Acceleration Time Series Research Articles

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

Image-Acceleration Multimodal Danger Detection Model on Mobile Phone for Phone Addicts.

With the popularity of smartphones, a large number of "phubbers" have emerged who are engrossed in their phones regardless of the situation. In response to the potential dangers that phubbers face while traveling, this paper proposes a multimodal danger perception network model and early warning system for phubbers, designed for mobile devices. This proposed model consists of surrounding environment feature extraction, user behavior feature extraction, and multimodal feature fusion and recognition modules. The environmental feature module utilizes MobileNet as the backbone network to extract environmental description features from the rear-view image of the mobile phone. The behavior feature module uses acceleration time series as observation data, maps the acceleration observation data to a two-dimensional image space through GADFs (Gramian Angular Difference Fields), and extracts behavior description features through MobileNet, while utilizing statistical feature vectors to enhance the representation capability of behavioral features. Finally, in the recognition module, the environmental and behavioral characteristics are fused to output the type of hazardous state. Experiments indicate that the accuracy of the proposed model surpasses existing methods, and it possesses the advantages of compact model size (28.36 Mb) and fast execution speed (0.08 s), making it more suitable for deployment on mobile devices. Moreover, the developed image-acceleration multimodal phubber hazard recognition network combines the behavior of mobile phone users with surrounding environmental information, effectively identifying potential hazards for phubbers.

New methodology to estimate fatigue from acceleration time series experimentally validated in an onshore and floating offshore wind turbine

In onshore wind turbines, the association of fatigue damage with the environmental conditions based on the wind field properties have already been shown to be effective. However, most of the predictive power of such approach is based on average results, being the informative power regarding a single event extremely reduced. In this paper, a new methodology based on a reduced order model where the tower top accelerations are explicitly taken into consideration is proposed, with the adequate adaptations for floating offshore and onshore applications. After a numerical validation of the model predictions using OpenFAST, the methodology is used to reconstruct the bending moments time series of both an onshore and a floating wind turbine that are currently being monitored with accelerometers and strain gauges. The existence of this experimental data allowed for a direct comparison of the accelerations based estimates with the values obtained from the strain gauges readings and the method was proven to reproduce with great accuracy the tower bending moments time series. This conclusion sustain the claim that the proposed methodology for fatigue estimation is more robust than conventional SCADA-driven approaches and more informative regarding each individual event, making it suitable to be used in real time decision making processes.

Clustering-Based Classification of Polygonal Wheels in a Railway Freight Vehicle Using a Wayside System

Polygonal wheels are one of the most common defects in train wheels, causing a reduction in comfort levels for passengers and a higher degradation of vehicle and track components. With the aim of contributing to the safety and reliability of railway transport, this paper presents the development of an innovative methodology for classifying polygonal wheels based on a wayside system. To achieve that, a numerical train-track interaction model was adopted to simulate the passage of a freight train over a virtual wayside monitoring system composed of a set of accelerometers installed on the rails. Then, the acquired acceleration time series was transformed to a frequency domain using a Fast Fourier transform (FFT), and on this data, damage-sensitive features were extracted. The features based on Principal Component Analysis (PCA) showed great sensitivity to the harmonic order, while the ones based on Continuous Wavelet Transform (CWT) model showed great sensitivity to the defect amplitude. One step further, all features are merged using the Mahalanobis distance in order to obtain a damage index strongly correlated with the polygonal defect. Finally, a cluster analysis allowed the automatic classification of polygonal wheels, according to the harmonic order (harmonic-based) and defect amplitude (amplitude-based). The proposed methodology demonstrated high efficiency in identifying different types of polygonal wheels using a minimum layout of two sensors.

Feasibility study for test rig assessments of fish passage conditions in a Kaplan turbine

The assessment of fish passage conditions in hydroelectric turbines consists of identifying and quantifying physical magnitudes leading to increased risks of injury of fish passing through turbines in operation. Such assessments are usually carried out either with the use of computer-based methods during design or with field testing of live fish and sensors passing through prototypes. A method in between consists of test rig experimentation, which is critical for testing fish-focused design concepts and offers the opportunity for implementing the most effective design measures for improved fish survivability. However, fish-related assessments in test rigs are not sufficiently documented for industrial applications. This work presents the main findings of an experimental campaign to quantify fish-related hydraulic magnitudes in a physical model of a Kaplan turbine in a commercial test rig. Two operating conditions were tested by releasing miniaturized autonomous sensor devices (termed Sensor Fish Mini) at the turbine intake flow, passing them through the runner in motion and recovering them at the draft tube exit. During passage, time series of acceleration, absolute pressure and rotational velocity were recorded. The recordings were then interpreted to determine the magnitude and likely location of hydraulic stressors hazardous to fish. The statistical tests on the reported measurements indicated that low pressure, collisions on the runner and rotations in the draft tube were not different between the two tested operating points. On the other hand, pressure drop and collision rates on the distributor differed considerably as a function of net head. The outcomes of this investigation showed that test rig evaluations of fish-related properties with Sensor Fish Mini can contribute to an evidence-based development of turbine geometries designed for providing safer passage conditions. Future work will investigate the scaling of test rig measurements to hydraulically equivalent magnitudes in the prototype and their biological consequences.

Directionality of FIV3 ground-motion intensities during the 6 February 2023 Kahramanmaraş, Türkiye earthquake doublet

At present time, ground-motion prediction models neglect the directionality observed in horizontal components of earthquake ground motions, that is, the important changes in ground-motion intensity that occur with changes in azimuth. This study presents an investigation of the directionality of a recently proposed measure of ground-motion intensity during the 6 February 2023, Mw 7.8 Pazarcık and Mw 7.5 Elbistan earthquake doublet in the Kahramanmaraş region of Türkiye, which resulted in the collapse of more than 35,000 buildings and caused almost 60,000 fatalities. The studied intensity measure is referred to as FIV3, which has been shown to be better correlated with structural collapse than the spectral acceleration at the fundamental period of the structure. The improved intensity measure is period-dependent and is computed as the sum of the three largest incremental velocities with the same polarity obtained from the area under segments of a low-pass filtered ground acceleration time series. The following aspects are studied in this article: variation of FIV3 intensity with changes in the orientation; variation of FIV3 intensity with changes in the period of vibration; attenuation of FIV3 intensities with increasing distance; and spatial distribution of the orientation of maximum FIV3 intensity. This study is based on 231 pairs of records from the Mw 7.8 main event and 222 pairs of records from the Mw 7.5 event. Similarly to the directionality of spectral ordinates, it is found that the directionality of FIV3 intensity also increases with increasing period. Strong directionality occurred not only in the near field but up to distances as large as 400 km from the epicenter. The orientation of maximum FIV3 intensity is found to occur close to the transverse orientation, consistent with observations for the orientation of maximum spectral ordinates during strike-slip earthquakes.

Modeling and Prediction of Nonstationary Ground Motions as Time–Frequency Images

Proper modeling of nonstationary characteristics of ground motions is critical in estimating the seismic performance of structures. The importance of ground motion nonstationarity is being increasingly recognized in seismic design codes and provisions. This paper develops a new method for predicting seismic ground motions in the joint time–frequency domain based on recorded ground motions. We treat wavelet energy maps of acceleration records as images and extract essential patterns from them using principal component analysis. These patterns are then linked to the seismic source, path, and site variables using general regression neural network. The resulting model predicts an image representing the expected evolutionary power spectrum conditioned on the input variables. An example application is presented using records from the Next Generation Attenuation of Ground Motions Database of the Pacific Earthquake Engineering Center. As opposed to conventional ground motion prediction models, the proposed approach retains the time domain characteristics of ground motions. The results show that the proposed model can predict significant patterns in the seismic energy distribution, acceleration time series, and 5% damped elastic spectral accelerations.

A numerical methodology for estimating site-specific cascading earthquake and tsunami dynamic loading on critical infrastructure

A numerical methodology for deriving cascading earthquake and tsunami loading patterns for use in critical infrastructure design is proposed. Earthquake source parameters are first used to generate synthetic ground motion acceleration time series at the site of a critical infrastructure using an open-source stochastic extended finite-source simulation algorithm. Using the same earthquake source information, the tsunami phases of generation, propagation, and inundation are modeled using a coupling approach for determining tsunami wave forces. The coupling approach combines (1) a code based on non-linear shallow water equations solved by the Finite Volume method, which is used to capture the tsunami flow characteristics from generation through the inundation phase until relatively close to the structure, coupled with (2) an open-source computational fluid simulation code, which relies on solving the Navier–Stokes equations using the Smoothed Particle Hydrodynamics method. An appendix presents the validation of the numerical methodology through correlation of the numerical simulations with field data recorded during the 2011 Japan Earthquake and Tsunami. The methodology is then employed to characterize the ground shaking and tsunami loading patterns at the site of the container terminal of the Sines deep-water seaport in Portugal, when subjected to the historic 1755 Great Lisbon Earthquake and Tsunami plausible scenarios. For the site, the generated ground motion acceleration time series show peak accelerations exceeding Eurocode 8 design values. Tsunami flow depths and velocities were computed in horizontal and vertical directions and could be used in the assessment of the existing infrastructure or the design of the future expansion of the port.

Earthquake Early Warning Starting From 3 s of Records on a Single Station With Machine Learning

AbstractWe introduce the Ensemble Earthquake Early Warning System (E3WS), a set of Machine Learning (ML) algorithms designed to detect, locate, and estimate the magnitude of an earthquake starting from 3 s of P‐waves recorded by a single station. The system is made of six Ensemble ML algorithms trained on attributes computed from ground acceleration time series in the temporal, spectral, and cepstral domains. The training set comprises data sets from Peru, Chile, Japan, and the STEAD global data set. E3WS consists of three sequential stages: detection, P‐phase picking, and source characterization. The latter involves magnitude, epicentral distance, depth, and back azimuth estimation. E3WS achieves an overall success rate in the discrimination between earthquakes and noise of 99.9%, with no false positive (noise mis‐classified as earthquakes) and very few false negatives (earthquakes mis‐classified as noise). All false negatives correspond to M ≤ 4.3 earthquakes, which are unlikely to cause any damage. For P‐phase picking, the Mean Absolute Error is 0.14 s, small enough for earthquake early warning purposes. For source characterization, the E3WS estimates are virtually unbiased, have better accuracy for magnitude estimation than existing single‐station algorithms, and slightly better accuracy for earthquake location. By updating estimates every second, the approach gives time‐dependent magnitude estimates that follow the earthquake source time function. E3WS gives faster estimates than present alert systems relying on multiple stations, providing additional valuable seconds for potential protective actions.

Near-fault ground motion characteristics and its effects on a collapsed reinforced concrete structure in Hatay during the February 6, 2023 Mw7.8 Kahramanmaraş earthquake

Ground motion in the vicinity of fault rupture produces pulse-type loading that may impose severe lateral displacement demands on structures due to forward-directivity effects. The Mw7.8 Kahramanmaraş Earthquake on February 6, 2023 created very high amplitude ground motions in the city of Hatay with distinct near-fault characteristics. In the city of 400,000 inhabitants, 33 % of the building stock was severely damaged or collapsed. Among the collapsed buildings, it was observed that there were new buildings built according to the latest seismic regulations. The seismic characteristics of the ground motion in Hatay were examined, the fault rupture, ground conditions and earthquake source characteristics affecting the engineering demand parameters were determined. Then, the seismic behavior and the reasons for the collapse of a 13-story reinforced concrete building, which is thought to be earthquake resistant with its structural system and material quality, were investigated using acceleration time-series recorded at a station very close to the building location. It has been understood that in shear wall systems, wall position and seismic detailing can impart significant damage (total destruction) on these elements under the effect of large displacement and high axial load demand by limiting the required ductility. Wall boundaries require more stringent confinement detailing than stated by the code under near-fault effects.

Dynamic displacement estimation of structures using one-dimensional convolutional neural network

For large infrastructures, dynamic displacement measurement in structures is an essential topic. However, limitations imposed by the installation location of the displacement sensor can lead to measurement difficulties. Accelerometers are characterized by easy installation, good stability and high sensitivity. For this regard, this paper proposes a structural dynamic displacement estimation method based on a one-dimensional convolutional neural network and acceleration data. It models the complex relationship between acceleration signals and dynamic displacement information. In order to verify the reliability of the proposed method, a finite element-based frame structure was created. Accelerations and displacements were collected for each node of the frame model under seismic response. Then, a dynamic displacement estimation dataset is constructed using the acceleration time series signal as features and the displacement signal at a certain moment as target. In addition, a typical neural network was used for a comparative study. The results indicated that the error of the neural network model in the dynamic displacement estimation task was 9.52 times higher than that of the one-dimensional convolutional neural network model. Meanwhile, the proposed modelling scheme has stronger noise immunity. In order to validate the utility of the proposed method, data from a real frame structure was collected. The test results showed that the proposed method has a mean square error of only 5.097 in the real dynamic displacement estimation task, which meets the engineering needs. Afterwards, the outputs of each layer in the dynamic displacement estimation model are visualized to emphasize the displacement calculation process of the convolutional neural network.

Multiscale entropy algorithms to investigate the degree of complexity and variability of trunk acceleration time series in patients with Parkinson's disease

Multiscale entropy algorithms to investigate the degree of complexity and variability of trunk acceleration time series in patients with Parkinson's disease

Detection of Structural Damage in a Shaking Table Test Based on an Auto-Regressive Model with Additive Noise

Damage identification plays an important role in enhancing resilience by facilitating precise detection and assessment of structural impairments, thereby strengthening the resilience of critical infrastructure. A current challenge of vibration-based damage detection methods is the difficulty of enhancing the precision of the detection results. This problem can be approached through improving the noise reduction performance of algorithms. A novel method based partially on the errors-in-variables (EIV) model and its total least-squares (LS) algorithm is proposed in this study. Compared with a classical damage detection approach involving adoption of auto-regressive (AR) models and the least-squares (LS) method, the proposed method accounts for all the observation errors as well as the relationships between them, especially in an elevated level of noise, which leads to a better accuracy. Accordingly, a shaking table test and its corresponding finite element simulation of a full-scale web steel structure were conducted. The acceleration time-series output data of the model after suffering from different seismic intensities were used to identify damage using the presented detection method. The response and identification results of the experiment and the finite element analysis are consistent. The finding of this paper indicated that the presented approach is capable of detecting damage with a higher accuracy, especially when the signal noise is high.

Impact load identification method based on artificial neural network for submerged floating tunnel under collision

In order to minimize economic and human losses due to collisions that may cause serious problems, such as severe damage and collapse, in a submerged floating tunnel (SFT), an impact load identification method that can estimate the magnitude, as well as the location of the impact as the point, is proposed. The proposed method is based on an artificial neural network, specifying multi-layered perceptron (MLP), and its performance is verified by numerical simulation and experimental tests. The structural response data in the time domain obtained from the SFT when collisions occur within an ocean current is used to train the neural network model. The MLP-based identification system is investigated to establish each purpose of impact points and magnitude estimation considering the classification and regression model. In the numerical simulation, a finite element model of the SFT simplified as the beam on an elastic foundation is established in ABAQUS and the time history of impact is used to simulate the impact as a collision in hydraulic resistance. The time-series acceleration responses of the simulation are extracted to convert to input variables. The experiment with an SFT model, a set of accelerometers, and an impact hammer is conducted for the identification system validation. Only using the time-series acceleration as input variables for the neural network shows an accurate estimation of the impact points and the magnitude over 97%, and provides reasonable results using the minimal amount of data.

Relevance of deep sequence models for recognising automated construction activities: a case study on a low-rise construction system

Recognising activities of construction equipment is essential for monitoring productivity, construction progress, safety, and environmental impacts. While there have been many studies on activity recognition of earth excavation and moving equipment, activity identification of Automated Construction Systems (ACS) has been rarely attempted. Especially for low-rise ACS that offers energy-efficient, cost-effective solutions for urgent housing needs, and provides more affordable living options for a broader population. Deep learning methods have gained a lot of attention because of their ability to perform classification without manually extracting relevant features. This study evaluates the feasibility of deep sequence models for developing an activity recognition framework for low-rise automated construction equipment. Time series acceleration data was collected from the structure to identify major operation classes of an ACS. Long Short Term Memory Networks (LSTM) were applied for identifying the activity classes and the performance was compared with that of traditional machine learning classifiers. Diverse augmentation methods were adopted for generating datasets for training the deep learning classifiers. Several recently published literature seem to establish the superiority of complex deep learning techniques over traditional machine learning algorithms regardless of the application context. However, the results of this study show that all the conventional machine learning classifiers perform equivalently or better than deep learning classifiers in identifying activities of the ACS. The performance of the deep learning classifiers is affected by the lack of diversity in the initial dataset. If the augmented dataset significantly alters the characteristics of the original dataset, it may not deliver good recognition results.

Reproductive behavior of Atlantic halibut (Hippoglossus hippoglossus) interpreted from electronic tags.

Reproductive timing, location, and behavior are important characteristics that determine marine population dynamics, structure, and resilience to threats, including fishing and climate change. It is challenging to evaluate factors driving variability in these reproductive traits in wild fishes because of the difficulty observing individuals in their natural environments. In the present study, we used high-resolution depth, temperature, and acceleration time series recorded by pop-up satellite archival tags to (1) identify and characterize patterns in depth and acceleration that may be indicative of spawning events in large Atlantic halibut (Hippoglossus hippoglossus), and (2) estimate the effects of individual traits (body size and sex) and environmental factors (location and temperature) on spawning time and frequency. Unique rapid rises observed in the winter depth profiles were interpreted as spawning events. The initiation of the first presumed spawning rise was negatively correlated to water temperature experienced during the prespawning season, suggesting that currently increasing water temperature in the Gulf of St. Lawrence may induce phenological change in halibut spawning time. The number of rises of batch-spawning females was unrelated to female body size. The present study demonstrates how electronic tagging can be used for in-depth characterization of timing, location, and behaviors associated with spawning in a large flatfish species. Such information can inform spatiotemporal management and conservation measures aiming to protect species from directed fishing and by-catch during spawning.

A deep neural network-assisted metamodel for damage detection of trusses using incomplete time-series acceleration

In this article, a deep neural network (DNN)-driven metamodel is first introduced to damage identification of trusses utilizing acceleration signals incompletely measured from limited sensors. This metamodel can automatically learn its damage-sensitive properties itself to construct a better DNN from previously trained poorer ones. Data utilized to build such a model are generated by finite element method (FEM). In which, inputs are the acceleration behavior corresponding to measurement sensors, and outputs are the damage ratios of truss members. The damage site and extent of low-risk members predicted by the current DNN are eradicated by a suggested damage threshold. This helps to dramatically reduce the number of output units to feed into the next DNNs. By repeating such a procedure, the subsequently upgraded DNNs become more precise after several iterations although they only require a simple network architecture, fewer samples, small epochs and less computational effort. To illustrate the efficiency and robustness of the present metamodel, four 2D and 3D trusses with various damage scenarios are investigated. Obtained results indicate that the suggested approach can reliably diagnose both the location and severity of damaged truss members using acceleration behavior measured by a few sensors in a relatively short period of time, even with high noise levels.

Simulating the strong ground motion of the 2022 MS6.8 Luding, Sichuan, China Earthquake

Stochastic finite-fault simulations are effective for simulating ground motions and are widely used in engineering to determine the impacts of ground motion and develop relevant predictive equations. In this study, the source, path, and site amplification coefficient of western Sichuan Province, China, and stochastic finite-fault simulations were used to simulate the acceleration time series, Fourier amplitude spectra, and 5% damped response spectra of 28 strong-motion stations with rupture distances within 300 km of the 2022 MS6.8 Luding earthquake. The simulation results of 14 stations at rupture distances of 45–185 km match the observation. However, the simulation results of 3 near- and 6 far-field stations at rupture distances of 12–36 km and 222–286 km, respectively, were obviously deviated from the observations. Simulation results of the near-field stations are larger than the observations at high frequencies (>6 Hz). The discrepancy likely comes from the nonlinear site effect of near-field stations, which reduced the site amplification at high frequencies. Simulation result of the far-field stations is smaller than the observation at frequencies above 1 Hz. As these stations are located close to the Longmenshan Fault Zone (LFZ), thus, we obtained a new quality factor (Q) from data of historical events and stations located around LFZ. Using the new Q value, the discrepancies of the high-frequency simulation results of the far-field stations were corrected. This result indicated that the laterally varying Q values can be used to address the impact of strong crustal lateral heterogeneity on simulation.

A robust approach to calculating bridge displacements from unfiltered accelerations for highway and railway bridges

Bridge displacement, particularly due to specific loading events, is increasingly seen as a data signal that potentially contains information useful for bridge management. The lack of a convenient fixed reference location historically made displacement measurement challenging in field conditions, but recent developments in image processing have enabled low-cost, camera-based displacement measurements. Even so, logistical challenges remain. These include organising access to locate the camera, stabilising the camera in windy conditions and managing the large data files that result. Consequently, since accelerometers are widely used, research has continued to investigate recovery of displacement through double integration of acceleration signals. The main challenge with this approach is the errors in the calculated displacement signal due to the low frequency noise. Much of the research to overcome this challenge has involved the use of filters, but results can be highly sensitive to parameter selection and it can be difficult to define the confidence level for the calculated displacement. In this paper, an approach is proposed that does not require the use of filters. The approach, instead, focuses on the quality of hardware used to minimise low frequency noise in acceleration signals, and then implements various quality checks for different bridge and loading scenarios. Sensitivity of the proposed procedure to the choice of start and end points in the acceleration time series data is demonstrated and an approach to overcome this sensitivity has been developed. The robustness of the approach was trialled on three different bridges, subject to three different loading scenarios, that is, a moving truck, a moving train, and pseudo static loading with a truck stopping on a bridge. Where the calculated displacements passed quality checks, they were found to be in good agreement with the directly measured displacements (recorded with Imetrum displacement measurement system). Unsurprisingly, long duration, pseudo-static loading is the most challenging case due to cumulation of low frequency noise effects, where the quality checks correctly identified that the calculated signal would be unreliable.

An Empirical Spectral Ground-Motion Model for Iran Using Truncated Iranian Strong-Motion Database Enriched by Near-Field Records

ABSTRACT An updated strong-motion database of the Iranian earthquakes has been used to propose empirical equations for the prediction of peak ground velocity (PGV), peak ground acceleration (PGA), and 5%-damped Spectral Accelerations (SA) up to0.0 s for geometric mean of horizontal components. Some records from the NGA-West2 are added to the database to enrich it at near source distances for large magnitude events. Lack of data in the near source distances causes less accuracy in previous Iranian Ground Motion Models (GMMs) in comparison with the current study. In this work, the regression analyses have been performed on a truncated database which causes to obtain unbiased results. We used 3015 acceleration time series from 594 earthquakes after truncation of data to develop a new GMM. The provided model is valid for Joyner-Boore distances ranging from 0 km to80 km and magnitudes ranging from 4 to 7.9 and Vs30 ranging from50 m/s to500 m/s.