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Linear elastic pseudo-velocity response spectra for the strong motion data considered in this study calculated by OpenSeismoMatlab and SeismoSignal.
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OpenSeismoMatlab is an innovative open-source software for strong ground motion data processing, written in MATLAB. The software implements an elastoplastic bilinear kinematic hardening constitutive model and uses a state-of-the-art single step single solve time integration algorithm featuring exceptional speed, robustness and accuracy. OpenSeismoM...
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... A key reason for selecting this tool is that it is easy to use, reliable and popular among researchers worldwide. For validation, the derived results were compared with the OpenSeismoMatlab tool developed by Papazafeiropoulas and Plevris36 . It is known that conventional time integration algorithms are incorporated in Seismosignal to calculate the dynamic response. ...
This study evaluates the constant ductility displacement
response spectra (CDDRS) for near-fault ground motions
(NFGMs) of the 1999 Chi-Chi earthquake in Taiwan.
The complex source mechanism was mainly caused by
a rupture on the Chelungpu fault that spanned about
100 km. A dataset of 63 NFGM records was considered to
understand the effects of CDDRS due to hanging wall,
foot wall and directivity effects. CDDRS were generated
using SeismoSignal tool for the selected ground motions.
Significant variation was observed in both elastic and
inelastic CDDRS for tall buildings due to the hanging
wall effect. In addition, variations in the inelastic spectra
were found to be more prominent due to the backward
directivity effect. Moreover, the inelastic displacement
spectral values for pulse-like ground motions were much
higher than those associated with non-pulse ground
motions.
... This process is carried out to extract the initial damages, which are considered as input features, as well as the cumulative damages, which are considered the target variable of the dataset. The remaining input features (IMs) derived from the second shocks seismic signals, are calculated using Python [105] and NumPy [106] code, whereas the computation of acceleration spectra is performed using OpenSeismoMatlab [107]. The histograms and the probability density curves for all variables across the total dataset are provided in Figure A1 in Appendix A. ...
Recently developed Machine Learning (ML) interpretability techniques have the potential to explain how predictors influence the dependent variable in high-dimensional and non-linear problems. This study investigates the application of the above methods to damage prediction during a sequence of earthquakes, emphasizing the use of techniques such as SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDPs), Local Interpretable Model-agnostic Explanations (LIME), Accumulated Local Effects (ALE), permutation and impurity-based techniques. Following previous investigations that examine the interdependence between predictors and the cumulative damage caused by a seismic sequence using classic statistical methods, the present study deploy ML interpretation techniques to deal with this multi-parametric and complex problem. The research explores the cumulative damage during seismic sequences, aiming to identify critical predictors and assess their influence on the cumulative damage. Moreover, the predictors contribution with respect to the range of final damage is evaluated. Non-linear time history analyses are applied to extract the seismic response of an eight-story Reinforced Concrete (RC) frame. The regression problem’s input variables are divided into two distinct physical classes: pre-existing damage from the initial seismic event and seismic parameters representing the intensity of the subsequent earthquake, expressed by the Park and Ang damage index (DIPA
) and Intensity Measures (IMs), respectively. In addition to the interpretability analysis, the study offers also a comprehensive review of ML methods, hyperparameter tuning, and ML method comparisons. A LightGBM model emerges as the most efficient, among 15 different ML methods examined. Among the 17 examined predictors, the initial damage, caused by the first shock, and the IMs of the subsequent shock—IFVF
and SIH
—emerged as the most important ones. The novel results of this study provide useful insights in seismic design and assessment taking into account the structural performance under multiple moderate to strong earthquake events.
... A brief depiction of the formulas for the studied IMs is given in Table 1. IMs values are derived with Python [87] and NumPy [88] code, whereas the computation of acceleration spectra is performed using OpenSeismoMatlab [89]. ...
This study investigates the interpretability of machine learning (ML) models applied to cumulative damage prediction during a sequence of earthquakes, emphasizing the use of techniques such as SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDPs), Local Interpretable Model-agnostic Explanations (LIME), Accumulated Local Effects (ALE), Permutation and Impurity-based technique. The research explores the cumulative damage during seismic sequences, aiming to identify critical predictors and assess their influence on the cumulative damage. Moreover, the predictors contribution in respect with the range of final damage is evaluated. Nonlinear time history analyses are applied to extract the seismic response of an eight-story Reinforced Concrete (RC) frame. The regression problem’s input variables are divided into two distinct physical classes: pre-existing damage from the initial seismic event and seismic parameters representing the intensity of the subsequent earthquake, expressed by Park and Ang damage index (DIPA) and Intensity Measures (IMs), respectively. The study offers a comprehensive review of cutting-edge ML methods, hyperparameter tuning, and ML method comparisons. A LightGBM model emerges as the most efficient, among 15 different ML methods examined, with critical predictors for final damage being the initial damage caused by the first shock and the IMs of the subsequent shock: IFVF and SIH. The importance of these predictors is supported by feature importance analysis and local/global explanation methods, enhancing the interpretability and practical utility of the developed model.
... There are two main earthquake monitoring networks currently operating in Türkiye: Figure 1, together with the epicenter of the earthquake event. For the processing of the acceleration time histories, the open-source Matlab code OpenSeismoMatlab [30] was used, which has been developed by the authors and is quite reliable since it has been successfully verified in several cases in the literature [31][32][33]. The software uses an advanced time integration algorithm first presented in [34]. ...
... The scaling levels are appropriately selected to force the structure through the entire range of behavior, from elastic to inelastic [29]. OpenSeismoMatlab [30] is capable of performing IDA analysis for a single record and an SDOF structure. Such IDA curves contain useful information about a seismic record, from a structural point of view. ...
... These are the PGA (plotted in Figure 23), effective PGA (EPGA, according to [43], plotted in Figure 24), PGV (plotted in Figure 25), spectral intensity defined according to [44] (plotted in Figure 26), spectral intensity according to [45] (plotted in Figure 27), Arias intensity (plotted in Figure 28), and significant duration (plotted in Figure 29). All the aforementioned parameters have been calculated using the OpenSeis-moMatlab software [30], only for horizontal strong ground motion components. It is noted that the µLN and σLN parameters of the lognormal distribution that appear in the legends of the histogram plots are different from the mean value and standard deviation of the data being plotted. ...
The effects on structures of the earthquake with the magnitude of 7.8 on the Richter scale (moment magnitude scale) that took place in Pazarcık, Kahramanmaraş, Türkiye at 04:17 a.m. local time (01:17 UTC) on 6 February 2023, are investigated by processing suitable seismic records using the open-source software OpenSeismoMatlab. The earthquake had a maximum Mercalli intensity of XI (Extreme) and it was followed by a Mw 7.5 earthquake nine hours later, centered 95 km to the north–northeast from the first. Peak and cumulative seismic measures as well as elastic response spectra, constant ductility (or isoductile) response spectra, and incremental dynamic analysis curves were calculated for two representative earthquake records of the main event. Furthermore, the acceleration response spectra of a large set of records were compared to the acceleration design spectrum of the Turkish seismic code. Based on the study, it is concluded that the structures were overloaded far beyond their normal design levels. This, in combination with considerable vertical seismic components, was a contributing factor towards the collapse of many buildings in the region. Modifications of the Turkish seismic code are required so that higher spectral acceleration values can be prescribed, especially in earthquake-prone regions.
... Örnek çalışmalardan bir tanesinde Yunanistan için mühendislik yer hareketi parametrelerinin ampirik bağıntılarla hesaplanarak, yer hareketi kaydının genliği, frekans içeriği, süresi ve enerjisinin etkilerini araştırarak sönüm ilişkisi denklemlerine dahil edilmiştir [1]. Benzer şekilde, mühendislik parametrelerini hesaplamak için, kuvvetli yer hareketi verilerini işlemek üzere yazılmış açık kaynaklı yazılım örneği olarak OpenSeismoMatlab yazılımı gösterilebilir [2]. OpenSeismoMatlab, çeşitli zaman serilerini ve tepe değerlerini, Arias yoğunluğunu, önemli süreyi, çeşitli lineer elastik tepki spektrumlarını, Fourier genlik spektrumunu ve ortalama periyodu hesaplayabilen bir yazılım algoritmasına sahiptir. ...
Yeni gelişen teknolojilerle beraber deprem mühendisliğindeki uygulamalarda kullanılmak üzere deprem karakteristik özelliklerinin ve mühendislik şiddet parametrelerinin hızlı ve pratik bir şekilde belirlenebilir olması önem arz etmekte olup, yazılımsal olarak parametrelerin entegrasyonu uygulamalarında artış gözlemlenmektedir. Bu çalışmada, Python yazılım dili kullanılarak, belli bir depreme ait, kuvvetli yer hareketi kayıtları zaman serisi kayıtlarından, maksimum ivme (Peak Ground Acceleration (PGA)), maksimum hız (Peak Ground Velocity (PGV)) ve maksimum yerdeğiştirme (Peak Ground Displacement (PGD)) değerleri hesaplanmış; kayıtlara ait hız-ivme-yerdeğiştirme zaman serileri üretilmiş; tepki spektrumları Türkiye Bina Deprem Yönetmeliği’nde (TBDY-2018) tanımlanmakta olan tasarım spektrumları ile karşılaştırılarak görselleştirilmiştir. Deprem sinyallerinin frekans içerikleri de ayrıca elde edilmiştir (Fourier Amplitude – Frequency grafikleri). Elde edilen çıktılar görselleştirilerek, yorum kolaylığı sağlanması amaçlanmıştır. Çalışma kapsamında geliştirilen açık kaynaklı yazılım (Depremlerin kARakteristik özeLliklerini Ayrıştırma: DARLA v1.0) yardımıyla, deprem meydana geldiği andan itibaren bölge özelinde deprem mühendisliği anlamında yarı-otomatik üretilen bilimsel veriler ışığında hızlı değerlendirme yapılması hedeflenmiştir.
... For the processing of the acceleration time histories, the open-source Matlab code OpenSeismoMatlab [5] was used, which has been developed by the authors and is quite reliable since it has been successfully verified in several cases in the literature [6][7][8]. ...
... The scaling levels are appropriately selected to force the structure through the entire range of behavior, from elastic to inelastic [11]. OpenSeismoMatlab [5] is capable of performing IDA analysis for a single record and a SDOF structure. Such IDA curves contain useful information about a seismic record, from a structural point of view. ...
The effects on structures of the earthquake with magnitude 7.8 on the Richter scale (moment magnitude scale) which took place in Pazarcik, Kahramanmaras, Turkiye at 04:17 a.m. local time (01:17 UTC) on February 6, 2023, are investigated by processing suitable seismic records using the open-source software OpenSeismoMatlab. The earthquake had a maximum Mercalli intensity of XI (Extreme) and it was followed by a Mw 7.5 earthquake nine hours later, centered 95 km to the north-northeast from the first. Peak and cumulative seismic measures as well as elastic response spectra, constant ductility (or isoductile) response spectra, and incremental dynamic analysis curves were calculated for two representative earthquake records of the main event. Furthermore, the acceleration response spectra of a large set of records were compared to the acceleration design spectrum of the Turkish seismic code. Based on the study, it is concluded that the structures were overloaded far beyond their normal design levels. This, in combination with considerable vertical seismic components, was a contributing factor towards the collapse of many buildings in the region. Modifications of the Turkish seismic code are required so that higher spectral acceleration values can be prescribed, especially in earthquake-prone regions.
... In the last few years, numerous papers have focused on the issue of strong-motion data-processing and quality-control procedures. In many cases, schemes for data-processing procedures (e.g., Boore, 2005;Boore and Bommer, 2005;Akkar and Boore, 2009;Massa et al., 2010;Pacor, Paolucci, Ameri, et al., 2011;Pacor, Paolucci, Luzi, et al., 2011;Paolucci et al., 2011;Boore et al., 2012;Puglia et al., 2018), open-source tools (e.g., Weber et al., 2007;Hosseini and Sigloch, 2017;Jones et al., 2017;Kalkan and Stephens, 2017;Hearne et al., 2019;Petersen et al., 2019;Zaccarelli et al., 2019), and commercial software (e.g., Papazafeiropoulos and Plevris, 2018) for strongmotion data analyses are addressed to offline seismic waveforms datasets, relevant seismic sequences (e.g., Massa et al., 2016;Cara et al., 2019;Rekoske et al., 2020), or to the compilation of flat files for engineering applications (e.g., Akkar et al., 2010;Bindi et al., 2018;Lanzano et al., 2019;Bahrampouri et al., 2021). Moreover, machine learning models for the automatic detection of anomalies on data and metadata (Kleckner et al., 2021;Zaccarelli et al., 2021) or automated quality screening of ground-motion records from small-magnitude earthquakes (Bellagamba et al., 2019) have also been developed. ...
The strategy for data processing in the Engineering Strong-Motion Database (ESM) is to disseminate only manually revised data to ensure the highest quality. However, manual processing is no longer sustainable, due to the ever-increasing rate of digital earthquake records, from global, regional, and national seismic networks, and a new framework for strong-motion data processing is required, so that records are automatically processed and the human revision is restricted to selected significant records. To this end, we present ESMpro—a modular Python software for a renewed processing framework of ESM. The software is available in a stand-alone beta version to facilitate testing and sharing among the scientific community. ESMpro provides automatic settings for waveform trimming and filtering, along with the automatic recognition of poor-quality data and multiple events. ESMpro allows classifying each record in different quality classes to reduce manual revision on a subset of the incoming data. ESMpro also allows handling different processing techniques in a modular and flexible structure to facilitate the implementation of new or alternative algorithms and file formats. The testing performed on the ESM database results in a good correspondence between the automatic and manual data processing, supporting the migration toward fully automatic procedures for massive data processing.
... The ground acceleration time-history data of the Bhuj earthquake, Koyna earthquake, Anza earthquake, Nahanni earthquake and Northridge earthquake are taken for the dynamic analysis. George [17] analyzed the structural response of the MDOF system with response spectrum analysis by programming algorithms for different spectra. They analyzed and compared the results of acceleration, velocity and displacement spectra using 11 strong ground motion databases with different software applications. ...
... Area-wise non-dimensional parameter = (17) Thus, the mass at each floor (m) can be calculated as: ...
Random ground motions in horizontal, vertical and arbitrary directions radiate away from the focus or the hypocenter under the surface of the earth during an earthquake. When the earth shakes violently, the buildings, which act as vertical cantilevers, undergo vibrations inducing significant inertial forces. Large uncertainties are associated with the response of the buildings to random lateral forces; thus it is of paramount importance to understand the dynamic structural behavior of high rise buildings. Real world high rise building such as Burj Khalifa has been analyzed by response spectrum analysis with the lumped-mass model. The response of Burj Khalifa is simulated when subjected to ground acceleration motion of different earthquake recordings within a MATLAB framework. The ground motion acceleration databases of the El Centro earthquake in 1940 and Bhuj earthquake in 2001 are taken as inputs for the present analysis with other inputs pertaining to different storey masses, storey-stiffness, number of stories, damping ratios and mode shapes. Furthermore, numerical examples to demonstrate the impact of a safe seismic gap between adjacent buildings to prevent pounding or collision during seismic events are presented. It may be observed that computation and adoption of critical gaps between buildings facilitate the best-optimized use of land and provide safety against the pounding of multi-storey buildings under the effect of earthquake excitations.
... To this end, constant-ductility spectra for elastic-plastic SDOF oscillators have been constructed for motions with and without fling corresponding to a range of target displacement ductility (μ Δ = Δ m /Δ y ) values, viz., 1 (elastic), 3 and 5 ( Fig. 2b for 2% viscous damping) [40]. Constant ductility spectra plotting the values of peak displacement responses are obtained through a rigorous iterative solution of the actual equation of motion. ...
Within the context of near-fault motions with fling-step signature, the present paper reevaluates the applicability of a straightforward alternative to estimate the response to bidirectional shaking per unidirectional analysis. The method originally evolved for motions with forward-directivity, uses unidirectional analysis in a single incidence angle. The performance of the method is examined using reinforced concrete (RC) bridge piers as the representative single-degree-of-freedom (SDOF) oscillators. Synthetic fling has been introduced to two horizontal components of processed (base-line corrected and filtered) strong motions. It has been shown that the response to bidirectional excitation can be closely estimated by means of a unidirectional analysis using the ground motion components rotated to the most preferred orientation. Existence of the least preferred orientation in which the unidirectional analysis results in the maximum difference from the bidirectional analysis appears to reinforce the development. For a ground motion pair, these orientations can be identified in advance through characteristic ground motion parameters, viz., energetic length and characteristic intensity for peak and cumulative demand, respectively. The usefulness of the said orientations to estimate the Park-Ang damage index is also established. The efficacy of the simple methodology has also been demonstrated through fragility analysis. The straightforward method can hence be used in the near-fault regime regardless of the ground motion characteristics.
... to 10 with at a 0.01 step size using the OpenSeismo toolbox in MATLAB R2020b (Papazafeiropoulos and Plevris, 2018). The four time-series clusters and spectral clusters can be clearly identified in Figure 3. ...
Clustering analysis of sequence data continues to address many applications in engineering design, aided with the rapid growth of machine learning in applied science. This paper presents an unsupervised machine learning algorithm to extract defining characteristics of earthquake ground-motion records, also called latent features, to aid in ground-motion clustering and selection. In this context, a latent feature is a low dimensional machine-discovered spectral characteristic learned through nonlinear relationships of a neural network autoencoder. Clustering can be performed on the latent features and used to select a representative archetypal subgroup from a large ground-motion suite. The objective of efficient ground-motion selection is to choose records representative of what the structure will probabilistically experience in its lifetime. Three examples are presented to validate this approach, including a synthetic spectral dataset and spectra from field recorded ground-motion records. Deep embedding clustering of ground motion spectra improves on the results of static feature extraction, utilizing characteristics that represent the sparse spectral content of ground motions.
