Ground Acceleration Research Articles

Peak Acceleration Distribution on the Surface of Reef Islands under the Action of Vertical Ground Motion

This paper adopts the fluid–structure coupling algorithm based on the acoustic fluid element, the fluid dynamic artificial boundary, and the consistent viscoelastic artificial boundary of solid media to establish a finite element model of the dynamic interaction of the reef-island–seawater system. Then, a numerical simulation of the seismic response of the reef-island site is carried out to study the seismic ground motion distribution patterns of the reef–seawater site and the reef-island–lagoon site. The innovation of this article is that the influence of reef–island topography and fluid–structure coupling is considered in the analysis when vertical ground motion is input. The results show that the slope angle of the bottom layer has a significant influence on the peak ground acceleration distribution and peak size on the island slope surface and the reef platform. For high-frequency input motion, a smaller reef platform width will induce a larger peak acceleration response on the reef platform. Seawater has a significant suppressive effect on vertical ground acceleration. The more high-frequency components of the input bedrock motion, the more obvious this suppression effect will be. The existence of the lagoon will amplify the maximum peak acceleration on the reef platform. According to the calculation results, lagoon terrain can amplify the maximum horizontal and vertical peak accelerations on the reef platform by about 19 and 6 times relative to the free-field results, respectively.

Intelligent assessment of building damage of 2023 Turkey-Syria Earthquake by multiple remote sensing approaches

A catastrophic Mw7.8 earthquake hit southeast Turkey and northwest Syria on February 6th, 2023, leading to more than 44 k deaths and 160 k building collapses. The interpretation of earthquake-triggered building damage is usually subjective, labor intensive, and limited by accessibility to the sites and the availability of instant, high-resolution images. Here we propose a multi-class damage detection (MCDD) model enlightened by artificial intelligence to synergize four variables, i.e., amplitude dispersion index (ADI) and damage proxy (DP) map derived from Synthetic Aperture Radar (SAR) images, the change of the normalized difference built-up index (NDBI) derived from optical remote sensing images, as well as peak ground acceleration (PGA). This approach allows us to characterize damage on a large, tectonic scale and a small, individual-building scale. The integration of multiple variables in classifying damage levels into no damage, slight damage, and serious damage (including partial or complete collapses) excels the traditional practice of solely use of DP by 11.25% in performance. Our proposed approach can quantitatively and automatically sort out different building damage levels from publicly available satellite observations, which helps prioritize the rescue mission in response to emergent disasters.

A CFD-FEA coupled model for simulating dynamic response of offshore jacket platform under earthquake considering wind, wave, current and aftershock loads

Offshore platforms as the key equipment in marine oil and gas exploitation are facing challenges from various environmental conditions, such as winds, waves, currents and even earthquakes. Due to the combined action of those concurrent loads, there is potential risk of accidents. The aim of this paper is to systematically study the dynamic response and safety assessment of the large scaled offshore structures against the concurrent loads considering the fluid-structure interaction (FSI) effects. Three-dimensional numerical wave flume including a jacket platform is established by CFD software Fluent, based on which the simulation of wave generation and propagation is carried out. Then the FSI effects between waves and platform are analyzed using the CFD- FEA coupling method. Subsequently, the seismic ground accelerations are inputted into the platform, correspondingly the structural response is analyzed. Moreover, multiple influencing factors including earthquake direction and mainshock-aftershock sequence are discussed. The results show that the structure response under concurrent loads is not a simple linear superposition of different load. The seismic response would be underestimated if wave action is ignored when the earthquake acts on certain angles. The methodology proposed could be regarded as an effective way to analyze the FSI problem under realistic offshore environment.

Empirical Fragility Analysis of Haitian Reinforced Concrete and Masonry Buildings

This study develops empirical fragility curves for concrete and masonry buildings in Haiti, utilizing data from the 2021 earthquake. A dataset of 3527 buildings from the StEER database, encompassing a diverse range of building types, is used. These buildings types include reinforced concrete structures with masonry infills, confined masonry buildings, reinforced masonry bearing walls, and unreinforced masonry bearing walls. Shakemaps from the USGS are utilized to assess the earthquake’s intensity at each building, with the peak ground acceleration (PGA) as the intensity measure. Damage is classified into five distinct states: no damage, minor, moderate, severe, and partial or total collapse. For each of these states, the corresponding probabilities of exceedance are calculated, and log-normal cumulative distribution functions were fitted to those data to produce empirical fragility curves. The results show a notable similarity in performance among the four types, each having high probability of failure even under low-intensity earthquakes. Total fragility curves (including all four building types) are developed subsequently and they are convolved to the probabilistic seismic hazard map of Haiti to assess the seismic risk. This includes estimating the annual probability of partial/total collapse and the probability of partial/total collapse in the event of 475-year and 2475-year earthquakes. The results indicate a significant risk, with up to 64% probability of collapse in certain areas for the 2475-year earthquake and a probability of collapse of 15% for a 475-year earthquake. These findings underscore the critical vulnerability of Haiti’s buildings to seismic events and the urgent need for their retrofit.

Novel approach to the derivation of dam breach parameters in 2D hydrodynamic modeling of earthquake induced dam failures

Current methods of dam breach analyses adopt a deterministic approach. Applying these methods to Concrete Faced Rockfill Dams (CFRD) is fraught with huge levels of uncertainty, especially in the context of natural hazards. The frequency and magnitude of rainfall and earthquakes are higher in today's world. In the literature, the complete collapse of dams is modelled on dam breach parameters that define the dam break outflow but are not related to the return periods of natural hazards. As more new dams are constructed to control the floods in intra-plate seismic regions, this study presents a novel approach to the derivation of generalized dam break parameters for CFRD based on the structural analysis of Finite Element Model (FEM) simulations for peak ground accelerations corresponding to 475 and 2475 year return periods. Furthermore, the occurrence of rainfall and earthquake for different return periods are modelled using 2D hydrodynamic simulations. Results show the significance of generalized dam breach parameters for planning and managing CFRDs during earthquakes. The study emphasizes the utilization of structural analysis outputs for the hydraulic modeling of dam breaks, which will result in more specific and accurate dam break parameters. Additionally, the study has shown that the flood risk and the severity will increase with the intensity of earthquake and rainfall magnitudes. Disaster mitigation strategies can be optimized by considering the integrated occurrence of rainfall and earthquakes based on the probability of occurrence, demonstrated using a case-study dam. Another significant outcome of the study is the effect of soil saturation condition during a dam break, which reveals that areas within 40 km of the dam breach location might be worst affected.

Enhancing co-seismic landslide susceptibility, building exposure, and risk analysis through machine learning

Landslides are devastating natural disasters that generally occur on fragile slopes. Landslides are influenced by many factors, such as geology, topography, natural drainage, land cover, rainfall and earthquakes, although the underlying mechanism is too complex and very difficult to explain in detail. In this study, the susceptibility mapping of co-seismic landslides is carried out using a machine learning approach, considering six districts covering an area of 12,887 km2 in Nepal. Landslide inventory map is prepared by taking 23,164 post seismic landslide data points that occurred after the 7.8 MW 2015 Gorkha earthquake. Twelve causative factors, including distance from the rupture plane, peak ground acceleration and distance from the fault, are considered input parameters. The overall accuracy of the model is 87.2%, the area under the ROC curve is 0.94, the Kappa coefficient is 0.744 and the RMSE value is 0.358, which indicates that the performance of the model is excellent with the causative factors considered. The susceptibility thus developed shows that Sindhupalchowk district has the largest percentage of area under high and very high susceptibility classes, and the most susceptible local unit in Sindhupalchowk is the Barhabise municipality, with 19.98% and 20.34% of its area under high and very high susceptibility classes, respectively. For the analysis of building exposure to co-seismic landslide susceptibility, a building footprint map is developed and overlaid on the co-seismic landslide susceptibility map. The results show that the Sindhupalchowk and Dhading districts have the largest and smallest number of houses exposed to co-seismic landslide susceptibility. Additionally, when conducting a risk analysis based on susceptibility mapping, as well as considering socio-economic and structural vulnerability in Barhabise municipality, revealed that only 106 (1.1%) of the total 9591 households, were found to be at high risk. As this is the first study of co-seismic landslide risk study carried out in Nepal and covers a regional to the municipal level, this can be a reference for future studies in Nepal and other parts of the world and can be helpful in planning development activities for government bodies.

Prior knowledge‐infused neural network for efficient performance assessment of structures through few‐shot incremental learning

AbstractStructural seismic safety assessment is a critical task in maintaining the resilience of existing civil and infrastructures. This task commonly requires accurate predictions of structural responses under stochastic intensive ground accelerations via time‐costly numerical simulations. While numerous studies have attempted to use machine learning (ML) techniques as surrogate models to alleviate this computing burden, a large number of numerical simulations are still required for training ML models. Therefore, this study proposes a prior knowledge‐infused neural network (PKNN) for achieving efficient structural seismic response predictions and seismic safety assessment at a low computation cost. In this approach, first, the prior knowledge inherently within a theoretical dynamic model of a structure is infused into a neural network. Then, by utilizing the few‐shot incremental learning technique, this network would be further fine‐tuned by only a few numerical simulations. The resulting PKNN would be able to accurately predict the seismic response of a structure and facilitate seismic safety assessment. In this study, the PKNN's accuracy and computational efficiency are validated on a typical frame structure. The results revealed that the proposed PKNN could be used for accurately predicting the structural seismic responses and assessing the structural safety under a low computational cost.

Study on seismic risk assessment model of water supply systems in mainland China

Abstract. Using the peak ground acceleration (PGA) under four exceedance probabilities calculated via a probabilistic seismic hazard analysis method for China, the probability density function of PGA was obtained by fitting with the Cornell seismic hazard exceedance probability PGA function model. Combined with the seismic fragility function of the water supply system calculated on the basis of the empirical matrix of actual earthquake damage and the exposure of fixed assets, the expected seismic loss and expected loss rate models of the water supply system were established, and the classification standard with the expected seismic loss rate of the water supply system as the index was proposed. The seismic fragility of the water supply system was classified, and the exposure of the water supply system was analyzed. The total fixed assets in the Statistical Yearbook of Urban Water Supply, henceforth called Water Supply Yearbook, were taken as the exposure to earthquake in the region. The accuracy of the fragility model in this paper was verified through the actual earthquake damage losses in Deyang. Taking the water supply system of 720 cities in mainland China as an example, the distribution maps of expected seismic loss and expected loss rate were calculated and drawn. The expected loss rate model was verified by the key earthquake prevention areas in mainland China. The assessment model based on the expected loss and expected loss rate was taken as the seismic risk assessment model of water supply systems in mainland China.

Site-specific stochastic ground motion model utilizing deterministic physics-informed simulations: A Bayesian approach

Limited availability of recorded ground motions poses a challenge for reliable probabilistic seismic-hazard analysis (PSHA), even in highly seismic regions like the Western United States. Stochastic ground motions are commonly employed to address this challenge. However, the stochastic ground motion models (GMMs) may not consistently generate ground motions compatible with the site hazard due to their calibration using global data, failing to capture site-specific characteristics adequately. In the absence of recorded motions, physics-informed simulations provide a viable alternative but are deterministic with limitations of their own that makes them challenging to support PSHA. This article introduces a Bayesian framework that combines prior knowledge from a stochastic GMM, calibrated with global data, with site-specific data obtained from deterministic physics-informed simulations. The proposed framework utilizes the Rezaeian–Der Kiureghian (2010) model as the stochastic GMM and incorporates site-specific data from the CyberShake 15.12 study. By updating the mean and variance of the predictive relationships, along with the marginal distribution of the model parameters, through Bayesian inference, this framework allows for the simulation of site-specific ground motions consistent with the site characteristics. The statistics of peak ground acceleration distributions, as well as both the median and variability of the elastic response spectra, obtained from the calibrated stochastic GMM, demonstrate consistency with those derived using GMMs based on the Next Generation Attenuation (NGA) database.

Peak ground acceleration prediction for on-site earthquake early warning with deep learning

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.

Pemodelan Peak Ground Accleration (PGA) dan Intensitas Gempabumi berdasarkan Pengukuran Mikrotremor pada Kawasan Longsor di Desa Kalongan, Ungaran Timur

The landslide in Kalongan Village affected an area of 5 hectares and has the potential to experience further landslides. Landslides can occur with triggers of external factors such as earthquakes. The Peak Ground Acceleration (PGA) parameter can represent the impacts that occur on the surface in the form of earthquake intensity (MMI). Kalongan Village is 25.5km from the epicenter of the Ambarawa earthquake on 27 May 2023. This study aims to modeling the PGA microzonation and the intensity of the Ambarawa earthquake in the avalanche zone in Kalongan Village. The PGA model is based on measurements of 72 microtremor data in the avalanche zone using the Kanai method. Parameters of the Ambarawa earthquake with a magnitude of 2.5 and a depth of 5 km. The results of PGA modeling in the avalanche zone varied between 1.0-7.1 gal or I-II MMI values. The results of the PGA modeling show that aftershocks can occur if the strength of the Ambarawa earthquake reaches M=4 or 7.1-50.8 gal. The strength of the earthquake is equivalent to the intensity of II-V MMI.

Carbon Loss from Earthquake Induced Landslides in Fiordland

Landslides triggered by two earthquakes in Fiordland, New Zealand, were mapped using Google Earth Pro retro satellite imagery (eye altitude of 1.5-2.5 km, enabling ± 50 m precision), then catalogued and classified in a GIS inventory. Ground acceleration during the 2003 Mw 7.2 Secretary Island Earthquake resulted in at least 1852 landslides, but the larger Mw 7.8 2009 Dusky Sound Earthquake had lower accelerations and produced only 313. Both events dislodged large swaths of native forest, grassland vegetation and soil from Fiordland’s steep slopes in similar proportions, some landing in rivers and fiords. The landslide maps and magnitude-frequency distributions show close similarity to a published Global Forest Loss dataset derived from 2001-2022 satellite imagery. Assuming forest loss in this wilderness is predominantly landslide-related, it enables the more-precise earthquake-induced landslide mapping to be placed in a longer-term context of other vegetation loss, mostly rainfall-induced landslides, during the past two decades. The total area of forest loss during the 2003 earthquake was anomalous, whereas during 2009 the earthquake losses were similar to areas of forest loss assumed to be rainfall-induced landslides throughout Fiordland each year. Carbon concentrations of landslide vegetation and soil were calculated by defining vegetation types from a published Land Cover Database, and soil organic carbon concentration from a 1 km resolution raster dataset. Total carbon loss from earthquake-induced landslides amounts to 2.05E+06 tonnes for the 2003 Mw7.2 and 2.17E+05 tonnes for the 2009 Mw 7.8. By way of comparison, New Zealand’s total annual carbon sequestration was 6.3E+06 tonnes in 2020. Earthquake-induced and rainfall-induced landslides are shown to account for significant changes in stored carbon and available carbon sequestration, especially in areas of densely vegetated land, such as Fiordland. Processes of landscape disturbance should be included in carbon accounting and estimates of national greenhouse gas emissions.
 
 Supervised by: Caroline Orchiston
 Scholarship Project Funded by: Centre for Sustainability

Development and verification of real‐time hybrid simulation with deep learning‐based nonlinear numerical substructure

AbstractReal‐time hybrid simulation (RTHS) provides an effective approach for assessing structural responses under dynamic excitation. However, performing RTHS with a complex nonlinear numerical substructure is challenging, as computations must be completed within predefined time steps. In this study, a RTHS framework which contains a rate‐dependent experimental substructure and a nonlinear numerical substructure has been proposed and verified. An OpenSees model was constructed to simulate a three‐story, one‐bay steel building with viscous dampers at each floor and was used to generate the training dataset through a large number of nonlinear time‐history analyses. A Recursive Long Short–Term Memory (LSTM) neural network model was trained to predict the nonlinear structural responses using ground acceleration and time‐delayed damper force located at the first story. Hence, the Recursive‐LSTM model served as a surrogate model for the numerical substructure, implicitly incorporating delay compensation for the experimental substructure. After the training was completed, offline testing was performed to realize the stability of the RTHS framework. Then, online RTHS with a virtual damper taken as the experimental substructure was conducted to further confirm the feasibility and accuracy. Afterwards, a nonlinear rotary fluid viscous damper (RFVD) was fabricated as the actual experimental substructure, whose dynamic response was not considered in training the Recursive‐LSTM model. Finally, RTHS with the RFVD was completed successfully and stably, demonstrating the capability of a well‐trained Recursive‐LSTM model to serve as a nonlinear numerical substructure incorporating constant delay compensation for RTHS. The potential of the proposed RTHS framework is worth further studies in the future.

Influence of soil - foundation - tank interaction on buckling strength of liquid storage tanks

Large earthquakes in the last 25 years have caused significant damage to buildings and infrastructure, including the partial or total collapse of storage tanks in various industries. Elephant foot buckling, or local buckling at the base, is one of the main failure modes observed in these structures, and this failure mode can lead to their collapse and/or complete loss of contents. Although hydrostatic and hydrodynamic loads typically affect the seismic response of tanks, the effect of soil type on tank buckling behavior has not been widely studied or recognized. This research aims to evaluate the effect of soil type on seismic fragility of tanks by analyzing typical storage tanks used in the wine industry. The work focuses on elephant foot buckling for tanks with both unanchored and anchored bases and compares the influence of three different types of soil and two different tank geometries. The approach uses the capacity spectrum method, as opposed to the more commonly used incremental dynamic analysis, to determine a critical peak ground acceleration to cause buckling at the tank. The tanks were subjected to 21 Chilean seismic records with three different soil types and a no-soil condition. From the results a lognormal fragility curve, and its median and standard deviation, are calculated. The results indicate that unanchored tanks built softer soils exhibit poorer performance, while tanks in competent soils and rock exhibit good performance. Anchored tanks show less sensitivity to soil types than unanchored tanks. The study demonstrates the importance of considering soil-foundation-structure interaction for wine storage tanks, but the results indicate that many comparable storage structures will be similarly affected.

Hazard analysis of earthquake in Pleret, Bantul Regency, Yogyakarta Special Region based on microtremor data

Pleret, one of the sub-districts in Bantul Regency, Yogyakarta, is prone to earthquakes. This area is traversed by an active fault system known as the Opak fault, with a maximum magnitude of 6.6 Mw. The local geological conditions can be determined based on microtremor data from 43 measurement points. The purpose of this research is to understand the dynamic characteristics of the local geological conditions, including the dominant frequency (f0), shear wave velocity (Vs30), peak ground acceleration (PGA), and to map the earthquake impacts on the population and structures. Microtremor measurements were conducted for approximately 20 minutes, revealing a dominant frequency range of 0.61-10.85 Hz. The HVSR curve inversion revealed the Vs30 range 213 to 826 m/s distribution. Peak Ground Acceleration (PGA) values varied from 0.16 to 0.26 g. Using InaSAFE software, the estimated affected population reached 49,052 people, with over 23,176 structures affected. The results of this mapping are expected to be utilized as a step towards enhancing disaster preparedness.

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

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

Spectral ratio analysis of the site amplification effect and nonlinear site response at MYGH10 during the 2021 MW 7.1 Fukushima earthquake in Japan

AbstractGround motion recording with peak ground acceleration (PGA) exceeding 1.43 g was observed at the Yamamoto (MYGH10) station during the MW 7.1 Fukushima earthquake off the east coast of Honshu, Japan, in February 2021. In this study, we investigated the site amplification effect and nonlinear site response at the MYGH10 site using the horizontal‐to‐vertical spectral ratio (HVSR) and surface‐to‐borehole spectral ratio (SBSR), respectively. The site transfer function of station MYGH10 was presented using HVSRs (HVSR on the ground surface) and SBSR at different frequency bands. The dominant contributing frequency band of the PGA was determined by increasing the high cutoff frequency of the bandpass filter when filtering the acceleration recording, which was used to interpret the anomaly of the PGA by combining it with the site characteristics. We found an obvious site effect below the downhole sensor through the HVSRb (HVSR on the downhole bedrock) and determined the influence of the frequency band of the downhole site on ground motion. In addition, substantial discrepancies in the frequency and amplitude between the spectral ratio curves for the Fukushima mainshock records and the weak historical motions were identified. The calculated values of the degree of nonlinearity suggested that a strong nonlinear site response occurred at station MYGH10. Finally, the recovery of the site after strong shaking was evaluated by using the average spectral ratio curves of several aftershock records.

The impact of blast vibration on the site response

In civil engineering, one of the main problems is the stability of structures. Blast vibrations can be one of several elements influencing the stability of these structures. In order to prevent any effects on the structures, the maximum suggested values of the peak particle velocity and frequency from the blast are generally determined using the existing appropriate government rules and industry standards as reference. In this paper, the effect of these vibration on the site response has been investigated. Parameters such as peak ground acceleration, Pseudo spectral acceleration and maximum stress and strain has been compared for two different soil models. The results indicate that the soft soil has a significant impact and amplifies the input parameters considerably.

Evaluating the identification methods of permanent displacement derived from strong motion records: Case studies in the 2023 Turkey–Syria earthquake

AbstractOn February 6, 2023, the Republic of Turkey experienced a rare occurrence of two successive earthquakes, each with a magnitude exceeding 7.0. The Disaster and Emergency Management Agency (AFAD) swiftly shared the strong motion records, thereby enriching the global database of near‐fault strong motion records. Identification of permanent displacement is essential for effectively utilizing these records. In this study, we refined the permanent displacement identification method, which combines the Hermit interpolation baseline correction with flatness determination by incorporating a low‐pass filter. Following this, we compared four permanent displacement identification methods, including our improved approach. We applied these to the strong motion record of station 4404 and compared the results with the Global Positioning System coseismic displacement. At the same time, we used field investigation data to verify the effectiveness of our improved method, studying its applicability for both single‐wave packet and multiwave packet records. The conclusions are the following: the improved method provides a more reasonable and effective means of identifying permanent displacement. When the peak ground acceleration (PGA) exceeds 1 g, the permanent displacement identifications from the four methods differ significantly. The discrepancy in permanent displacements identified by the four methods in the horizontal direction is larger than that in the U−D direction. For the record with the largest PGA (station 4614), our improved method yields more reasonable results compared with other techniques. Furthermore, the choice of segmentation time nodes in the velocity time history significantly affects the identification of permanent displacement.

Time history comparative analyses of a thin-walled cold-formed steel structure considering P-D effect

The paper presents a comparative time history analysis based on the 1D-beam Finite Element Method (FEM) for a thin-walled cold-formed steel structure previously investigated both theoretically and experimentally. The recent earthquake which occurred in Turkey with 7.8Mw and a Peak Ground Acceleration (PGA) of almost 0.5g showed numerous collapses of different types of buildings. Romania has an active seismic area located in the Vrancea region where, in 1977 a 7.5Mw earthquake also occurred. In this context, this paper presents a comparative analysis with time history accelerograms of the recorded earthquake in North – South and East - West directions. The structural model was analyzed in Robot Structural Analysis software in two scenarios of the time history accelerograms considering also the P-Δ effect. The finite elements of the structure were defined as 1-D beam elements with hinged joints and limited axial stiffness previously obtained in past studies. The results showed a substantial increase in terms of displacements and stresses.