Engineers frequently use simplified seismic slope displacement procedures to evaluate the performance of earth structures and natural slopes. Current practices typically adopt Newmark‐type and semi‐empirical seismic displacement prediction models (SDPMs) to estimate the seismic slope displacements ( D ) based on ground motion intensity measures at a given hazard level. In a probabilistic framework, the full range of potential IM values and possible earthquake scenarios are utilized to estimate earthquake‐induced slope displacements for target hazard levels. This study outlines a methodology for deriving probabilistic D values for slopes in Greater Vancouver and the Lower Mainland, incorporating local seismic hazard sources and updates from Canada's 6th‐generation seismic hazard model (CSHM6). A novel computational tool, EqDisp, is developed and used, integrating probabilistic seismic hazard analysis with the calculation of probabilistic permanent displacements ( D ). The study streamlines the process of calculating probabilistic D values for specific locations and site classes, considering various slope characteristics and uncertainties inherent in SDPMs. Using EqDisp, displacement calculations are performed for 264 different sites across Lower Mainland and simplified regional equation is also introduced for direct estimation of the probabilistic displacement values. The significance of probabilistic approaches in generating displacement hazard curves and seismic landslide hazard maps is underscored, enhancing regional assessments of slope stability hazards.

It is important to appropriately capture the uncertainty associated with ground motion predictions. This study evaluates the component‐to‐component variability of 5%—damped response spectral ordinates using 4331 horizontal ground motions recorded in strike‐slip earthquakes. This variability is quantified relative to three scalar measures of central tendency of ground motion intensity (namely, geometric mean, RotD50, and MaxRotD50). The influence of earthquake magnitude, source‐to‐site distance, and the spatial distribution of recording stations relative to the epicenter on the component‐to‐component variability around each scalar measure is also studied. When combined with within‐event and between‐event variabilities, component‐to‐component variability allows the quantification of the total uncertainty of the random horizontal component, which better represents demand on a structure. The definition used in the past to quantify component‐to‐component variability was found to be sensitive to the orientation of horizontal sensors, leading to under‐ or over‐estimation depending on the orientation of the as‐recorded components relative to the orientation of maximum spectral response at a given station. Equations for component‐to‐component variability that are independent of the orientation of the sensors are proposed to obtain the total variability of the randomly oriented horizontal component from either the geometric mean or the RotD50. Compared to the commonly used equation, the proposed equations yield larger variabilities in the medium‐ to long‐period ranges and have smaller event‐to‐event variability.

The seismic performance of residential buildings is a pressing concern in high‐seismicity regions. In Colombia, Thin Lightly Reinforced Concrete Wall (TLRCW) buildings have become a common solution to address the housing deficits. This study evaluates the impact of structural configuration on the seismic performance of 52 low‐ and mid‐rise constructed TLRCW buildings, examining key parameters such as stiffness index, wall index, aspect ratio, longitudinal reinforcement ratios distributed in the wall web and concentrated at the wall edge, wall thickness, and confinement. Numerical simulations in OpenSeesPy were used to assess the performance of archetypes under a set of hazard‐consistent ground motion records, generating fragility curves for each archetype. For each archetype, the seismic performance is evaluated in terms of the probability of collapse for the maximum considered earthquake (MCE). The results show that certain structural configurations can increase the probability of collapse for low‐rise and mid‐rise buildings. Specifically, very thin walls (80–100 mm) with aspect ratios greater than 6, light longitudinal reinforcement ratio distributed in the wall web using a single layer of welded wire mesh (less than 0.20%), and light vertical reinforcement concentrated at the wall edge (less than 0.50%) increase the probability in low‐rise buildings. In contrast, high steel ratios of vertical reinforcement concentrated at the wall edges (more than 2%) combined with low web reinforcement (0.20% or less) are key attributes in mid‐rise buildings that lead to unacceptable seismic performance.

This study proposes a framework to assess the seismic risk by integrating city‐scale numerical simulations with sensor data prediction. The study begins with advanced numerical simulations using two primary methods: the integrated earthquake simulator (IES) and the stochastic Green's function method. The stochastic Green function method is used to generate the motion from the fault model to the outcropped engineering bedrock. The IES is used to simulate the ground motion on the surface, considering different scenarios, and the sumulation results are seperated into training and validation datasets. In this study, all scenarios are generated from a single fault model, and variability is introduced by changing the rupture initiation point, representing a limited dataset but practically relevant setting. Proper orthogonal decomposition (POD) is then applied to the training datasets to extract spatial modes, and those modes are used for identifying the most suitable distributions for sensors. In this study, the sensors are strategically placed to maximize data collection efficiency while minimizing the overall number of sensors used. Optimizing sensor distribution is crucial owing to the challenges associated with deploying numerous sensors in actual situations. The distribution of sensors obtained by this method is validated using a test set generated from IES simualation. The case study focusing on Sendai city (Japan), for scenarios associated with the Nagamachi–Rifu fault, finds an optimal solution for the distribution of sensors and allows for an overall prediction of the expected accuracy. The results show that seismic risk prediction and assessment on a city scale can be achieved by this method while maintaining accuracy and the number of sensors. The present work therefore demonstrates the feasibility of the proposed rapid prediction framework under limited dataset conditions for a specific, high impact seismic source, while the extension to multiple faults and magnitudes is left for future work.

This paper introduces a procedure to estimate moment magnitude ( M W ) in earthquake‐induced soil liquefaction probabilistic risk assessment. The method is based on using the historical seismicity modelled with a truncated Gutenberg‐Richter (G‐R) relation, jointly with an empirical model linking the M W to the maximum source‐to‐site distance at which soil liquefaction was observed in past earthquakes. The proposed method predicts M W as a function of the recurrence intervals, which are assimilated to return periods (RPs), and it may be used in probabilistic or semi‐probabilistic seismic design as prescribed by modern building codes worldwide. Moreover, it is independent from any intensity measure (IM) of ground motion, and this is a desirable feature since the selection of the optimal IM for liquefaction risk assessment does not meet a general agreement among specialists. The novel procedure to estimate M W has been applied at five Italian sites with different levels of seismicity. The results obtained with the proposed method to define M W have been compared with other approaches from the current Italian engineering practice. Then, the application was extended to the whole Italian territory to compute maps of the expected M W for liquefaction risk assessment for the RPs of 475, 975, and 2,475 years. Such maps represent useful tools for practitioners in Italy when performing liquefaction‐triggering analyses. Although the proposed method to compute the expected M W in soil liquefaction triggering studies has been developed with a specific focus on the Italian territory, the procedure may be applied elsewhere.

Empirical correlations between ground‐motion intensity measures (IMs) are essential for engineering applications, including conditional ground‐motion selection and vector‐valued probabilistic seismic hazard analysis (VPSHA). Although previous research has thoroughly investigated interperiod correlations of response spectral ordinates and relationships between nonspectral IMs and pseudo‐spectral acceleration (PSA), the connections between engineering IMs and Fourier spectral ordinates remain inadequately characterized from an engineering seismology standpoint. This investigation establishes comprehensive empirical correlations between PSAs, Arias intensity (AI), and cumulative absolute velocity (CAV) with Fourier spectral amplitudes utilizing the NGA‐West2 database. The linear correlations are presented using total residuals of the IMs with respect to established ground‐motion models from Abrahamson et al. (2014), Bora et al. (2019), Afshari and Stewart (2016), and Campbell and Bozorgnia (2019). A partial correlation methodology isolates frequency‐dependent relationships. The results confirm earlier observations by Bora et al. (2016) and identify specific frequency ranges controlling individual IMs. Peak ground acceleration (PGA) and peak ground velocity (PGV) are predominantly influenced by higher‐frequency content, with distinct frequency ranges characterizing each parameter. Conversely, AI and CAV exhibit sensitivity across a broader, overlapping frequency band (1–3 Hz). Scenario‐dependent analysis through magnitude binning reveals systematic correlation variations, which essentially explain the regional differences in IM‐Fourier amplitude relationships. These findings illuminate the physical foundation of engineering IMs and hold substantial implications for host‐to‐target adjustments of GMMs, next‐generation GMM development, physics‐based ground‐motion simulation, and understanding of IM correlations in existing literature. The work successfully connects seismological and engineering ground‐motion characterizations.

This opinion paper articulates factors I believe are instrumental to the success of interdisciplinary research centers, drawing on a decade of experience with QuakeCoRE: New Zealand's Centre for Earthquake Resilience as a co‐lead in its establishment and subsequently Deputy Director and Director. The discussion follows a chronological progression from center proposal development, establishing an enduring culture, broadening disciplinary scope to become a more genuine interdisciplinary center, and ever‐present challenges. To foster enduring success, centers should craft a resonating vision and mission, be a natural monopoly with regard to topics and geography, establish deep collaboration through active leadership and transparency, and strategically broaden scope while preserving disciplinary excellence and funding flexibility. Leaders must balance collaboration with healthy competition, provide upward mobility for emerging researchers, and mitigate vulnerabilities like funding insecurity through constant evolution. Collectively, these approaches enable centers to achieve transformative societal impacts that far exceed individual efforts. Rooted in QuakeCoREs context as a natural earthquake laboratory, the insights are intended to offer practical guidance for interdisciplinary centers worldwide.

The complex terrain and expanding transport networks in Southwest China have heightened the demand for valley‐spanning bridges in active fault zones, posing seismic design challenges. This study investigates the seismic response of a near‐fault valley‐spanning bridge using deterministic ground motion simulations of reverse fault rupture. The goal is to evaluate whether the common seismic design assumption that horizontal and vertical ground motions can be represented by SV and P waves is consistent with actual conditions represented by the fully 3D waveform solution and to assess the validity of assuming Rayleigh waves as input waves. Results indicate significant differences in the internal forces. Axial force distribution varied: The fully 3D waveform solution and Rayleigh wave caused peak axial forces near the arch crown and arch foot, while the P–SV wave caused peaks mainly near the arch foot. Analysis of transfer functions for axial forces at the arch foot revealed that these differences are attributed to variations in the dominant mode shapes excited by different inputs: The fully 3D waveform solution predominantly excited longitudinal and transverse rotational modes, Rayleigh wave primarily excited transverse rotational modes, and P–SV wave mainly excited longitudinal and vertical modes. These findings underscore that treating seismic motions as single Rayleigh or P–SV waves may cause overestimation or underestimation of the structural response. Therefore, the fully 3D waveform solution is recommended for reliable near‐fault seismic response assessment and design of valley‐spanning bridges in complex terrain.

In the present study, an accurate ground motion model (GMM) for the Korean Peninsula is developed by considering local seismological characteristics, including source, site, and three-dimensional (3D) path effects. An existing GMM for rock site conditions is adopted as the base model. To enhance the accuracy of this model and incorporate local seismological features, residuals between the observed and predicted intensity measures (IMs), such as peak ground acceleration ( PGA ) and pseudo-spectral acceleration ( PSA ), are computed. These residuals are further disaggregated into components associated with specific seismological characteristics: δ Source , δ Site , and δ Path . Regression analyses are conducted to develop empirical equations for δ Source , δ Site , and δ Path based on the calculated residuals. These correction terms are subsequently integrated into the existing GMM to establish the improved model. A practical example is provided to illustrate the application of the proposed GMM. The accuracy and reliability of the improved model are validated by comparing the observed IM values with those predicted by the proposed GMM.