Displacement Estimation Research Articles

Estimation with Ultimate Quantum Precision of the Transverse Displacement between Two Photons via Two-Photon Interference Sampling Measurements.

We present a quantum sensing scheme achieving the ultimate quantum sensitivity in the estimation of the transverse displacement between two photons interfering at a balanced beam splitter, based on transverse-momentum sampling measurements at the output. This scheme can possibly lead to enhanced high-precision nanoscopic techniques, such as superresolved single-molecule localization microscopy with quantum dots, by circumventing the requirements in standard direct imaging of camera resolution at the diffraction limit, and of highly magnifying objectives. Interestingly, we show that our interferometric technique achieves the ultimate spatial precision in nature irrespectively of the overlap of the two displaced photonic wave packets, while its precision is only reduced of a constant factor for photons differing in any nonspatial degrees of freedom. This opens a new research paradigm based on the interface between spatially resolved quantum interference and quantum-enhanced spatial sensitivity.

Spectral speckle displacement in defocused and tilted imaging systems

Speckle patterns offer valuable insights into the surface characteristics or the characteristics of the light generating the speckle. One possible way to extract this information is via spectral speckle correlation (SSC). The cross-correlation between two speckle fields, generated at different wavelengths, can be used for example to determine the roughness of the illuminated surface. Taking defocused measurements of the surface or measuring on a tilted surface leads to a displacement between the speckle, which in turn affects the cross-correlation and leads to errors in the calculated roughness. In this work we present a model to determine the lateral speckle displacement for a change in wavelength in the case of subjective speckle and defocused, tilted objects. This model is therefore applicable to a wide range of applications and allows to estimate and correct for this speckle displacement. Experimental results show sub-pixel accuracy for object tilts up to ±7° and defocus distances up to ±25 mm.

Strength reduction factor for inelastic-displacement estimation and design of self-centering viscous-hysteretic systems

This study focuses on developing strength reduction factor Rμ spectrum, a maximum inelastic displacement (MID) estimation method and a displacement-based design method for single-degree-of-freedom (SDOF) bilinear structures with self-centering viscous-hysteretic devices (SC-VHDs). The effects of design parameters on Rμ spectrum are statistically investigated under near-fault pulse-like ground motions (NFPG) and far-field ground motions (FFG), and its prediction formula is fitted according to the statistical data. Following proposed Rμ spectrum, a MID estimation method and a performance-based design method are proposed, respectively. Next, three SC-VHD frames are designed step-by-step to verify the proposed design method, and then their MIDs are calculated step-by-step to validate the MID estimation method. Finally, a risk assessment is conducted on a SC-VHD frame and a conventional frame to compare their collapse performance and the performance exceeding 0.2% residual drift in the probability framework. The results indicate that Rμ spectrum is primarily affected by the ductility ratio, period and viscous damping ratio, while the parameters of ring springs have little effect on it. Rμ spectrum under the NFPE is significantly lower than that under the FFE except for periods from 0.2 s to 0.6 s. The Rμ spectrum formula and the MID estimation method both have errors that are typically less than 10%. The designed SC-VHD frame can achieve the target performance, and its probability of collapse and exceeding 0.2% residual drift in a period of 50-year is significantly smaller than that of the conventional frame.

Estimation of pop-up satellite archival tag initial surface position: applications for eastern Bering Sea crab research

BackgroundClimate change is reshaping Bering Sea crab distributions and recent population declines have elevated the urgency in understanding spatial dynamics in relation to management boundaries. While pop-up satellite archival tags (PSATs) can provide fishery-independent movement information, a high level of spatial resolution is needed to evaluate small-scale (i.e., 10 s of km) movements of crabs. Because PSATs drift at the surface prior to acquisition of a satellite-estimated location (via Argos), the accuracy of pop-up location (i.e., animal terminal position) estimates depends on the ability to accurately estimate drift error. We deployed PSATs (n = 36) on fixed-position moorings in Bristol Bay and Marmot Bay, Alaska to validate a new method to estimate tag pop-up location and an associated error ellipse that uses in situ drift data from surfaced tags to estimate drift error. Estimated pop-up location was compared to the location of tag surfacing (i.e., the tag’s fixed position) and to an alternative estimate (i.e., an early satellite-estimated location). Additional tags were deployed on mature male red king crab (Paralithodes camtschaticus) in Bristol Bay during 2020 (n = 84) and 2021 (n = 90) to compare estimates of tag drift error and crab displacement derived using the method.ResultsFor red king crab tags with pop-up location estimates in 2020 (n = 79) and 2021 (n = 46), mean drift error was 9% and 44% of mean crab displacement, respectively. For fixed-position PSATs with pop-up location estimates (n = 27), mean distance between the estimated pop-up location and the tag’s fixed position was 2.0 km, representing a mean improvement in accuracy of 51% over alternative estimates. Corresponding error ellipse estimates either encompassed the tag’s fixed position (n = 11) or their boundaries were a mean distance of 1.3 km (n = 16) from the fixed position.ConclusionsOur method improves pop-up location estimates for PSAT-tagged animals and is particularly well suited for crabs and other slow-moving benthic marine species. The improved estimates enhance PSAT utility for addressing management and ecological questions that require a fine spatial resolution, such as movement near management boundaries or defining essential habitat for important life history events.

Late quaternary faulted landforms and recent activity of the eastern segment of the Nanbei River–Boli Fault

The Nanbei River–Boli Fault is one of the largest NW-trending faults in Northeast China. However, limited data on the style and timing of recent earthquake activity on this fault greatly restrict the accuracy of seismic risk assessment. Here, we provide evidence of late Pleistocene to Holocene activity of the eastern segment of this fault for the first time. Specifically, the eastern segment of the Nanbei River–Boli Fault has experienced multiple earthquakes since the late Quaternary. Trench excavations indicate a recent strong earthquake at 7.28–2.11 ka, with approximately 1 m of co-seismic vertical displacement and preliminary estimates of the minimum vertical slip rates of 0.07–0.12 mm/a. According to a comprehensive analysis, we estimate that the eastern segment of the Nanbei River–Boli Fault can generate strong earthquakes of magnitude 7.0. This research provides important information for seismic risk assessments and seismic zoning, which can help seismic hazard assessment and mitigate their effects in Northeast China.

Displacement estimation for a high-rise building during Super Typhoon Mangkhut based on field measurements and machine learning

Knowledge of displacement responses of high-rise buildings under harsh wind excitations is essential for their wind-resistant structural design. This paper develops a machine learning model named long short-term memory (LSTM) to estimate the displacements of a 420-m-high building during Super Typhoon Mangkhut based on available field measurements. The developed model is trained and validated using the field measurements on the building during typhoon events, and the performance of the model is assessed against several evaluation criteria. Then, the trained LSTM model is employed to estimate the displacements of the skyscraper during Mangkhut. The accuracy of the estimated displacements is validated in time and frequency domains. Moreover, the background and resonant components of the estimated displacements during the extreme windstorm are analyzed. This paper aims to provide valuable reference for the wind-resistant design of high-rise buildings in tropical cyclone-prone regions.

New Displacement Method for Free Embedded Cantilever Walls in Sand

In the current literature, there is no practical formula to calculate the horizontal displacement of cantilever walls. To fill this gap, in the present study, eight formulae for the estimation of wall displacement were developed based on 431 FE wall model configurations. Each formula considers factors such as the wall height, embedment depth, surcharge load, unit weight, internal friction angle, elastic modulus of the surrounding soil, and flexural rigidity of the wall. The FE model, which was used in the development of the formula, was also validated against a physical laboratory study. In addition, the outputs obtained from the formulae were compared with the results of two laboratory studies and a real site study. Finally, a parametric study was performed to estimate the influence of formula input parameters on wall displacement.

Analysis of left ventricle regional myocardial motion for cardiac radioablation: Left ventricular motion analysis.

Left ventricle (LV) regional myocardial displacement due to cardiac motion was assessed using cardiovascular magnetic resonance (CMR) cine images to establish region-specific margins for cardiac radioablation treatments. CMR breath-hold cine images and LV myocardial tissue contour points were analyzed for 200 subjects, including controls (n=50) and heart failure (HF) patients with preserved ejection fraction (HFpEF, n=50), mid-range ejection fraction (HFmrEF, n=50), and reduced ejection fraction (HFrEF, n=50). Contour points were divided into segments according to the 17-segment model. For each patient, contour point displacements were determined for the long-axis (all 17 segments) and short-axis (segments 1-12) directions. Mean overall, tangential (longitudinal or circumferential), and normal (radial) displacements were calculated for the 17 segments and for each segment level. The greatest overall motion was observed in the control group-long axis: 4.5±1.2mm (segment 13 [apical anterior] epicardium) to 13.8±3.0mm (segment 6 [basal anterolateral] endocardium), short axis: 4.3±0.8mm (segment 9 [mid inferoseptal] epicardium) to 11.5±2.3mm (segment 1 [basal anterior] endocardium). HF patients exhibited lesser motion, with the smallest overall displacements observed in the HFrEF group-long axis: 4.3±1.7mm (segment 13 [apical anterior] epicardium) to 10.6±3.4mm (segment 6 [basal anterolateral] endocardium), short axis: 3.9±1.3mm (segment 8 [mid anteroseptal] epicardium) to 7.4±2.8mm (segment 1 [basal anterior] endocardium). This analysis provides an estimate of epicardial and endocardial displacement for the 17 segments of the LV for patients with normal and impaired LV function. This reference data can be used to establish treatment planning margin guidelines for cardiac radioablation. Smaller margins may be used for patients with higher degree of impaired heart function, depending on the LV segment.

GPS displacement dataset for the study of elastic surface mass variations

Abstract. Quantification of uncertainty in surface mass change signals derived from Global Positioning System (GPS) measurements poses challenges, especially when dealing with large datasets with continental or global coverage. We present a new GPS station displacement dataset that reflects surface mass load signals and their uncertainties. We assess the structure and quantify the uncertainty of vertical land displacement derived from 3045 GPS stations distributed across the continental US. Monthly means of daily positions are available for 15 years. We list the required corrections to isolate surface mass signals in GPS estimates and screen the data using GRACE(-FO) as external validation. Evaluation of GPS time series is a critical step, which identifies (a) corrections that were missed, (b) sites that contain non-elastic signals (e.g., close to aquifers), and (c) sites affected by background modeling errors (e.g., errors in the glacial isostatic model). Finally, we quantify uncertainty of GPS vertical displacement estimates through stochastic modeling and quantification of spatially correlated errors. Our aim is to assign weights to GPS estimates of vertical displacements, which will be used in a joint solution with GRACE(-FO). We prescribe white, colored, and spatially correlated noise. To quantify spatially correlated noise, we build on the common mode imaging approach by adding a geophysical constraint (i.e., surface hydrology) to derive an error estimate for the surface mass signal. We study the uncertainty of the GPS displacement time series and find an average noise level between 2 and 3 mm when white noise, flicker noise, and the root mean square (rms) of residuals about a seasonality and trend fit are used to describe uncertainty. Prescribing random walk noise increases the error level such that half of the stations have noise > 4 mm, which is systematic with the noise level derived through modeling of spatially correlated noise. The new dataset is available at https://doi.org/10.5281/zenodo.8184285 (Peidou et al., 2023) and is suitable for use in a future joint solution with GRACE(-FO)-like observations.

Continuous monitoring of endotracheal tube position with near infrared light.

Endotracheal intubation is a common approach for airway management in critically ill patients. However, the position of the endotracheal tube (ETT) may be altered during the procedure due to head movements. Accidental displacement or dislodge of the ETT may reduce the airflow, leading to moderate to severe complications, and in some cases even fatality. Therefore, timely detection of changes in ETT position in the trachea is critical to ensure immediate and intermediate interventions to maintain the ETT in the proper position. Currently, there are no widely utilized tools for real-time monitoring of ETT positions. The goal of this study is to develop a cost-effective and easy-to-use near-infrared (NIR) device, named Opt-ETT, capable of continuously monitoring the ETT position in the trachea of a patient. A side-firing fiber is attached to the side of the ETT to illuminate the trachea tissue with NIR light, and a detector board containing five phototransistors is affixed to the chest skin to measure the intensity of diffusely transmitted light. Displacement of the ETT is estimated using second-order polynomial fitting to the ratios of the phototransistor readings. Monte Carlo simulations, ex vivo experiment on porcine tissue, and in vivo experiments using a swine model have been conducted to assess the feasibility of the device. The design of the Opt-ETT device has been verified by the Monte Carlo simulations and ex vivo experiment. The estimation of displacement from in vivo experiments using the Opt-ETT exhibited a high degree of agreement with that measured by a reference sensor, with a discrepancy between to within a displacement range from to . The Opt-ETT device provides a potentially cost-effective solution for real-time and continuous monitoring of ETT position in patient during an intubation procedure.

Impacts of physical activity, sedentary behaviour, and sleep on depression symptoms in Canadian older adults 65 years of age and above: a compositional data analysis of the Canadian Longitudinal Study on Aging

BackgroundDaily time spent in moderate-to-vigorous physical activity (MVPA), light-intensity physical activity (LIPA), sedentary behaviour (SB) and sleep (collectively known as ‘movement behaviours’) are compositional, co-dependent variables. However, most studies examining effects of these behaviours on depression outcomes in older adults do not account for this. Study objectives were to use compositional data analysis methods to (1) examine the relationship between movement behaviour composition (daily time spent in MVPA, LIPA, SB, sleep) and depression symptoms, (2) estimate the extent to which changing time spent in any given movement behaviour within the movement behaviour composition was associated with changes in depression symptoms.Methods5643 older Canadian adults ≥ 65 years of age from the Canadian Longitudinal Study on Aging were studied using a quasi-longitudinal study design. Exposure was baseline daily movement behaviours; time spent in MVPA, LIPA and SB were derived from self-reported Physical Activity Scale for the Elderly responses. Night-time sleep was self-reported separately. Outcome was depression symptoms at follow-up obtained using the ten item Center for Epidemiologic Studies Depression Scale. Compositional data analysis was used to investigate associations between movement behaviours and depression symptoms.ResultsMovement behaviour composition was significantly associated with depression symptoms. Time spent in MVPA [exp(B) = 0.97 (95% CI: 0.94, 0.99)] and sleep [exp(B) = 0.91 (95% CI: 0.85, 0.97)] relative to the remaining movement behaviours were associated with lower depression symptoms scores. Relative time spent in SB was associated with higher depression symptoms scores [exp(B) = 1.09 (95% CI: 1.04, 1.15)]. Time displacement estimates revealed that the greatest change in depression symptoms scores occurred when time spent in MVPA was decreased and replaced with LIPA, SB, sleep or combination of these behaviours (+ 0.22 to 0.26 points increase on depression symptoms scores for 30 min/day displacements).ConclusionsDaily movement behaviour composition was associated with depression symptoms in older Canadians. Replacing time in MVPA with equivalent time from any other behaviour was associated with increased depression symptoms. Preserving time spent in MVPA may play a key role in mitigating and improving mental health in this demographic.

Research on numerical solution of statically indeterminate problem of steering tool combined bearing

The steering tool combined bearing is a mechanical component that can support forces, guide movement, and bear vibrations at the same time. In the process of analyzing the contact and load distribution inside the combined bearing, there are several contact points, and the static equilibrium equation is statically indeterminate. Based on dynamics theory and Hertz elastic contact theory, the concept of a load integral and the displacement estimation formula for roller bearings are introduced, and a numerical solution to the statically indeterminate problem of the steering tool combined bearing is given. According to the statically indeterminate equation matrix of the combined bearing, the relationship between the load and displacement of the combined bearing is studied to improve the statically indeterminate equation matrix. Moreover, the simulation results of the maximum stress and displacement distribution results of the inner and outer ring eccentricity from 1 to 6mm are compared with the algorithm prediction results. The error between the numerical solution of the statically indeterminate problem of the steering tool combined bearing and the simulation result is small, improving the calculation efficiency. This provides theoretical support for the design and reasonable application of the steering tool combined bearing.

Development of a 2D spatial displacement estimation method for turbulence velocimetry of the gas puff imaging system on EAST

A gas puff imaging (GPI) diagnostic has been developed and operated on EAST since 2012, and the time-delay estimation (TDE) method is used to derive the propagation velocity of fluctuations from the two-dimensional GPI data. However, with the TDE method it is difficult to analyze the data with fast transient events, such as edge-localized mode (ELM). Consequently, a method called the spatial displacement estimation (SDE) algorithm is developed to estimate the turbulence velocity with high temporal resolution. Based on the SDE algorithm, we make some improvements, including an adaptive median filter and super-resolution technology. After the development of the algorithm, a straight-line movement and a curved-line movement are used to test the accuracy of the algorithm, and the calculated speed agrees well with preset speed. This SDE algorithm is applied to the EAST GPI data analysis, and the derived propagation velocity of turbulence is consistent with that from the TDE method, but with much higher temporal resolution.

T-SPP: Improving GNSS Single-Point Positioning Performance Using Transformer-Based Correction

GNSS (global navigation satellite systems) technology enables high-precision single-point positioning (SPP) in open environments. However, the accuracy of GNSS positioning is significantly compromised in complex urban canyons due to signal obstructions and non-line-of-sight propagation errors. To address this challenge, we propose a GNSS displacement estimation algorithm. This method learns nonlinear dependencies between GNSS raw measurements and corresponding position changes, capturing dynamic and layered features in GNSS measurement data for displacement estimation. We introduce a denoising auto-encoder (DAE) to preprocess raw GNSS observations, reducing the impact of noise. The model simultaneously outputs estimated displacement and model confidence. The fusion process dynamically combines positioning results from the SPP algorithm and the D-Tran model, adaptively blending them to achieve accurate and optimal positioning estimation. This approach optimizes the accuracy of estimated positioning results while maintaining confidence in the estimation. Experimental results show a 61% reduction in root mean square error (RMSE) and 100% availability in urban canyon environments compared to traditional single-point positioning techniques.

German and European Ground Motion Service: a Comparison

Since the end of 2022, two ground motion services that cover the complete area of Germany are available as web services: the German Ground Motion Service (Bodenbewegungsdienst Deutschland, BBD) provided by the Federal Institute for Geosciences and Natural Resources (BGR), and the first release of the European Ground Motion Service (EGMS) as part of the Copernicus Land Monitoring Service. Both services are based on InSAR displacement estimations generated from Sentinel‑1 data. It would seem relevant to compare the products of the two services against one another, assess the data coverage they provide, and investigate how well they perform compared to other geodetic techniques. For a study commissioned by the surveying authority of the state of Baden-Württemberg (Landesamt für Geoinformation und Landentwicklung Baden-Württemberg, LGL), BBD and EGMS data from different locations in Baden-Württemberg, Saarland, and North Rhine-Westphalia (NRW) were investigated and validated against levelling and GNSS data. We found that both services provide good data quality. BBD shows slightly better calibration precision than EGMS. The coverage provided by EGMS is better than that of BBD on motorways, federal roads, and train tracks of the Deutsche Bahn. As an example, where both services have difficulties in determining the correct displacements, as they cannot be described well by the displacement models used for processing, we present the test case of the cavern field at Epe (NRW). Finally, we discuss the implications of our findings for the use of the products of BBD and EGMS for monitoring tasks.

Variational mode decomposition-based multirate data-fusion framework for estimating structural dynamic displacement by integrating vision- and acceleration-based measurements

Dynamic displacement is a crucial parameter in structural health monitoring (SHM) for assessing the safety, dependability, and suitability of structures under various types of excitations. Computer vision-based methods for dynamic displacement estimation have attracted much interest owing to their cost-effectiveness and convenience. However, these methods are limited by their low sampling rates and high data sensitivity. To compensate for these limitations, methods for combining data obtained from other sensors have been proposed. In this study, an experimental data-fusion framework for displacement estimation based on variational mode decomposition (VMD) was developed to leverage the advantages of vision- and acceleration-based measurements. The measurements were decomposed into ensembles of modes and recomposed to reconstruct the displacement with a higher accuracy and over a wider frequency range. An optimal mode recomposition method was proposed to achieve optimal mode combinations. Furthermore, this study introduced an improved vision-based displacement measurement method and a VMD-based indirect acceleration measurement method. The proposed framework was validated through four-story RC structure tests, which demonstrated that the method could enhance the accuracy of displacement estimation and extend the feasible frequency range compared with single-source displacement measurements. The method provides a promising solution for more effective health monitoring of modern structures subjected to a wide variety of dynamic loads.

Displacement Estimation via 3D-Printed RFID Sensors for Structural Health Monitoring: Leveraging Machine Learning and Photoluminescence to Overcome Data Gaps.

Monitoring object displacement is critical for structural health monitoring (SHM). Radio frequency identification (RFID) sensors can be used for this purpose. Using more sensors enhances displacement estimation accuracy, especially when it is realized through the use of machine learning (ML) algorithms for predicting the direction of arrival of the associated signals. Our research shows that ML algorithms, in conjunction with adequate RFID passive sensor data, can precisely evaluate azimuth angles. However, increasing the number of sensors can lead to gaps in the data, which typical numerical methods such as interpolation and imputation may not fully resolve. To overcome this challenge, we propose enhancing the sensitivity of 3D-printed passive RFID sensor arrays using a novel photoluminescence-based RF signal enhancement technique. This can boost received RF signal levels by 2 dB to 8 dB, depending on the propagation mode (near-field or far-field). Hence, it effectively mitigates the issue of missing data without necessitating changes in transmit power levels or the number of sensors. This approach, which enables remote shaping of radiation patterns via light, can herald new prospects in the development of smart antennas for various applications apart from SHM, such as biomedicine and aerospace.

A new p-y model for soil-pile interaction analyses in cohesionless soils under monotonic loading

The most widely used analysis method for the laterally loaded pile problem is the Winkler spring approach. Although researchers have proposed nonlinear formulations for the p-y curves, the contribution of the soil nonlinearity has not been thoroughly studied. The main drawback of the current approach is the use of a single stiffness in the p-y formulation. This study investigates the laterally loaded pile problem by employing the pressure-dependent hardening soil model with small-strain stiffness (HS-Small Model), where the degree of soil nonlinearity is better integrated. The parametric analyses are performed on the verified model for various pile and soil properties. A new p-y model is proposed for pile behaviour under monotonic loading based on the numerical analysis results. The model includes the initial stiffness, ultimate soil resistance, and degree of nonlinearity parameters. The validity of the proposed model is demonstrated by simulating a centrifuge and two field tests from the literature. The proposed model accurately accounts for soil nonlinearity and significantly improves the estimation of lateral displacements.

UDRSNet: An unsupervised deformable registration module based on image structure similarity.

Image registration is a challenging problem in many clinical tasks, but deep learning has made significant progress in this area over the past few years. Real-time and robust registration has been made possible by supervised transformation estimation. However, the quality of registrations using this framework depends on the quality of ground truth labels such as displacement field. To propose a simple and reliable method for registering medical images based on image structure similarity in a completely unsupervised manner. We proposed a deep cascade unsupervised deformable registration approach to align images without reliable clinical data labels. Our basic network was composed of a displacement estimation module (ResUnet) and a deformation module (spatial transformer layers). We adopted -norm to regularize the deformation field instead of the traditional -norm regularization. Additionally, we utilized structural similarity (ssim) estimation during the training stage to enhance the structural consistency between the deformed images and the reference images. Experiments results indicated that by incorporating ssim loss, our cascaded methods not only achieved higher dice score of 0.9873, ssim score of 0.9559, normalized cross-correlation (NCC) score of 0.9950, and lower relative sum of squared difference (SSD) error of 0.0313 on CT images, but also outperformed the comparative methods on ultrasound dataset. The statistical -test results also proved that these improvements of our method have statisticalsignificance. In this study, the promising results based on diverse evaluation metrics have demonstrated that our model is simple and effective in deformable image registration (DIR). The generalization ability of the model was also verified through experiments on liver CT images and cardiac ultrasound images.

Displacement and damage analysis of earth dams during the 2023 Turkiye earthquake sequence

The earthquake sequence that occurred on 6 February 2023 in Turkiye caused significant damage to various infrastructures including geostructures such as dams. A total of 17 earth dams within a 200-km radius of the earthquake epicenter experienced varying degrees of damage, ranging from minor (∼2 cm) to major (up to ∼150 cm) deformations. As study of these reveals that the damaged dams are located within the closest distance to the fault of less than 30 km, with an average value of ∼12 km. This study specifically focuses on the seismic displacement analysis of the 17 damaged dams, utilizing the sliding block methods. The recorded motion data was analyzed using the kriging technique to estimate the spectral response at the dam sites. Moreover, the recorded ground motions were scaled to the resonant period of the dam site to estimate acceleration time history. The findings reveal that the rigid block analysis can provide an average estimation of seismic displacement with a relative error of less than 44%. The results of the damage analysis indicate that seven dams reached the ultimate limit state and two dams experienced the serviceability limit state. Moreover, the univariate and multivariate fragility functions are developed to estimate seismic probabilistic analysis of earth dams based on the observed data and the limit states. The results show that the selection of a single intensity measure (IM) and a combination of IMs can affect the predicted probability of failure. The findings provide an insight into the resilience assessment of dams and other geosystems during this strong earthquake.