Motion Recordings Research Articles

Does seismic isolation reduce the seismic vulnerability and the variability of the inelastic seismic response? Large-scale experimental investigation

AbstractThis paper focuses on the large-scale experimental investigation of the seismic vulnerability and the variability of the inelastic seismic response of seismically isolated structures in comparison to conventional, fixed-based structures. The experimental setup comprises a steel structure consisting of two steel columns and a steel mass on top. The structure is seismically isolated using four friction pendulum bearings and subjected to an ensemble of strong recorded earthquake ground motion excitations using the shaking table of ETH laboratory. A mechanical clevis connection consisting of two hinges and two replaceable steel coupons is designed and constructed to facilitate the investigation of the seismic inelastic behavior of the structure for the selected ground motion record ensemble through the replacement of the damaged coupons after each shaking table excitation. Within this frame, the mechanical clevis connection presented in this study facilitates the parametric and experimental investigation of the seismic, inelastic behaviour of a wide range of structures and the experimental determination of their seismic fragility curves. The seismic vulnerability and the variability of the seismic response of the seismically isolated and the corresponding fixed-based structure are compared for three seismic hazard levels. The comparison of the response of the two structures demonstrates experimentally the ability of seismic isolation to reduce the seismic vulnerability and the variability of the seismic response of structures subjected to strong earthquake ground motion excitation, thus leading to the design of structures of higher performance, predictability and reliability in their response, even for extreme earthquake events.

Correlation models for next-generation amplitude and cumulative intensity measures using artificial neural networks

Various next-generation intensity measures (IMs) have emerged in recent years. For instance, filtered incremental velocity, FIV3, was shown to be efficient in predicting the collapse of structures, or average spectral acceleration, Saavg, shown to be well-correlated with a wide range of response, from initiation of structural damage yielding to structural collapse. Different ground motion models (GMMs) are available to predict the probabilistic distribution of these IMs, given a set of seismological parameters. Nonetheless, a few key correlation models are still missing in the literature, which are elemental for a more holistic approach to ground motion record selection. These models are vital when performing seismic hazard analysis and ground motion record selection using state-of-the-art approaches like the conditional spectrum or generalized conditional intensity measure (GCIM) methods. This study uses a single machine-learning-based generalized ground motion model (GGMM) to estimate all the IMs. The model is based on the Next-Generation Attenuation (NGA)-West2 database and was utilized to quantify the correlations between the residuals. Correlations were calculated for intra- and inter-event residuals, but only those for total residuals are presented here, given their utility. Since the calculation of residuals comes from the same GMM and the same database of ground motions, this produces more consistent correlation coefficients between all IMs. To facilitate the usage of these correlation coefficients, predictive models of the empirical data were developed using machine-learning-based techniques, namely artificial neural networks (ANNs). It was found that FIV3 is strongly correlated with Sa(~1 s) and itself across all periods and has a weak negative correlation with duration at short periods and near-zero correlation for longer periods. Also, a stronger negative correlation between Sa and significant duration was found, compared with other prominent existing models. Direct correlation models between Sa and Saavg are also proposed.

Identification of strong motion record baseline drift based on Bayesian-optimized Transformer network

Identification of strong motion record baseline drift based on Bayesian-optimized Transformer network

Seasonal Variability of Ice Motion for Hubbard and Valerie Glaciers, Alaska

Hubbard Glacier is a large fast-flowing tidewater-terminating glacier in the St. Elias Mountains and is connected at its terminus to Valerie Glacier. Although Hubbard Glacier has been shown to experience large intra-annual velocity changes and a long-term deceleration, previous seasonality studies have had limited timescale without a dense record of motion. Valerie Glacier’s variability has also been understudied, with only one study reporting its seasonal behaviour. The goal of this study was to combine ITS_LIVE, RADARSAT-2, RADARSAT Constellation Mission, and TerraSAR-X/TanDEM-X derived velocity data to create the densest record of motion ever constructed for Hubbard and Valerie glaciers from July 2013-April 2022 in order to explore seasonal velocity variability of both glaciers. Air temperature (NCEP-NCAR Reanalysis) was used to estimate surface melt on the glaciers, which was explored as a potential driver for seasonal velocity changes. Valerie Glacier had a seasonal pattern of fast flow in May, with minimum flow between August-November before accelerating again. Hubbard Glacier displayed a unique seasonal pattern that has not been previously observed on this glacier, with two periods of fast motion: one in May and one in December-February. It is inferred that the spring peaks and late summer/fall minimums on both glaciers are due to meltwater reaching the glacier bed and influencing the subglacial hydrology. The cause of the winter peak and slight velocity drop before the spring peak on Hubbard Glacier has not been determined and should be a topic for future studies, although it is hypothesized to influenced by its geometry.

Pulse component extraction and its application in simulation of pulse-like ground motions

A procedure for simulating new pulse-like ground motion based on natural pulse-like ground motion record is proposed. Firstly, an extraction method of pulse waveform based on Modified Ensemble Empirical Mode Decomposition is proposed, which reveals the relationship between half-wavelength width and pulse time-frequency information. Secondly, according to the response spectrum of the extracted non-pulse time-history, a triangular series is used to generate a stationary time-history, and then the extracted pulse component is added to the time-history, and the intensity envelope is adjusted to obtain the simulated pulse-like ground motion. Finally, the seismic response results of the Bouc-Wen-Baber-Noori model and the three-dimensional finite element model of the high-rise building are validated. It shows that the nonlinear effect of the simulated pulse-like ground motion by this method is close to that of the original natural pulse-like ground motion. The simulated pulse-like ground motion by this method can be used to analyze the inelastic response of various structures.

Paleomagnetic Records From Pulsed Magmatism in the Southwestern Laurentia Large Igneous Province and Cardenas Basalt Support Rapid Late Mesoproterozoic Plate Motion

AbstractMafic intrusions, lava flows, and felsic plutons in southwestern Laurentia have been hypothesized to be associated with the emplacement of a late Mesoproterozoic (Stenian Period) large igneous province. Improved geochronologic data resolve distinct episodes of mafic magmatism in the region. The ca. 1,098 Ma main pulse of southwestern Laurentia large igneous province (SWLLIP) magmatism is recorded by mafic intrusions across southeastern California to central Arizona. A younger episode of volcanism resulted in eruptions that formed the ca. 1,082 Ma Cardenas Basalt, which is the uppermost unit of the Unkar Group in the Grand Canyon. With the updated geochronological constraints, we develop new paleomagnetic data from mafic sills in the SWLLIP. Overlapping poles between the Death Valley sills and rocks of similar age in the Midcontinent Rift are inconsistent with large‐scale Cenozoic vertical axis rotations in Death Valley. We also develop a new paleomagnetic pole from the ca. 1,082 Ma Cardenas Basalt (pole longitude = 183.9°E, pole latitude = 15.9°N, = 7.4°, N = 18). The new paleomagnetic data are consistent with the pole path developed from time‐equivalent rocks of the Midcontinent Rift, supporting interpretations that changing pole positions are the result of rapid equatorward motion. These data add to the record of Laurentia's rapid motion from ca. 1,110 to 1,080 Ma that culminated in collisional Grenvillian orogenesis and the assembly of Rodinia.

Sufficiency assessment of intensity measures for natural and spectral-matched ground motion records

A correct Intensity Measure (IM) selection is essential for Performance-Based Earthquake Engineering (PBEE) applications, as any probabilistic seismic demand model (PSDM) depends significantly on the IM. If a single IM can describe the complexity of the corresponding ground motion record, it can be defined as sufficient in an absolute sense. However, this is unlikely because a single number should be able to inform on the frequency content, the amplitude, the duration, the energy content, etc. For this reason, literature studies have defined sufficiency in a relative sense to investigate whether one IM is more sufficient (i.e., more informative) than another in predicting the structural response. This work explores the relative sufficiency of eight scalar IMs through Nonlinear Response History Analyses (NRHAs) using two sets of 20 pairs of ground motion records. Both sets are spectrum-compatible and consist of unscaled natural and spectral-matched records. Also, both Cloud and Incremental Dynamic Analysis procedures are used. This study demonstrates that Cloud analysis cannot be used in its conventional form to study sufficiency when spectral-matched accelerograms are used. When natural accelerograms are employed, the results clearly indicate the existence of a sufficient IM among those selected. Conversely, it is more difficult to define the relative sufficiency of the IMs for spectral-matched records because the operation of record adjusting leads to similar structural demands. This result could question either the validity of using spectral-matched accelerograms for PBEE due to the lack of aleatory variability in the structural demand or the necessity of having a sufficient IM when a PSDM is fitted in a PBEE analysis using spectral-matched accelerograms.

Serviceability fragility assessment of non-seismically designed railway viaducts in Turkey under near-field and far-field earthquakes

While many railway viaducts still in use in Turkish railway networks are located on active fault zones that produced historical destructive earthquakes, they are not seismically designed in accordance to current standards. This study aims to provide a better understanding about the serviceability fragility of such systems by conducting detailed probability-based seismic assessments under near-field and far-field earthquakes. To achieve this, firstly, 3D finite element models of a range of selected viaducts in Turkey were generated based on their original design drawings. The developed FE models were validated by comparing the analytical modal frequencies with the results of in-situ dynamic tests involving a series of acceleration measurements. Nonlinear time history analyses were then carried out under 25 near-field and 25 far-field real three-component ground motion recordings to obtain the seismic response of each selected viaduct. Subsequently, probabilistic seismic demand models were defined using linear regression analysis to determine relationships between engineering demand parameters (EDPs) and ground motion intensity measures (IMs). Peak ground acceleration (PGA), peak ground velocity (PGV) and spectral acceleration at 1.0 s (Sa (1.0)) were used as the IM parameter, while the maximum lateral displacement of the bridge spans for different service velocities defined in the Eurocode was considered as the EDP at serviceability damage state. Finally, analytical fragility curves for all the selected railway viaducts were developed considering maximum damage probability for the IM levels. The results, in general, demonstrate the seismic vulnerability of the existing viaducts in Turkey. It is shown that while at low speed limits the viaducts exposed to far-field ground motions were more vulnerable than those under near-field ground motions, at high speed limits the viaducts subjected to near-field ground motions were more vulnerable. Also, it is seen that reinforced concrete and masonry viaducts are generally more vulnerable to earthquakes than the steel viaducts. The outcomes of this study should prove useful for the seismic risk assessment, loss estimation and rehabilitation of the railway transportation networks in future studies.

High-speed super-resolution structured illumination microscopy with a large field-of-view

Structured illumination microscopy (SIM) has been extensively employed for observing subcellular structures and dynamics. However, achieving high-speed super-resolution SIM with a large field of view (FOV) remains challenging due to the trade-offs among spatial resolution, imaging speed and FOV under limited bandwidth constraints. Here, we report a novel SIM technique to address this issue. By utilizing a high-speed camera and a rolling image reconstruction strategy to accelerate super-resolution image acquisition, as well as using a deep resolution enhancement to further improve spatial resolution, this SIM technique achieves imaging with a spatial resolution of 94 nm, a FOV of 102 × 102 µm2, and an imaging speed of 1333 frames per second. The exceptional imaging performance of this proposed SIM technique is experimentally demonstrated through the successful recording of the Brownian motion of fluorescent microspheres and the photobleaching of fluorescently labeled microtubules. This work offers a potential tool for the high-throughput observation of high-speed subcellular dynamics, which would bring significant applications in biomedical research.

Empirical approaches for non-linear site response: results for the ESG6-blind test

As a contribution to step 3 of the ESG6 blind prediction exercise, we present an application of two different, purely empirical approaches to estimate the strong ground motion at a soft site ("KUMA") from the observed ground motion at a reference rock site ("SEVO") for the two largest shocks of the Kumamoto 2016 sequence. The two methods estimate the non-linear transfer function between a reference rock and a sedimentary site by modifying the linear transfer function derived from weak motion recordings. The modification is based either on a machine learning tool based on a wide collection of Japanese weak and strong motion recordings and the associated site metadata (method 1), or on an estimate of a site-specific parameter related to an average non-linear site response (method 2). The acceleration time series are then derived at the sedimentary site of interest using an estimation of the time delay between wave arrivals at the rock and site stations, and a minimum phase assumption for the site transfer function. These predictions were made blindly, but after the ESG6 conference they could be compared both with the actual ground motion recorded at KUMA during the two shocks, and the average and range of all other predictions preformed for this benchmark. Both of these purely empirical methods provide an honorable prediction of usual engineering ground motion parameters of the two target events. The performance of these two purely empirical approaches is at least comparable to those of the numerical simulation methods for the foreshock—if not better—and slightly worse for the (largest) mainshock. As the methods required only recordings of weak motions at the target and a referent sites and very simple description of the soil profile. The use of moderate motions to constrain the frequency shift prediction for the second method and the consideration of an alternative phase modification are possible ways to improvement.Graphical

Video-based categorization system and frequency analysis of gestures in saxophone playing

The study of gestures in music performance provides valuable insights for instrumental learning. However, gestural vocabularies vary depending on the instrument being played, according to its postural and technical specificities. The goals of this study were twofold: first, to create a gesture categorization system for saxophone players, and second, to analyse their gestural behaviour across contrasting musical excerpts. A criteria-based observational analysis was conducted, considering the type and frequency of gestures identified in a database of 100 video and motion recordings. The categorization system, including 15 gesture types applicable to the case of saxophone playing, was further validated by 2 expert raters. A descriptive appendix is provided for the identification of each gesture type. Results revealed that: (1) knee and trunk flexion, feet elevation, mediolateral sway and flap were the most recurrent gestures among saxophone players; (2) energetic, fast-tempo excerpts led to higher movement frequency; and (3) impulsive gestures (head nods) were idiosyncratic of the excerpt containing repeated accentuated notes. These results present a definition of the gestural behaviour of saxophone players, which constitutes relevant knowledge for the development of future studies in the fields of injury prevention, body expression and historically informed performance.

An Earthquake Ground-Motion Model for Southwest Iberia

ABSTRACT Ground-motion models (GMMs) are fundamental for the estimation of ground shaking for probabilistic seismic hazard assessment. Because of the paucity of ground motion recordings in regions of low seismicity, stochastic approaches are often employed to generate synthetic data. In this study, we developed a GMM using a stochastic simulation approach for southwest Iberia—a zone for which seismic hazard is usually assessed using models developed for other regions. We collected geological, tectonic, and ground-motion data for offshore and inland Iberia, and calibrated several parameters for a stochastic simulation. The resulting synthetic response spectra were used to train a machine learning algorithm (artificial neural network) capable of predicting peak ground acceleration, peak ground velocity, and spectral acceleration on rock (VS30=760 m/s), along with the associated between-event and within-event terms. The resulting model was compared against other existing models for stable continental regions and ground-motion recordings for Portugal and Spain. The results indicate a good agreement with observations and the model can be used directly in probabilistic seismic hazard analysis for southwest Iberia.

Contributions of core, mantle and climatological processes to Earth’s polar motion

Earth’s spin axis slowly moves relative to the crust over time. A 120-year-long record of this polar motion from astronomical and more modern geodetic measurements displays interannual and multidecadal fluctuations of 20 to 40 milliarcseconds superimposed on a secular trend of about 3 milliarcseconds per year. Earth’s polar motion is thought to be driven by various surface and interior processes, but how these processes operate and interact to produce the observed signal remains enigmatic. Here we show that predictions made by an ensemble of physics-informed neural networks trained on measurements to capture geophysical processes can explain the main features of the observed polar motion. We find that glacial isostatic adjustment and mantle convection primarily account for the secular trend. Mass redistribution on the Earth’s surface—for example, ice melting and global changes in water storage—yields a relatively weak trend but explains about 90% of the interannual and multidecadal variations. We also find that core processes contribute to both the secular trend and fluctuations in polar motion, either due to variations in torque at the core–mantle boundary or dynamical feedback of the core in response to surface mass changes. Our findings provide constraints on core–mantle interactions for which observations are rare and global ice mass balance over the past century and suggest feedback operating between climate-related surface processes and core dynamics.

A Classification Method of Earthquake Ground Motion Records Based on the Results of K-Means Clustering Analysis

This paper presents a classification method for earthquake ground motion records utilizing the results of K-means cluster analysis. The moment magnitude and Joyner–Boore distance are utilized as the primary parameters for clustering the earthquake ground motion records. The classification boundaries are established through an examination of moment magnitude ranges, Joyner–Boore distance ranges, and spectral characteristics within each cluster. In this study, a comprehensive dataset comprising 7627 horizontal earthquake acceleration records was meticulously curated for analysis. The data were subjected to separate clustering and grouping procedures, allowing for insightful comparisons between the resultant clusters. Significant disparities in spectral characteristics across the classification groups were demonstrated. These differences become particularly pronounced when a moment magnitude threshold of 6 and a Joyner–Boore distance threshold of 140 km are employed to categorize the ground motion records. The approach underscores the substantial impact of classification based on earthquake ground motion spectral characteristics, while also mitigating the potential instabilities inherent in cluster analysis results. A refined and quantitatively robust framework for understanding and categorizing earthquake ground motions is provided, offering valuable insights for seismic data analysis and contributing to more accurate and reliable assessments of seismic activity.

Integration of High-Rate GNSS and Strong Motion Record Based on Sage–Husa Kalman Filter with Adaptive Estimation of Strong Motion Acceleration Noise Uncertainty

A strong motion seismometer is a kind of inertial sensor, and it can record middle- to high-frequency ground accelerations. The double-integration from acceleration to displacement amplifies errors caused by tilt, rotation, hysteresis, non-linear instrument response, and noise. This leads to long-period, non-physical baseline drifts in the integrated displacements. GNSS enables the direct observation of the ground displacements, with an accuracy of several millimeters to centimeters and a sample rate of 1 Hz to 50 Hz. Combining GNSS and a strong motion seismometer, one can obtain an accurate displacement series. Typically, a Kalman filter is adopted to integrate GNSS displacements and strong motion accelerations, using the empirical values of noise uncertainty. Considering that there are significantly different errors introduced by the above-mentioned tilt, rotation, hysteresis, and non-linear instrument response at different stations or at different times at the same station, it is inappropriate to employ a fixed noise uncertainty for strong motion accelerations. In this paper, we present a Sage–Husa Kalman filter, where the noise uncertainty of strong motion acceleration is adaptively estimated, to integrate GNSS and strong motion acceleration for obtaining the displacement series. The performance of the proposed method was validated by a shake table simulation experiment and the GNSS/strong motion co-located stations collected during the 2023 Mw 7.8 and Mw 7.6 earthquake doublet in southeast Turkey. The experimental results show that the proposed method enhances the adaptability to the variation of strong motion accelerometer noise level and improves the precision of integrated displacement series. The displacement derived from the proposed method was up to 28% more accurate than those from the Kalman filter in the shake table test, and the correlation coefficient with respect to the references arrived at 0.99. The application to the earthquake event shows that the proposed method can capture seismic waveforms at a promotion of 46% and 23% in the horizontal and vertical directions, respectively, compared with the results of the Kalman filter.

Regression-based Evaluation of Novel Intensity Measures under Alternative Ground Motion Record Sets

Regression-based Evaluation of Novel Intensity Measures under Alternative Ground Motion Record Sets

Prediction of Human Reaching Pose Sequences in Human–Robot Collaboration

Abstract In human–robot collaboration, robots and humans must work together in shared, overlapping, workspaces to accomplish tasks. If human and robot motion can be coordinated, then collisions between robot and human can seamlessly be avoided without requiring either of them to stop work. A key part of this coordination is anticipating humans’ future motion so robot motion can be adapted proactively. In this work, a generative neural network predicts a multi-step sequence of human poses for tabletop reaching motions. The multi-step sequence is mapped to a time-series based on a human speed versus motion distance model. The input to the network is the human’s reaching target relative to current pelvis location combined with current human pose. A dataset was generated of human motions to reach various positions on or above the table in front of the human starting from a wide variety of initial human poses. After training the network, experiments showed that the predicted sequences generated by this method matched the actual recordings of human motion within an L2 joint error of 7.6 cm and L2 link roll–pitch–yaw error of 0.301 rad on average. This method predicts motion for an entire reach motion without suffering from the exponential propagation of prediction error that limits the horizon of prior works.

Three-dimensional measurement method of binary particle collisions under dry and wet conditions

The description, calculation and design of solids processes, such as fluidization, pneumatic conveying or mixing, require knowledge of collision dynamics between particles and apparatus walls or interparticle collisions without, but often also with the presence of an additional liquid. For the study of such dry and wet interparticle collisions, an experimental setup is presented that allows the performance of dry or wet free binary collisions of spherical particles down to a minimum diameter of 1 mm. The recording of the particle motions in all three spatial directions is provided by two synchronized highspeed cameras. Methods and algorithms are presented for analyzing the translational and rotational motion in three-dimensional space, as well as for measuring the amount of liquid on the particles in the case of wet collisions. Motion data from dry collisions between equal sized and unequal sized spherical particles are compared with a three-parameter collision model. In addition, a first series of measurements of wetted collisions is used to compare the collision dynamics with dry collisions.

The Use of Immersive Technologies in Karate Training: A Scoping Review

This study investigates the integration of immersive technologies, primarily virtual reality (VR), in the domain of karate training and practice. The scoping review adheres to PRISMA guidelines and encompasses an extensive search across IEEE Xplore, Web of Science, and Scopus databases, yielding a total of 165 articles, from which 7 were ultimately included based on strict inclusion and exclusion criteria. The selected studies consistently highlight the dominance of VR technology in karate practice and teaching, with VR often facilitated by head-mounted displays (HMDs). The main purpose of VR is to create life-like training environments, evaluate performance, and enhance skill development. Immersive technologies, particularly VR, offer accurate motion capture and recording capabilities that deliver detailed feedback on technique, reaction time, and decision-making. This precision empowers athletes and coaches to identify areas for improvement and make data-driven training adjustments. Despite the promise of immersive technologies, established frameworks or guidelines are absent for their effective application in karate training. As a result, this suggests a need for best practices and guidelines to ensure optimal integration.

Simultaneous Recording of Remote Domain Dynamics in Membrane Proteins Using the Double-Labeled DXB/DXT Technique.

Protein dynamics play important roles in biological functions, which accompany allosteric structure changes. Diffracted X-ray blinking (DXB) uses monochromatic X-rays and nanocrystal probes. The intramolecular motion of target proteins is analyzed from the intensity changes in detector signals at the diffraction rings. In contrast, diffracted X-ray tracking (DXT) elucidates molecular dynamics by analyzing the trajectories of Laue spots. In this study, we have developed a dual-labeling technique for DXB and DXT, allowing the simultaneous observation of motions at different domains in proteins. We identified zinc oxide (ZnO) crystals as promising candidates for the second labeling probes due to their excellent diffraction patterns, high chemical stability, and favorable binding properties with proteins. The diffraction spots from the ZnO crystals are sufficiently separated from those of gold, enabling independent motion analysis at different domains. Dual-labeling DXB was employed for the motion analysis of the 5-HT2A receptor in living cells. Simultaneous motion recording of the N-terminus and the second extracellular loop demonstrated ligand-induced motion suppression at both domains. The dual-labeling DXT technique demonstrated a capsaicin-induced peak shift in the two-dimensional motion maps at the N-terminus of the TRPV1 protein, but the peak shift was not obvious in the C-terminus. The capsaicin-induced motion modulation was recovered by the addition of the competitive inhibitor AMG9810.