Selection of the optimal intensity measure is an important contribution to reducing the numerous uncertainties in seismic inputs within the context of performance-based earthquake engineering, especially for unreinforced masonry buildings that exhibit strong nonlinear behaviour. While traditional metrics such as efficiency, sufficiency, and practicality have been successfully used to determine optimal intensity measures for seismic demand models and fragility curves, the impact of different intensity measures on the final vulnerability curves has not been sufficiently investigated. Therefore, a new vulnerability-based metric is proposed, based on the vulnerability curve variance and its first derivative, with the aim of determining the optimal intensity measure for new vulnerability models of mid-rise unreinforced masonry buildings. Both traditional and new metrics were used to evaluate the performance of common intensity measures, using a typical unreinforced masonry building located in Zagreb, Croatia as a case study. The new metric produced intensity measure rankings in line with traditional metrics, but additionally proved effective in quantifying the impact of intensity measure choice on the final vulnerability curve, making it a reliable tool for vulnerability modelling. Average spectral acceleration and peak ground velocity were among the best performing intensity measures, confirming their use for unreinforced masonry buildings.

Abstract Accurate prediction and synthesis of seismic waveforms are crucial for seismic‐hazard assessment and earthquake‐resistant infrastructure design. Existing prediction methods, such as ground‐motion models and physics‐based wavefield simulations, often fail to capture the full complexity of seismic wavefields, particularly at higher frequencies. This study introduces HighFEM , a novel, computationally efficient, and scalable generative model for broadband stochastic seismic‐waveform generation. Our approach leverages a spectrogram representation of the seismic‐waveform data, which is reduced to a lower‐dimensional manifold via an autoencoder. A state‐of‐the‐art diffusion model is trained to generate this latent representation conditioned on key input parameters: earthquake magnitude, recording distance, site conditions, hypocenter depth, and azimuthal gap. The model generates waveforms with frequency content up to 50 Hz. Any scalar ground‐motion statistic, such as peak ground‐motion amplitudes and spectral accelerations, can be readily derived from the synthesized waveforms. We validate our model using commonly employed seismological metrics and performance metrics from image‐generation studies. Our results demonstrate that the openly available model can generate realistic high‐frequency seismic waveforms across a wide range of input parameters, even in data‐sparse regions. For the scalar ground‐motion statistics commonly used in seismic‐hazard and earthquake‐engineering studies, we show that our model accurately reproduces both the median trends of the real data and their variability. To evaluate and compare the growing number of these and similar “Generative Waveform Models” (GWMs), we argue that they should be openly available and included in community ground‐motion‐model evaluation efforts.

ABSTRACT This study presents a seismic microzonation assessment of the Erenler district (Sakarya, Türkiye), located along the North Anatolian Fault Zone. A total of 37 ambient noise, 50 seismic, and 51 borehole datasets were integrated to evaluate local site effects. Predominant frequency ranges 0.83−12.5 Hz, while engineering bedrock depth varies between 9 and 286 m. The average 30-meter shear wave velocity spans from 144 to 523 m/s, with the lowest values observed in northern Quaternary alluvium and the highest in southern zones with coarse sediments. 1D nonlinear site response analysis for the 1999 İzmit Earthquake scenario yield peak ground accelerations of 0.08–0.41 g, and spectral accelerations frequently exceed design spectra, particularly at 1.0 s periods. Liquefaction potential, evaluated using the liquefaction potential index and the Ishihara boundary curve, indicates high susceptibility in areas with shallow groundwater and silty sands. However, areas underlain by thick non-liquefiable layers (>3–7.5 m) exhibit no evidence of surface manifestations. An integrated seismic hazard index map, produced by weighting seven criteria within the Analytic Hierarchy Process (AHP) framework, reveals high and very high hazard zones north of the Sapanca–Akyazı fault segment. The findings confirm the effectiveness of AHP-based microzonation for seismic risk mitigation and urban planning.

ABSTRACT In this study, we simulate ground motions for three major earthquakes that occurred in Central Italy: the 2009 Mw 6.3 L’Aquila earthquake and the two largest events of the 2016 Amatrice–Visso–Norcia sequence, the Mw 6.2 Amatrice and Mw 6.5 Norcia earthquakes. These events caused widespread damage, intense ground shaking, and casualties, making them key cases for evaluating and improving ground-motion modeling techniques, especially given the high-quality recordings available from near-source stations. We use the frequency–wavenumber (f-k) method to generate broadband synthetic ground motions ≤7 Hz. This approach balances computational efficiency with physical accuracy, making it well suited for regional-scale applications. Green’s functions are computed from the Central Italian Apennines 1D layered velocity model (Herrmann et al., 2011), and kinematic rupture models are generated using the methodology of Graves and Pitarka (2016; hereafter, GP16), implemented into the f-k framework. Site response is incorporated using frequency-dependent nonlinear amplification factors (Borcherdt, 1994). We evaluate the simulations through a detailed comparison with observed ground motions using RotD50 spectral acceleration and a goodness-of-fit analysis. In addition to simulations at recorded stations, we compute ground motions over a dense grid of virtual sites to assess spatial variability and source-related effects such as rupture directivity and regional amplification. Results are benchmarked against predictions from the empirical ground-motion model ITA18 (Lanzano et al., 2019). Our findings demonstrate that the f-k method can reproduce key features of observed ground motion with good agreement, capturing both near-fault effects and broader regional trends. This study demonstrates that the f-k approach offers a computationally efficient and physically robust alternative for deterministic ground-motion simulation, bridging empirical models and 3D simulations for practical use in hazard scenarios and urgent computing workflows.

ABSTRACT This study provides an updated probabilistic seismic hazard model for Maharashtra state. A new and updated earthquake catalogue including events from January 1600 to October 2021 has been compiled. Six declustering trials using various declustering methods are applied to develop the corresponding six earthquake catalogues. A seventh catalogue (C1) is compiled by extracting common events from these six catalogues. Fault recurrence parameters for linear sources are derived using the parameters estimated from C1. Further, six ground motion prediction equations developed for Peninsular India are initially considered and subsequently reduced to four based on their performance using the global intraplate earthquake ground motion records. Three logic tree models are used which include linear and areal source contributions to estimate peak ground acceleration. Seismic hazard maps are developed for return periods of 2475 and 475 years, evaluating PGA and spectral acceleration at 0.2 and 1.0 s at the bedrock level. Koynanagar and Panvel regions in the western part of the state exhibit higher PGA values for both return periods. The estimated seismic hazard for Area A1 is lower than the values specified by IS (1893) (2016), whereas some areas in A2, A3, and A4 exhibit higher hazard values than those specified in the code.

This article assesses the seismic design provisions for floor diaphragms per ASCE/SEI 7-22 Section 12.10.1 and Section 12.10.3, with emphasis on how these provisions account for the higher-mode effects on peak horizontal floor accelerations of earthquake-resistant buildings. A framework is developed for processing and analyzing strong-motion acceleration data from buildings monitored by the California Strong Motion Instrumentation Program (CSMIP), which is used to assess the floor diaphragm design acceleration coefficients in ASCE/SEI 7-22. These coefficients, which estimate peak floor accelerations at the design-level earthquake, are modified in this study to consider the intensity of measured ground motions and the level of earthquake-induced inelastic building response. First, the spectral response factors, defined as the ratio between the spectral response acceleration at a given floor and that at the ground, are evaluated at the second- and third-mode periods and compared with those at the first-mode period to assess the higher-mode effects. Then, two metrics are defined to compare the modified acceleration coefficients with the measured peak floor accelerations. The results confirm the need to consider the characteristic dynamics of the buildings to estimate the design forces for floor diaphragms. This consideration is included in ASCE/SEI 7-22 Section 12.10.3 but not rationally considered in Section 12.10.1. The modified acceleration coefficients based on ASCE/SEI 7-22 Section 12.10.1 can considerably underestimate the peak floor accelerations, whereas those based on Section 12.10.3 can capture the increased peak floor accelerations caused by the higher-mode earthquake-induced inertial forces. Thus, the authors recommend using ASCE/SEI 7-22 Section 12.10.3 over Section 12.10.1 to estimate the design forces for floor diaphragms; however, certain assumptions considered in Section 12.10.3 require further revision.

ABSTRACT The vertical component of ground motion plays an important role in the seismic response of structures such as large-span bridges and nuclear power plant components. To investigate the correlation coefficients of horizontal and vertical ground motions for seismic hazards in China, in this research, based on the ground motion records provided by the Institute of Engineering Mechanics of the China Earthquake Administration from 2007 to 2020, firstly, the horizontal and vertical Chinese GMPEs were fitted to calculate the correlation coefficients. Subsequently, horizontal spectral acceleration-horizontal spectral acceleration (SAH-SAH), horizontal spectral acceleration-vertical spectral acceleration (SAH-SAv), and vertical spectral acceleration-vertical spectral acceleration (SAv-SAv) correlation coefficients were calculated in the period range of 0.01–4.0 s based on the fitted Chinese GMPEs. The observed correlations were then compared with the widely used parametric model proposed by Kohrangi et al., and the same trend was found within the study period. Furthermore, the correlation coefficients of other IM combinations were calculated, and parametric equations were fitted. Finally, the magnitude and distance dependence of the correlation coefficients was investigated. The results of this research provide the necessary database for hazard-consistent selection records of vertical ground motions and vector-valued probabilistic seismic hazard assessments in China.

Soil type is a critical factor influencing the seismic performance of buildings, as it affects the level of damage sustained during earthquakes. This paper presents a novel approach to classifying building soil types using pseudo-spectral acceleration readings recorded during seismic events. By leveraging machine learning classifiers, the study develops a model that accurately identifies soil types from pseudo-spectral acceleration data, achieving an accuracy of 89.16%. The methodology involves preprocessing the seismic data, extracting key features, and applying various classifiers to determine the most effective model. Performance is evaluated using metrics such as accuracy, precision, and recall. The findings indicate that this approach significantly improves soil classification accuracy over traditional methods, providing a practical tool for seismic hazard assessment and building design. This research further advances earthquake engineering by offering a data-driven solution to enhance building resilience.

ABSTRACT This study aims to investigate long-term modal parameter changes in Hagia Sophia in Istanbul as a function of atmospheric parameters. The structure dates back to the 6th century and is on the UNESCO World Heritage list. The primary structural materials of the monument are fired clay bricks, lithic components (limestone, granite), and a characteristic mortar. We determined variations in modal frequencies, modal damping ratios, and mode shapes over 4 years, between 2013 and 2017. Transfer functions were used for the estimation of modal frequencies, the half-power bandwidth method for the modal damping ratio, and frequency domain decomposition for mode shapes. The long-term variation of the mode shapes was investigated using the MAC, COMAC, and ECOMAC methods. The results showed that the modal frequencies of Hagia Sophia strongly depend on temperature. An increase in temperature likely causes microcracks to close, increasing the rigidity and modal frequency. Therefore, the spectral accelerations corresponding to the modal frequencies change significantly in the response spectra obtained using past earthquakes. Modal damping is influenced by many atmospheric factors, but the most effective one is precipitation. Mode shapes are only marginally affected by environmental factors.

This study proposes the first ground motion prediction equation (GMPE) for the parameter INp, an intensity measure based on the spectral shape. A Machine Learning Algorithm based on Support Vector Machines (SVMs) was employed due to its robustness towards outliers, which is a key advantage over ordinary linear regression. INp also offers a more robust measure of the ground motion intensity than the traditionally used spectral acceleration at the first mode of vibration of the structure Sa(T1). The SVM algorithm, configured for regression (SVR), was applied to derive the prediction coefficients of INp for diverse vibration periods. Furthermore, the complete dataset was analyzed to develop a unified, generalized expression applicable across all the periods considered. To validate the model’s reliability and its ability to generalize, a cross-validation analysis was performed. The results from this rigorous validation confirm the model’s robustness and demonstrate that its predictive accuracy is not dependent on a specific data split. The numerical results show that the newly developed GMPE reveals high predictive accuracy for periods shorter than 3 s and acceptable accuracy for longer periods. The generalized equation exhibits an acceptable coefficient of determination and Mean Squared Error (MSE) for periods from 0.1 to 5 s. This work not only highlights the powerful potential of machine learning in seismic engineering but also introduces a more sophisticated and effective tool for predicting ground motion intensity.

Evaluation of seismic risk by capturing the influences of strong motion duration and frequency contents of ground motion through probabilistic approaches is the main element of this study. Unlike most existing studies that mainly focus on intensity measures such as peak ground acceleration or spectral acceleration, this work highlights how duration and frequency characteristics critically influence dam response. To achieve this, a total of 45 ground motion records, categorized by strong motion duration (long, medium, and short) and frequency content (low, medium, and high), were selected from the PEER database. Nonlinear numerical dynamic analysis was performed by scaling each ground motion from 0.05 g to 0.5 g, with the drift ratio at the dam crest used as the Engineering Demand Parameter. It is revealed that long-duration and low-frequency ground motions induced significantly higher drift demands. The fragility analysis was conducted using a lognormal distribution considering extensive damage threshold drift ratio. Finally, the probabilistic seismic risk was carried out by integrating the site-specific hazard curve and fragility curves which yield the height risk for long durations and low frequencies. The outcomes emphasize the importance of ground motion strong duration and frequency in seismic performance and these findings can be utilized in the dam safety evaluation.

Indonesia is located in a tectonically active region influenced by the interactions of several tectonic plates. This tectonics setting give rise to numerous active faults and subduction zones, making Indonesia highly susceptible to earthquakes. To mitigate earthquake risk, seismic hazard assessments are essential and contribute directly to the development of earthquake-resistant building codes or premium assets estimation for assets insurance. This study aims to assess seismic hazard analysis in Sumatra and Kalimantan using the Event-Based Probabilistic Seismic Hazard Analysis (EB-PSHA) method for a 250-year return period (0.4% annual exceedance probability in one year) for Peak Ground Acceleration (PGA) and Spectral Acceleration (SA) at 0.3 s and 0.6 s. Three seismic source models, Active Shallow Crusts, Subduction Interfaces, and Background Sources, are used in this analysis. A combined earthquake catalog from several agencies is used to estimate the magnitude of completeness ( ), a-value, and b-value based on the mainshock earthquake only. This analysis utilize Ground Motion Prediction Equations (GMPEs) randomly sampled to estimate the potential intensities. These findings reveal significant regional variations in seismicity, with the southern Sumatra showing high seismicity rate and the northern part indicating potential stress accumulation. Particularly in Bengkulu Province, due to the relative high seismicity rate based on the seismicity statistical parameters of a-value and b-value. It also suggests the influence of multiple megathrusts and active faults. In contrast, Kalimantan shows lower hazard overall, though East Kalimantan records localized high intensities due to the Meratus and Mangkahilat faults. Although Kalimantan’s seismicity is low, historical events demonstrate that distant earthquakes can still cause substantial impacts. The model has been validated by using six historical events and it is in good agreement more than 75% of correlation. The results offer valuable input for seismic risk analysis on the potential building loss estimation through Event Loss Table (ELT).

Abstract Boeotia, located in Central Greece, experiences frequent seismic activity, mainly due to its proximity to the Gulf of Corinth. Significant earthquakes have occurred in the broader study area, such as the ones of Atalanti (M w = 6.8, 6.9) in 1894, as well as the Alkyonides sequence in 1981 that included three M w > 6.0 events. In late 2020, a M w = 4.6 mainshock took place near Thiva, a populated town in Boeotia, followed by the 2021–2022 seismic sequence with three M w > 4.0 earthquakes. The objective of this study is to perform a Probabilistic Seismic Hazard Assessment (PSHA) for Boeotia through the computation of the Peak Ground Acceleration (PGA) and Peak Ground Velocity (PGV) using two truncation levels (ε = 0 and 3). Moreover, Uniform Hazard Spectra (UHS) are constructed in terms of Spectral acceleration (S a ) for Thiva and Livadia, the capital of Boeotia. To achieve this, three seismotectonic models in the form of area sources are employed in the computational framework. Ground Motion Prediction Equations (GMPEs), using data of the area of Greece, are utilized to estimate PGA and PGV. For each area source, the percentages of normal and non-normal (reverse or strike-slip) fault plane solutions are computed in order to generate minor branches for each GMPE that takes into account the focal mechanism type. This approach introduces variability and reduces uncertainties in PSHA. Additionally, a sensitivity analysis was performed by keeping constant one logic tree, first the GMPE, then the source-model tree, while varying the other, to assess the consistency of individual GMPEs and source models. The findings reveal that western and eastern Boeotia have higher seismic hazard, attributed to the seismotectonics of the study area. Additionally, the seismic hazard level in Thiva is higher compared to Livadia.

ABSTRACT This research investigates the seismic behavior of single-story reinforced concrete (RC) moment-resisting frame buildings with strength irregularity in plan due to variability in concrete compressive strength. The study aims to evaluate the influence of this kind of irregularity on the seismic performance of code-compliant RC structures. Field data on concrete strengths from RC buildings constructed in Tabriz, Iran, is utilized to define statistical parameters and create potential strength irregularity scenarios. The effects of strength deterioration and stiffness degradation are explicitly studied. Incremental dynamic analysis is employed to capture the non-linear response accurately. Fragility curves are developed to assess the probability of exceeding different damage states. This study highlights the critical role of incorporating cyclic deterioration for accurate seismic performance predictions, especially at higher damage levels. Failing to account for cyclic deterioration can lead to overestimations of the spectral acceleration (up to 36%) at collapse. While strength irregularity has minimal influence on behavior at moderate drift levels, its impact becomes significant in the larger non-linear deformation range, with a potential reduction in the seismic collapse capacity up to 14% compared to the reference regular structure. The findings suggest that a higher number of low-strength load-bearing members exacerbate reductions in collapse capacity. Additionally, higher standard deviations in concrete compressive strength amplify torsion in structures with strength irregularities, observed at both design-basis earthquake (DBE) and maximum considered earthquake (MCE) levels.

Summary Earthquake early warning systems are designed to provide critical seconds of warning before strong ground shaking, facilitating emergency mitigation efforts. Existing methods, such as neural networks and ground motion prediction equation-based approaches, rely on manually defined parameters and physics-based computations, which introduce human bias and hinder the efficiency of real-time applications. Furthermore, current studies primarily focus on scalar metrics such as peak ground acceleration and peak ground velocity to evaluate earthquake impacts. These metrics are limited to measuring ground shaking intensity and fail to capture the spectral characteristics of ground motion. Therefore, a ground-motion and structural-oriented deep learning-based model is proposed to predict uniform hazard spectral acceleration values across 111 periods ranging from 0.01 to 20 seconds. The framework is initially trained and evaluated on 17,500 ground-motion records from the crustal Next Generation Attenuation West 2 project. Spectral acceleration values are predicted by two subsets: deep learning-based uniform hazard spectral acceleration models 1 and 2. These models effectively utilize feature information from the initial seconds of seismic waveforms, eliminating the need for empirically defined parameters. Two deep learning-based models are developed for two datasets representing two distinct broad geographical regions. Both models utilize a similar deep-learning architecture but vary in input settings and hyperparameters to account for regional seismic characteristics. To assess the model's goodness-of-fit between observed and predicted values, as well as its generalization ability, we rigorously compare the two models with the latest data from the U.S. Geological Survey Earthquake Hazard Toolbox and the Japanese Strong-Motion Earthquake Network, respectively. An explainable artificial intelligence technique has been applied to better understand the framework and analyze how individual input features influence the outputs of the trained models. Integrating cutting-edge deep learning technologies into ground motion and engineering seismology reveals the significant potential of the model in enhancing real-time early warning systems. This integration also provides valuable support to various end-users involved in seismic monitoring, facilitating well-informed decisions in both real-time and near-real-time scenarios.

To accurately assess the seismic damage of high-rise structures under long-period ground motions (LPGMs), a 36-story SRC frame-RC core tube high-rise structure was designed. Twelve groups of LPGMs and twelve groups of ordinary ground motions (OGMs) were selected and bidirectionally input into the structure. The spectral acceleration S90c considering the effect of higher-order modes was adopted as the intensity measure (IM) of ground motions, and the maximum inter-story drift angle θmax under bidirectional ground motions was taken as the engineering demand parameter (EDP). Structural Incremental Dynamic Analysis (IDA) was conducted, the structural vulnerability was investigated, and seismic vulnerability curves, damage state probability curves, vulnerability index curves, as well as the probabilities of exceeding performance levels and vulnerability index of the structure during 8-degree frequent, design, and rare earthquakes were obtained, respectively. The results indicate that structural damage is significantly aggravated under LPGMs, and the exceeding probabilities for all performance levels are greater than those under OGMs, failing to meet the seismic fortification target specified in the code. When encountering an 8-degree frequent earthquake, the structure is in a moderate or severe damage state under LPGMs and is basically intact or in a slight damage state under OGMs. When encountering an 8-degree design earthquake, the structure is in a severe damage or near-collapse state under LPGMs and is in a moderate damage state under OGMs. When encountering an 8-degree rare earthquake, the structure is in a near-collapse state under LPGMs and in a severe damage state under OGMs.

ABSTRACT This study aims to develop correlation models for ground-motion intensity measures based on the Turkish strong-motion data. The intensity measures under examination are horizontal spectral accelerations (SAs) with periods spanning from 0.01 to 10 s, in addition to peak ground acceleration, peak ground velocity, and significant duration. The investigation focuses on the dependence of the developed correlation coefficient models on the definitions of intensity measures. The examination of the Turkish strong-motion database revealed that the correlations in SA are mainly unaffected by the method chosen to define SA in multicomponent ground-motion records. Furthermore, an evaluation is conducted regarding the potential ergodicity dependence within the correlations of ground-motion intensity measures. Specifically, the total residuals derived from a recently developed local partially nonergodic ground-motion prediction model were categorized into between-event, event-and-site-corrected, and between-site residuals. The analysis revealed that the correlations among the between-event residuals exhibit a dependence on region and magnitude. Similarly, it was found that the correlations of event-and site-corrected residuals are influenced by region and source-to-site distance. By utilizing the site-corrected, region-specific correlations in conjunction with computed site-specific ground motions, partially nonergodic conditional mean spectra were developed for two well-monitored sites within the Marmara region.

Seismic risk assessment is a critical process for quantifying the expected structural damage and economic losses resulting from seismic events. Such studies are essential for developing effective pre-earthquake preparedness strategies and ensuring the efficient implementation of post-earthquake response plans. In this study, the seismic vulnerability and risk assessment of a typical low-rise reinforced concrete school building with shear wall systems, located at various locations in the province of Adıyaman, was carried out. First, a three-dimensional finite element model of the school building was developed. Subsequently, a nonlinear static (pushover) analysis was performed to obtain the capacity curve of the building. Based on three different empirical models, hybrid-based fragility curves were derived as a function of spectral acceleration. Furthermore, vulnerability curves were constructed using twelve different consequence models. A scenario-based seismic hazard analysis was conducted for the Narince segment, one of the active fault lines in the South-eastern Anatolia Thrust. As a result of the risk assessment, considering the proposed vulnerability models, the expected loss ratio values were computed at different locations. When the results are evaluated as a whole, it is observed that the loss values of the building vary significantly depending on the location. While certain locations are expected to experience irreparable damage, others are likely to sustain only minor, repairable damage. This study serves as a significant example for assessing the seismic risk of typical school building types. The proposed methodology and findings, if extended to other similar typologies, can facilitate the development of a comprehensive and regional-scale seismic risk assessment framework for school buildings.

Tall-pier girder bridges with fluid viscous dampers (FVDs) are widely used in earthquake-prone mountainous areas. However, the influence of higher-order modes and near-field earthquakes on tall piers has rarely been studied. Based on an asymmetric tall-pier girder bridge, a finite element model is established, and the parameters of FVDs are optimized using SAP2000. The higher-order mode effects on tall piers are explored by proportionally reducing the pier heights. The pulse effects of near-field earthquakes on FVD mitigation and higher-order modes are analyzed. The optimal FVDs can coordinate the force distribution among tall piers, effectively reducing displacement responses and internal forces. Due to higher-order modes, the internal force envelopes of tall piers exhibit concave-convex distributions. As pier heights decrease, the internal force envelopes gradually become linear, implying reduced higher-order mode effects. Long-period pulse-like motions produce the maximum seismic responses because the slender tall-pier bridge is sensitive to high spectral accelerations in medium-to-long periods. The higher-order modes are more easily excited by near-field motions with large spectral values in the high-frequency range. Overall, FVDs can simultaneously reduce the seismic responses of tall piers and diminish the influence of higher-order modes.

An expanded Pacific Earthquake Engineering Research (PEER) Center Next Generation Attenuation Phase 2 (NGA-West2) ground motion database, compiled using shallow crustal earthquakes in active crustal regions (ACRs), was used to develop the closed-form GS24b backbone ground motion model (GMM) for the RotD50 horizontal components of peak ground acceleration (PGA), peak ground velocity (PGV), and 5% damped elastic pseudo-absolute response spectral accelerations (SA). The GS24b model is applicable to earthquakes with moment magnitudes of 4.0 ≤ M ≤ 8.5, at rupture distances of 0 ≤ Rrup ≤ 400 km, with time-averaged S-wave velocity in the upper 30 m of the profile at 150 ≤ VS30 ≤ 1500 m/s, and for periods of 0.01 ≤ T ≤ 10 s. The new backbone model includes VS30 site correction developed based on multiple representative S-wave velocity profiles. For crustal wave attenuation, we used the apparent anelastic attenuation of SA—QSA (f, M). In contrast to the GK17, the GS24b backbone is a generic ACR model designed specifically to be adjusted to any ACRs. The GS24bc is an example of a partially non-ergodic model created by adjusting the backbone GS24b model for magnitude M, S-wave velocity VS30, and fault rupture distance residuals.