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"Motion simulation"
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A digital twin-based machining motion simulation and visualization monitoring system for milling robot
Compared with traditional CNC machines, robot milling has the advantages of low cost, high flexibility, and strong adaptability, providing a new solution for complex surface machining. However, robot machining trajectory planning in the real world is time-consuming and has safety risks. At the same time, how to achieve 3D visualization monitoring in the milling process effectively is also a challenging problem. Digital twin technology, with its characteristics of multi-dimension, high-fidelity, virtual-real fusion, and real-time interaction, provides an effective way to solve these problems. For this purpose, the paper designs and implements a robot milling motion simulation and visualization monitoring system based on the digital twin system framework. The system uses the Unity3D platform to construct the robot’s digital twin body, designs a material removal algorithm based on mesh deformation, and establishes a milling motion simulation model. Through virtual-real mapping technology, the system establishes a bidirectional communication between virtual and physical entities and achieves the result mapping of the robot milling motion simulation and the visualization monitoring of the milling process. Finally, the motion simulation and real-time visualization monitoring of the milling process are tested, verifying the effectiveness and timeliness of the system.
Journal Article
Integrating Strong Ground Motion Simulation with Nighttime Light Remote Sensing for Seismic Damage Assessment in the 2025 Dingri Mw7.1 Earthquake
On 7 January 2025, an Mw7.1 earthquake struck Dingri County, Tibet, causing severe damage in a high-altitude, sparsely instrumented region where traditional damage assessment methods are limited. To address this, we developed an integrated \"source simulation–nighttime light validation\" framework. First, a kinematic source model (constrained by InSAR and teleseismic data) and the Unified Seismic Tomography models for continental China lithosphere 2.0 (USTClitho2.0) velocity model were used with the curved-grid finite difference method to simulate high-resolution ground motion and intensity fields. Second, NASA Black Marble (VNP46A2) nighttime light data, processed with the Block-Matching and 3D filtering (BM3D) algorithm, were analyzed to compute pixel-level radiance changes and township-level total nighttime light loss rates (TNLR). The results reveal a high spatial consistency between simulated high-intensity zones and areas of significant light loss. For instance, Mangpu Township, within a simulated high-intensity zone, exhibited a TNLR of 44.7%. This demonstrates that nighttime light remote sensing can effectively validate physical simulations in areas lacking dense seismic networks. Our framework provides a novel, complementary methodology for rapid and reliable post-earthquake damage assessment in high-mountain, data-sparse regions.
Journal Article
Influence of source uncertainty on stochastic ground motion simulation: a case study of the 2022 Mw 6.6 Luding, China, earthquake
On September 5, 2022 (local time), a magnitude 6.6 earthquake was reported to have occurred in Luding County, Sichuan Province, Southwest China. In this simulation, a widely used stochastic finite-fault model was used to analyze how the source models affect the near-fault earthquake ground motion simulations of the 2022 Mw 6.6 Luding earthquake in China. Seven different slip models, one of them obtained from common fault parameters and random distributed slip amount, were used to yield the best match with the recordings. The simulated earthquake ground motions calculated in the frequency band of 0.05–20 Hz were compared with the observed values in both the time and frequency domains. Twelve acceleration observation stations located near the fault plane were selected in our simulation for comparison. The average H/V curves were estimated using the available acceleration records to consider the local site effect at each selected station. The research results indicate that none of the source models adopted in this study fully estimate the observed values at all the selected ground-motion stations. The simulated values of some slip models underestimate the level of the Fourier amplitude spectrum at frequencies above 6 Hz. The underestimation may be attributed to the directivity effect, which may produce a higher amplitude of observed ground motion in the high-frequency band. All the slip models show similar average model deviations except for the random slip model. Finally, the peak ground accelerations and peak ground velocities were predicted at these selected near-fault observation stations. The results indicate that the peak accelerations and velocities obtained from seven slip models correlate well with each other, but are slightly lower than the recorded values at most stations. In addition, the synthetized results calculated from the random and inverted slip models can be the same level only if a greater stress drop is adopted in the random model.
Journal Article
Rapid estimation of disaster losses for the M6.8 Luding earthquake on September 5, 2022
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6.8 earthquake occurred in Luding, Sichuan Province, China, on September 5, 2022. Since towns and villages in the earthquake-stricken area are densely populated, the earthquake caused severe fatalities and economic losses. Rapid estimation of earthquake intensity and disaster losses is significantly important for post-earthquake emergency rescue, scientific anti-seismic deployment, and the reduction of casualties and economic losses. Therefore, we make a preliminary rapid estimation of the earthquake intensity and disaster losses in the aftermath of the Luding earthquake. The seismic intensity represents the distribution of earthquake disasters and the degree of ground damage and can be directly converted from the peak ground velocity (PGV) map. To obtain a reliable PGV distribution map of this earthquake, we combined the finite-fault model constrained by seismic observations, with the complex three-dimensional (3D) geological environment and topographical features to perform strong ground motion simulation. Then, we compared the consistency between the simulated ground motion waveforms and observations, indicating the plausibility and reliability of simulations. In addition, we transformed the PGV simulation results into intensity and obtained a physics-based map of the intensity distribution of the Luding earthquake. The maximum simulated intensity of this earthquake is IX, which is consistent with the maximum intensity determined from the post-earthquake field survey. Based on the simulated seismic intensity map of the Luding earthquake and the earthquake disaster loss estimation model, we rapidly estimated the death and economic losses caused by this earthquake. The estimated results show that the death toll caused by this earthquake is probably 50–300, with a mathematic expectation of 89. Thus the government should launch a Level II earthquake emergency response plan. The economic losses are likely to be 10–100 billion RMB, with a mathematical expectation of 23.205 billion RMB. Such seismic intensity simulations and rapid estimation of disaster losses are expected to provide a preliminary scientific reference for governments to carry out the targeted deployment of emergency rescue and post-disaster reconstruction.
Journal Article
Seismic scenario simulation and ANN-based ground motion model development on the North Tabriz Fault in Northwest Iran
Earthquakes pose significant seismic hazards in urban regions, often causing extensive damage to the built environment. In regions lacking robust seismic monitoring networks or sufficient data from historical events, ground motion simulations are crucial for assessing potential earthquake impacts. Yet, validating these simulations is challenging, leading to notable predictive uncertainty. This study aims to simulate four scenario earthquakes with moment magnitudes of 6.8, 7.1, 7.4, and 7.7 in Iran, specifically investigating variations in fault plane rupture and earthquake hypocenter. The North Tabriz Fault (NTF), located within the seismic gap in northwest Iran, is selected as the case study due to the lack of well-recorded ground motions from severe earthquakes, despite historical evidence of large-magnitude events. Simulations are conducted using a stochastic finite-fault ground motion simulation methodology with a dynamic corner frequency. Validation of the simulations is performed by comparing estimated peak ground motions and pseudo-spectral ordinates with existing ground motion models (GMMs), supplemented by inter-period correlation analysis. Simulation results reveal high hazard levels, especially in the northeastern area near the fault plane. Intensity maps in terms of the Modified Mercalli Intensity (MMI) scale underscore the urgency for comprehensive preparedness measures. Finally, a region-specific GMM is developed using Artificial Neural Networks (ANN) to predict peak ground motion parameters with an online platform accessible to end-users.
Journal Article
A Survey on Human Performance Capture and Animation
With the rapid development of computing technology, three-dimensional （3D） human body models and their dynamic motions are widely used in the digital entertainment industry. Human performance mainly involves human body shapes and motions. Key research problems in human performance animation include how to capture and analyze static geometric appearance and dynamic movement of human bodies, and how to simulate human body motions with physical effects. In this survey, according to the main research directions of human body performance capture and animation, we summarize recent advances in key research topics, namely human body surface reconstruction, motion capture and synthesis, as well as physics-based motion simulation, and further discuss future research problems and directions. We hope this will be helpful for readers to have a comprehensive understanding of human performance capture and animation.
Journal Article
Nonparametric ground motion models of arias intensity and significant duration for the Italian dataset
In the field of ground motion simulation, the stochastic site-based methodology relies on the existing database of ground shaking. Based on these methodologies, several properties of seismic signals are used to simulate seismic waves. These parameters could be evaluated either parametrically via linear or nonlinear regression techniques or non-parametrically via sophisticated machine-learning algorithms. Nonetheless, parametric models, which consist of a particular mathematical formulation, can be a source of large bias. In this study, machine learning techniques are employed to develop predictive models for two main input parameters of a stochastic site-based ground motion model: Arias intensity and significant duration, which control the time variation of the simulated ground shakings. The Arias intensity, defined by the integral of the square of the acceleration time series, and the significant duration, which is related to the strong shaking phase of an earthquake, are also of particular interest in structural and geotechnical engineering fields. For this purpose, the random forest approach is employed to develop prediction models for the Italian database. To guarantee the prediction accuracy of the models also for unseen future data, only 80 percent of the data is used for training, and the rest is reserved for testing the trained model. The model hyperparameters are tuned to control bias and variance trade-offs by k-fold cross-validation. For each model, a set of hyperparameters is selected, and a possible range is given. Then, a Bayesian optimization technique is implemented to find the best set of these hyperparameters among the given range. All these models provided promising results compared to the prior models in the literature.
Journal Article
Simulation of strong ground motion for a potential Mw7.3 earthquake in Kopili fault zone, northeast India
In this study, we present the results of strong ground motion simulation carried out for a potential earthquake (Mw7.3) in the Kopili source zone of northeast India using the stochastic finite fault modeling technique. Prior to simulation of the potential event, the technique was validated by simulating a recorded earthquake Mw5.3, which was located close to the Nagaon district of Assam, India. The earthquake records (Mw5.3) of ten stations in the study region were analyzed, and average source parameters, namely corner frequency, seismic moment, stress drop and source radius, estimated to be 0.48 Hz, 1.44E + 24 dyne-cm, 103.4 bar and 2.7 km, respectively. While estimating the source parameters, the path attenuation parameters (Q and ko) also constrained at each seismic station. Using the constrained attenuation parameters, site amplification factors and source parameters, we simulated strong ground motion time histories for Mw5.3 event at individual sites and found them comparable with amplitude and frequency content of the respective observed records satisfactorily. The results, therefore, validated the technique used in the study. Further, the fault parameters, site amplification factors and average of the constrained attenuation parameters were used in simulation of the potential event (Mw7.3) and strong ground motion predicted for the entire northeast region (NER) at a grid interval of 0.5°. We used average values of constrained quality factor (Q) and Kappa parameter (ko) equivalent to 182f0.95 and 0.038, respectively, in the simulation. It is apparent that the simulated PGA conditionally followed the attenuation curves of the region, and hence, we suggest developing an appropriate attenuation curve for the NER. The study reveals that the sites located up to 250 km distance away from the source zone may experience significant PGA ranging between 160 Gals and 360 Gals. The cities located within this zone, viz., Tezpur, Nagaon, Udalguri, Diphu and Bomdila, may witness strong to very strong ground shaking associated with substantial damage to buildings and other important structures in the region.
Journal Article
Strong ground motion simulations of the 2016 Kumamoto earthquakes using corrected empirical Green’s functions: methods and results for ESG6 blind prediction Steps 2 and 3 with improved parameters
This paper describes the methods and results of the strong ground motion simulations for three earthquakes from the 2016 Kumamoto earthquake sequence using corrected empirical Green’s functions. The target earthquakes were an aftershock (Mw 5.5), the largest foreshock of the sequence (Mw 6.1), and the mainshock (Mw 7.1). The corrected empirical Green’s function method was used in the simulations. This simulation method combines simple source and path factors with empirical site amplification and phase factors to generate realistic site-specific strong motions. Simulations were originally conducted to participate in blind prediction exercises in ESG6. Although the simulations performed in this study were based on the models submitted to the blind prediction committee, several modifications were made after the blind prediction exercise. First, the observed records at the target site of the blind prediction called KUMA were used to compare observed and synthetic strong ground motions. In addition, a regional spectral inversion was conducted to obtain a more appropriate Q-value and site amplification factor. Synthetic strong motions were found to explain the observed strong ground motions at KUMA and other stations. Comparisons with predictions by other methods and the sensitivity to the rupture scenario were also discussed. These results provide useful information for applying the corrected Green’s function method to strong ground motion simulations.
Journal Article