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A new attenuation model is proposed to estimate the cumulative absolute velocity (CAV) of strong ground motions based on an updated earthquake database of the earthquakes occurred in Iran. The strong ground motion database is comprised from 4562 acceleration records of horizontal components associated with 574 earthquakes recorded in Iran during 1975–2019. A multiple regression procedure is used to develop the functional form of the predictive model as a function of the average soil shear wave velocity at the top 30 m ( V s30 ) of the recording stations, the moment magnitude ( M w ), and the epicentral distance ( R ). Performance of the presented attenuation relationship is investigated by a parametric study and compared with some selected local-scale models recently proposed for CAV. Assessment of the model demonstrates that the magnitude-dependency of CAV is significant across all epicentral distances. The distance-dependency of CAV is particularly pronounced for M w  > 5.5. The influence of V s30 becomes particularly prominent at extended epicentral distances exceeding ⁓50 km. The estimation of the liquefaction-induced lateral spreading is presented as an application of the proposed model for a typical site in Iran. The seismic sliding displacement is predicted ⁓31 cm, diminishing to ⁓2.5 cm when pore water pressure buildup is excluded from the calculations.

An early warning system forewarns an urban area of the forthcoming strong shaking, normally with a few seconds to a few tens of seconds of early warning time before the arrival of the destructive S-wave part of the strong ground motion. For urban and industrial areas susceptible to earthquake damage, where the fault rupture system is complex and the fault-site distances are short, there is usually insufficient time to compute the hypocenter, focal parameters and the magnitude of an earthquake. Therefore, simpler and robust early warning algorithm is needed. The direct (engineering) early warning systems are based on algorithms of the exceedance of specified threshold time domain amplitude levels. The continuous stations’ data are processed to compute specific engineering parameters robustly and compared with specified threshold levels. The parameters can be chosen as band-pass filtered peak ground accelerations and/or the bracketed cumulative absolute velocity (BCAV). In this paper, an enhancement to bracket cumulative absolute velocity for the application of online urban early warning systems results in a new parameter called window based bracketed cumulative absolute velocity (BCAV-W). The BCAV-W allows computation of cumulative absolute velocity in a specified window size and to include the vertical component of the motion. The importance of choosing optimum window size for the cumulative absolute velocity BCAV-W is discussed and the correlations between BCAV-W and the macro-seismic intensity are studied for three combinations of horizontal and vertical components of the motion. Empirical relationship is developed to estimate BCAV-W as a function of magnitude, distance, fault mechanism, and site category based on 1,208 recorded ground motion data from 75 earthquakes in active plate-margins.

This study presents empirical ground-motion models (GMMs) for estimating Arias intensity (I A ), cumulative absolute velocity (CAV) and significant ground-motion duration (D 5–75 and D 5–95 ), calibrated on Iranian strong motion database. The dataset consists of 1749 (with two horizontal components) acceleration motion time-series originated from 566 events with moment magnitude (M w ) 3–7.5 range and recorded at 338 stations in the distances range up to 200 km. Common functional forms were adopted for all four models to facilitate easy comparison of derived model parameters and model predictions. Residual distributions and their unbiased variation with predictor variables M w , hypocentral distance (R hypo ), time-averaged shear-wave velocity in the top 30 m (V S30 ) indicated robustness of the derived models. This study also examines residual correlations between different pairs of ground-motion intensity measures (GMIMs). The correlations were analysed separately for between-event ( δ B e ) and within-event ( δ W S es ) component of the residuals. The correlation of δ B e between: (1) I A and the two duration measures (D 5–75 and D 5–95 ), (2) CAV and the two duration measures were found depending upon the event magnitude (strongest for M w > 6). Similarly, the correlation of δ W S es between: (1) I A and the two duration measures, (2) CAV and the two duration measures were observed depending upon source-to-site distance (strongest for R hypo < 50 km). Furthermore, a relatively stronger negative correlation of δ W S es was observed between CAV and station-specific attenuation parameter (κ 0 ) (mainly at softer soil sites) in comparison to that between I A and κ 0 .

We present predictive models of cumulative absolute velocity (CAV), Arias intensity (I A ) and strong-motion duration (SD) by using a ground-motion database compiled from the broader European region. The same database was previously used to develop a set of pan-European ground-motion prediction equations (GMPEs) for 5%-damped horizontal and vertical elastic spectral ordinates as well as for damping scaling factors to modify 5%-damped spectral ordinates for a suite of damping values ranging from 1 to 50% (Akkar et al. in Bull Earthq Eng 12:517–547, 2014a ; 1429–1430, 2014b ; 359–387, 2014c ; 389–390, 2014d ). We present the CAV, I A and SD predictive models together with the correlation coefficients to consider their interdependency with the spectral ordinates estimated by the 2014 horizontal Akkar et al. GMPEs. Thus, together with these new predictive models a consistent ground-motion modeling (including conditional and joint probability hazard) is now possible for a wide range of engineering problems (from liquefaction hazard to probabilistic loss modeling) in the pan-European region. As in the case of previous 2014 Akkar et al. GMPEs, the predictive models in this paper are valid for shallow active crustal regions with point- and extended-source distances <200 km and moment magnitudes between 4 ≤ M w  ≤ 8. They are capable of representing soil conditions between 150 m/s ≤ V S30  ≤ 1200 m/s.

Rapid and accurate estimation of emergency response parameters during earthquakes is important in earthquake early warning (EEW) systems. Because earthquake rupture is not instantaneous, to accurately, safely, and reliably determine parameters and thresholds for emergency response, the cumulative absolute velocity (CAV) is used as the target parameter, and 7 P-wave characteristic parameters of strong ground motion records occurring 3 s after P-wave arrival at K-NET and KiK-net stations in Japan are used as inputs to construct a machine learning (ML) CAV prediction model based on the support vector machine (SVM) algorithm. The results show that compared with a single-parameter prediction algorithm, the proposed ML model can significantly reduce the error standard deviation and effectively address the phenomena of small value overestimation and large value underestimation. A confusion matrix analysis demonstrates that the 6-parameter model Pa&Pv&Pd&CAV&Ia&IV2 shows the best performance in improving the prediction accuracy and provides a threshold selection strategy for threshold-based EEW emergency response.

Machine learning (ML)-aided structural health monitoring (SHM) can rapidly evaluate the safety and integrity of the aging infrastructure following an earthquake. The conventional damage features used in ML-based SHM methodologies face the curse of dimensionality. This paper introduces low dimensional, namely, cumulative absolute velocity (CAV)-based features, to enable the use of ML for rapid damage assessment. A computer experiment is performed to identify the appropriate features and the ML algorithm using data from a simulated single-degree-of-freedom system. A comparative analysis of five ML models (logistic regression (LR), ordinal logistic regression (OLR), artificial neural networks with 10 and 100 neurons (ANN10 and ANN100), and support vector machines (SVM)) is performed. Two test sets were used where Set-1 originated from the same distribution as the training set and Set-2 came from a different distribution. The results showed that the combination of the CAV and the relative CAV with respect to the linear response, i.e., RCAV, performed the best among the different feature combinations. Among the ML models, OLR showed good generalization capabilities when compared to SVM and ANN models. Subsequently, OLR is successfully applied to assess the damage of two numerical multi-degree of freedom (MDOF) models and an instrumented building with CAV and RCAV as features. For the MDOF models, the damage state was identified with accuracy ranging from 84% to 97% and the damage location was identified with accuracy ranging from 93% to 97.5%. The features and the OLR models successfully captured the damage information for the instrumented structure as well. The proposed methodology is capable of ensuring rapid decision-making and improving community resiliency.

The present paper investigates the effect of the harmfulness of a potential earthquake on structural and seismic risks. It takes into account the magnitude, epicentral distance, and pseudo depth at the hypocenter as well as the soil classification in order to generate synthetic seismic motions to be considered as signal inputs for a structural seismic analysis. The most typical typology of dwellings and buildings that are widely existing in Algeria, i.e., a reinforced concrete frame structure, is considered for the case study. The results show that the theoretical models developed in this study are able to predict the performance point (spectral displacement) according to the cumulative absolute velocity. They also show that(CAV-S ) (S being the spectral displacement of the performance point defined by a pushover analysis) is slightly influenced by the value of the ultimate displacements of the structures and the soil parameters (shear velocity Vs).

This paper presents two on-going efforts of the Pacific Earthquake Engineering Research (PEER) center in the area of structural health monitoring. The first is data-driven damage assessment, which focuses on using data from instrumented buildings to compute the values of damage features. Using machine learning algorithms, these damage features are used for rapid identification of the level and location of damage after earthquakes. One of the damage features identified to be highly efficient is the cumulative absolute velocity. The second is vision-based automated damage identification and assessment from images. Deep learning techniques are used to conduct several identification tasks from images, examples of which are the structural component type, and level and type of damage. The objective is to use crowdsourcing, allowing the general public to take photographs of damage and upload them to a server where damage is automatically identified using deep learning algorithms. The paper also introduces PEER.s effort and preliminary results in engaging the engineering and computer science communities in such developments through the PEER Hub Image-Net (F-Net) challenge.

By referring to the two strongest earthquakes of the 2016–2017 Central Italy seismic sequence, this paper presents a procedure to make shaking maps through empirical relationships between macroseismic intensity and ground-motion parameters. Hundreds of waveforms were processed to obtain instrumental ground-motion features which could be correlated with the potential damage intensities. To take into account peak value, frequency, duration, and energy content, which all contribute to damage, cumulative absolute velocity and Arias intensity were used to quantify the features of the ground motion. Once these parameters had been calculated at the recording sites, they were interpolated through geostatistical techniques on the whole struck area. Finally, empirical relationships were used for mapping intensities, i.e., potential effects on the built environment. The results referred to both earthquake scenarios that were analyzed and were also used for assessing the influence of the spatial coverage of the instrumental network. In fact, after the first events, the Italian seismic network was subjected to the addition and thickening of sensors in the epicentral area, especially. The results obtained by models only dependent on ground-motion parameters or even on the epicentral distance were compared with the official ShakeMaps and the observed intensities for assessing their reliability. Finally, some suggestions are proposed to improve the procedure that could be used for rapidly assessing ground shaking and mapping damage potential producing useful information for non-expert audience.

In this paper, the application of the energy-based excess pore pressure generation model using damage potential to the assessment of liquefaction potential is examined through experimental and analytical investigations. For a more realistic description of the dynamic responses of saturated sands, the model parameters of the proposed model were modified based on the relative density. Dynamic undrained triaxial tests were performed for sand with different relative densities. Based on the test results, new equations for the model parameters with the relative density were addressed. Further, a liquefaction potential assessment procedure using the model based on the maximum cumulative excess pore pressure ratio is also proposed. Factors of safety calculated from the stress-based method and the proposed method were compared through examples with different soils and earthquake conditions.