the models, seismic time history responses were obtained. First, the seismic performance, critical position and failure modes of a single power transformer without isolation were examined. Then, the effect on the power transformer by arranging isolation bearings was confirmed. Moreover, the influence of site soil dynamic characteristics on the isolating performance of the power transformer was examined. Finally, the influence of the length margin of the bus bars on the seismic response of the isolated power transformer was analyzed. It was revealed that the bottom of the transformer bushings which were made of porcelain was a weak part. Installing isolation bearings effectively reduced the maximum stress response of the bushing for most ground motions. The dynamic characteristics of the site soil significantly affected the isolation efficiency of the power transformer, and the expected isolation effect was achieved when the soil was soft. The displacement response of the transformer was largely increased by isolation, the pulling effect of the bus bars on the porcelain bushing was significant. When the length margin of the bus bars was enlarged to 1.0 m, the influence on the bushing decreased to a low level that could be ignored. [ABSTRACT FROM AUTHOR]