Structural Response Data Research Articles

Dynamic performance assessment of offshore wind structures based on root morphology model

Assessing the dynamic performance of offshore wind turbines (OWTs) is crucial for their safe operation and maintenance. To evaluate the structural dynamic performance, traditional methods rely on the reanalysis of damage models, leading to the lack of the real-time capability. Therefore, this paper presents a structural dynamic performance assessment technology based on the root morphology model to address such issue. The approach is developed to synergize data fusion-based and environmental condition classification-based dynamic performance assessment technologies. The data fusion-based method, using optimized VMD for denoising and ARMA models for free response signals, enables real-time dynamic performance assessment by tracking changes in AR coefficients over time. In parallel, the environmental condition classification-based method employs wind speed and wave height monitoring data to classify structural response data into multiple healthy sub-databases. Subsequently, Extenics is applied to compute the correlation between the data to be evaluated and the performance state levels based on the classified data, providing a precise evaluation of the structural dynamic performance. Finally, these two technologies are integrated through root morphology models, which fully consider structural vibration response data and environmental monitoring data. To verify the real-time and reliability of the proposed method, field study has been carried out on a 4 MW monopile OWT during the passage of Typhoon 'In-fa'. Results indicate that the root morphology model-based assessment technique effectively evaluates the impact of typhoons on the dynamic performance of OWTs. Further, a rose diagram for the dynamic performance assessment of OWTs is drawn to achieve real-time evaluation of OWT dynamic performance, which has certain engineering significance to guarantee the safe operation of OWTs and reduce the cost of operation and maintenance.

VALIDATION AND PARAMETRIC STUDY OF LEAN DUPLEX STAINLESS STEEL HOLLOW SECTION STUB COLUMNS BY FINITE ELEMENT ANALYSIS

In this research, a parametric study was conducted on square hollow sections (SHS) and rectangular hollow sections (RHS) columns made of a lean duplex stainless steel (LDSS) grade, namely EN 1.4162. The aim was to determine structural response data due to variations of different design parameters, which is vital in designing structural members. Finite element modeling was utilized for this and the validity of the models was confirmed by comparing them with existing experimental test data. Thickness, corner radius, load eccentricity and slenderness ratio were taken as the varying parameters to study. A total of 32 columns were modeled to study with five variations for each parameter, making a total of 160 variations. For each variation, the ultimate load and the corresponding end shortening were recorded. Each parameter has some effects on ultimate load or end shortening or both except corner radius, which has no significant effect. To visualize the effect of a parameter, a trend line equation was developed by plotting the average result of all column models for each variation of that particular parameter. A linear increase in ultimate load and end shortening can be observed with an increase in column thickness, while opposite trends were witnessed with load eccentricity. Though a linear rise in end shortening was caused by the slenderness ratio, no significant change was seen in the ultimate load of the columns. The mathematical equations presented can be employed to forecast the maximum load-bearing capacity and the associated amount of end shortening when designing structural hollow sections made of LDSS.

Automatic damage detection and data completion for operational bridge safety using convolutional echo state networks

Structural response data might be partially missing during acquisition and transmission by a structural health monitoring (SHM) system, affecting subsequent structural damage identification. Conventional deep learning methods require a substantial amount of data and have low training efficiency. With this in mind, this paper proposes a hybrid approach for data reconstruction and damage identification using an echo state network embedded with convolutional layers. The network has a training-free reservoir layer and focuses on historical information and multi-channel spatial features. As data redundancy occurs in most measured structural vibration responses, the extended Frobenius norm-principal component analysis method is applied to select valuable samples for updating the network, which greatly enhances the training efficiency. Experimental and numerical data from a physical bridge model is used to verify the data reconstruction capability and damage identification accuracy of the proposed method, and its performance is further compared with several existing deep learning methods.

Experimental study on the elastic response characteristics of a rotating body under an oblique water impact load

In this study, an integrated experimental model was designed and a data acquisition system was established to capture the structural response of an elastic rotating body throughout the oblique water impact process. This established experimental methodology offers a dependable approach for gathering structural response data during the water impact process. The axial impact loads and effective stress on the rotating body throughout water impact were measured using acceleration sensor and strain gauge rosettes installed in the water impact zone of the structure, while a high-speed camera system captured the evolution of the flow field. Results from the arbitrary Lagrangian-Eulerian (ALE) numerical model were compared with those from repeated experiments, revealing good agreements. The article conducted water impact tests at different impact initial conditions, analyzing the response of the body to the impact load during water entry process across different water impact velocities and angles. Observations during the oblique water impact process revealed that the stress response at the center of the rotating body impact surface is approximately equal to that of the first water impact point, with this effect becoming more pronounced at higher velocities. This study contributes valuable experimental data for numerical investigations into the water impact of elastic structures.

Graph Feature Refinement and Fusion in Transformer for Structural Damage Detection.

Structural damage detection is of significance for maintaining the structural health. Currently, data-driven deep learning approaches have emerged as a highly promising research field. However, little progress has been made in studying the relationship between the global and local information of structural response data. In this paper, we have presented an innovative Convolutional Enhancement and Graph Features Fusion in Transformer (CGsformer) network for structural damage detection. The proposed CGsformer network introduces an innovative approach for hierarchical learning from global to local information to extract acceleration response signal features for structural damage representation. The key advantage of this network is the integration of a graph convolutional network in the learning process, which enables the construction of a graph structure for global features. By incorporating node learning, the graph convolutional network filters out noise in the global features, thereby facilitating the extraction to more effective local features. In the verification based on the experimental data of four-story steel frame model experiment data and IASC-ASCE benchmark structure simulated data, the CGsformer network achieved damage identification accuracies of 92.44% and 96.71%, respectively. It surpassed the existing traditional damage detection methods based on deep learning. Notably, the model demonstrates good robustness under noisy conditions.

A hierarchical Bayesian approach for parameter identification in structural deterioration models using probabilistic surrogate models

Most structural systems currently in operation are subjected to potentially multiple deterioration processes, for which different models exist to describe their evolution in time. The quantities of interest (QoIs) involved in them are often directly unobservable. However, advances in Structural Health Monitoring (SHM) have enabled gathering structural response data which can be used to indirectly make inferences on unobservable (state) quantities through Bayesian inference. Obtaining updated estimates of deterioration model parameters, given observed data, enables them to be used to predict future health states of interest for the structure, which constitutes a core tenet of condition-based maintenance (CBM). Key to obtaining robust parameter estimates is accounting for different uncertain quantities involved in the model that transforms unobservable quantities to observable ones, which in a Bayesian context is achieved through the likelihood function. This work presents an offline, flexible hierarchical framework to tackle this problem that employs probabilistic reduced order models (ROMs) in the construction of the likelihood function, that enable marginalizing out these uncertain quantities. The approximate likelihood function is then used within a typical Markov Chain Monte Carlo (MCMC) setting for parameter estimation. The framework is demonstrated in applications related to deterioration processes common to ship structures.

Structural acceleration response reconstruction based on BiLSTM network and multi-head attention mechanism

The effectiveness of structural health monitoring often relies on the precise analysis of structural response data, with data completeness directly impacting the performance of monitoring systems. Addressing the common issue of data loss in structural health monitoring systems, this study proposes a Bidirectional Long Short-Term Memory (BiLSTM) network based on a multi-head attention mechanism for the reconstruction of missing structural acceleration responses. Firstly, employing a two-layer BiLSTM network to establish an encoder-decoder framework for extracting spatio-temporal correlation features among sensor data. Subsequently, innovatively embedding a multi-head attention mechanism into the network framework enhances the modeling capability of long-distance input elements and establishes dependencies among different sensors, thereby enabling the network to better capture the global features of input information. The proposed method undergoes validation through a numerical example of a simply supported beam, practical monitoring data on the Hardanger Bridge from the Norwegian University of Science and Technology, and an experimental study of steel frame structure from our laboratory. These validations are compared with alternative response reconstruction models. The experimental results make obvious that the proposed method demonstrates superior accuracy in response reconstruction. Additionally, the suitability of this method for modal identification is validated. Through the utilization of reconstructed responses, the method effectively discerns the structural natural frequencies with accuracy.

Structural Damage Identification Based on Preprocessing Samples Mixed Training of Multi-Channel and Multi-Source Dynamic Response Data

Previous studies have shown that sampling processing and identification methods for neural networks damage identification in multi-channel and multi-source structural dynamic response data exhibit diversity, and the robustness and generalization issues of the model have not been effectively solved. This paper proposes a sample preprocessing technique and mixed training method suitable for multi-channel and multi-source dynamic response data to optimize the current structural damage identification methods based on neural networks. Through multi-dimensional discrete autocorrelation processing and Fourier transform, the preprocessed datasets from multiple sensors with multi-channel can access multi-channel CNN. Furthermore, the measured datasets from the actual structures are mixed with numerical simulation datasets before being used for neural network model training to address the model calibration and the imbalanced sample size under each classification label. The results of extensive model experiments and finite element verification of specimens show that the method performs outstandingly in detecting and identifying damage to simply supported beam structures using multi-channel CNN. The neural network model trained with preprocessed samples exhibits excellent robustness. The model using the mixed training method still performs well in identifying the accuracy of damage location and degree in simply supported beam structures after changing beam section and initial excitation. The method has certain generalization ability in detecting unknown damage in simply supported beam structures.

Maximum likelihood estimation of probabilistically described loads in beam structures

In recent years, focus has been shifted towards predictive maintenance in an effort to improve the reliability of operating structures. Processing structural response data obtained from in-situ sensors during operation can provide added value towards this direction. Structural Health Monitoring (SHM) methods are uniquely suited for this task; however, accounting for the effect of stochastic structural loads is critical for their robustness. In this work, a framework based on Maximum Likelihood Estimation (MLE) is presented, whose goal is to obtain inferences on typically unobservable quantities that describe stochastic structural loading. A structural beam is employed as a demonstrative case study, that is subjected to point loads with stochastic magnitude and application points. The hyperparameters that govern their underlying probability distribution functions (pdf) are the quantities of inferential interest. The inverse (load) identification process is performed using a marginalized MLE objective, where stochastic Monte Carlo (MC) integration is employed to perform the marginalization and Genetic Algorithms (GAs) are used as the optimizer. The Cramer–Rao (CR) lower bound is used to produce 95 % Confidence Intervals (CIs) to quantify estimation uncertainty.

A quantitative comparison study for structural flexibility identification using Accelerometric and computer vision-based vibration data

For civil engineering structures, the dynamics-based structural health monitoring is commonly based on inverse analysis of structural acceleration response data to identify the structural parameters. As the measurement of structural displacement response becomes more feasible and cost-effective thanks to advances in computer vision techniques, a comparison of the displacement and acceleration response data for structural identification has become of considerable interest among researchers. This paper presents a comprehensive analysis and comparison of structural flexibility identification based on displacement versus acceleration response data through uncertainty quantification technique. Firstly, theoretical derivation reveals that the energy spectral density of acceleration response is equal to the fourth power of the frequency of the energy spectral density of displacement response, which affects the distribution of measurement noise across different frequency bands. Secondly, the uncertainty quantification method for modal flexibility from subspace-based system identification is utilized to compare the derivation process of covariance estimation on modal flexibility. Finally, numerical analysis and experimental example are carried out to compare the structural flexibility identification results when using the displacement versus acceleration. It shows that more precise identification results of lower-order modal parameters are obtained when using displacement, which further improves the precision of modal flexibility identification. The displacement is more suitable for identifying the modal flexibility when the measurement noises are at the same level.

Reconstructing Missing Data Using a Bi-LSTM Model Based on VMD and SSA for Structural Health Monitoring

For structural health monitoring (SHM), a complete dataset is crucial for further modal identification analysis and risk warning. Unfortunately, data loss can occur due to sensor failure, transmission system interruption, or hardware failure, which can lead to missing data. Therefore, this study proposes a bidirectional long short-term memory neural network (Bi-LSTM) response recovery method based on variational mode decomposition (VMD) and sparrow search algorithm (SSA) optimization that utilizes the structural response data between multiple sensors and can simultaneously consider temporal and spatial correlations. A dataset containing approximately half a month of monitoring data was collected from a certain project for training, validation, and testing. A publicly available dataset was also referenced to validate the proposed method in this paper. Using the public dataset, under 13 different data loss rates, the VMD + SSA + Bi-LSTM model reduced the RMSE of data reconstruction by an average of 65.01% and 45.35% compared to the Bi-LSTM model and the VMD + Bi-LSTM models, respectively, while the coefficient of determination increased by 62.21% and 11.19%. The data reconstruction method proposed in this paper can accurately reconstruct the variation trends of missing data without the manual optimization of hyperparameters, and the reconstruction results are close to the real data.

Improved pool fire-initiated domino effect assessment in atmospheric tank farms using structural response

Domino effects are severe accident scenarios affecting storage tanks and are often initiated by pool fires. Flame engulfment and heat radiation are the two major sources triggering domino effect. Threshold-based and probit-based methods are widely used to assess the possibility and probability of a secondary accident. These methods are also a part of advanced methods devoted to examining synergic or coupling effects. The current work examines (i) how effective the threshold-based methods are and (ii) how accurate the current time to failure (TTF) estimation models are, which are the basis of probit-based methods. The results suggest that threshold-based methods are not pertinent for the quantitative assessment of domino effect and that significant improvement can be made in the existing TTF prediction models using site-specific structural response data. A new set of equations for TTF estimation using data analytics is proposed. Application to 4,080 pool fire scenarios demonstrates that the newly developed model can improve the TTF prediction performance compared to the existing models (around 22% in terms of R2). In addition, a method has been proposed and validated to correlate time with the failure probability for time-dependent domino effect assessment, which is a limitation of probit-based methods.

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.

Frequency-domain synchronization of structural health monitoring data

Since the advent of structural health monitoring (SHM), vibration analysis has been one of the most popular strategies for yielding modal information of structures, such as eigenfrequencies and mode shapes. As a result of the relatively high sampling rates adopted in vibration analysis, obtaining accurate modal information requires absolute synchronization of structural response data. Despite the advances in sensing technologies and synchronization protocols, synchronization discrepancies in structural response data may occur due to drifting in internal clocks of independent data acquisition units. Particularly in wireless SHM, in which each wireless sensor node essentially operates as an independent data acquisition unit, mitigating clock drifting has been an area of intensive research. This paper addresses the issue of data synchronization from a physics-based perspective, aiming to add an additional synchronization check at the post-sampling stage. Specifically, a frequency-domain synchronization approach is reported, which builds upon the relationship between time lags in structural response data and the cross spectral density phase angles. Validation tests are conducted using simulations of a multi-degree-of-freedom oscillator and data collected from a full-scale road bridge, demonstrating the capability of the approach to estimate time lags between SHM data even in the presence of non-proportional damping, thus overcoming a common limitation of other synchronization methods. The frequency-domain synchronization approach is intended to complement synchronization protocols in extracting accurate modal information.

Evaluation of Pedestrian Comfort for a Footbridge with Hinged Piers

To study the standardized evaluation method of vibration comfort of pedestrian bridges, the effect of the transient response term is ignored based on the generalized single degree of freedom (SDOF) method with reference to the American Institution of Steel Construction inc (AISC 11) specification. The resonance conditions between the crowd and the bridge structure are also considered, and the pedestrian bridge structure is simplified to an SDOF system. Then, the peak acceleration of the structure under crowd excitation is directly calculated using the generalized crowd load, and the comfort evaluation is carried out according to the German EN03 code and the Chinese code. For a new pedestrian bridge with hinged piers, human-induced vibration tests were carried out to obtain the structural acceleration time response data under various crowd load excitation conditions, and the acceleration values calculated with the numerical simulation method, the frequency response function method, the generalized SDOF method, and the proposed method were compared with field measurements. The results show that for the normal excitation condition with a crowd density less than or equal to 0.2 person/m2, the acceleration maximum error for the proposed method is between 4.22% and 13.28%. The error is only 6.35% compared with the finite element simulation results. The method can derive peak acceleration by performing only modal tests, eliminating errors caused by different testers and errors due to deflection measurements. It saves considerable time cost and economic cost, and improves the speed of pedestrian bridge comfort evaluation. Therefore, the method proposed for the first time in this study can accurately assess the human-caused vibration comfort of pedestrian bridges with articulated piers. Although the hinged-pier and column steel pedestrian bridge meets the code requirement of a vertical first-order frequency greater than 3 Hz, it is recommended to add TMD vibration damping measures to the bridge in order to improve pedestrian comfort.

A materials data framework and dataset for elastomeric foam impact mitigating materials

The availability of materials data for impact-mitigating materials has lagged behind applications-based data. For example, data describing on-field helmeted impacts are available, whereas material behaviors for the constituent impact-mitigating materials used in helmet designs lack open datasets. Here, we describe a new FAIR (findable, accessible, interoperable, reusable) data framework with structural and mechanical response data for one example elastic impact protection foam. The continuum-scale behavior of foams emerges from the interplay of polymer properties, internal gas, and geometric structure. This behavior is rate and temperature sensitive, therefore, describing structure-property characteristics requires data collected across several types of instruments. Data included are from structure imaging via micro-computed tomography, finite deformation mechanical measurements from universal test systems with full-field displacement and strain, and visco-thermo-elastic properties from dynamic mechanical analysis. These data facilitate modeling and design efforts in foam mechanics, e.g., homogenization, direct numerical simulation, or phenomenological fitting. The data framework is implemented using data services and software from the Materials Data Facility of the Center for Hierarchical Materials Design.

Integration of locating baseline-free nonlinear elements and identifying model-free nonlinear restoring forces in structures

Structural local nonlinearity often occurs when a structure is subject to strong excitation. Therefore, the identification of structural nonlinearity localizations and the characteristics of nonlinear restoring forces is of great significance in ensuring structural safety and reliability. Among the existing nonlinearity identification methods, locating structural nonlinear elements under seismic excitations is still a challenging problem and identifying model-free nonlinear restoring forces requires prior knowledge of nonlinearity position. To overcome these limitations, a novel two-step nonlinearity localization and identification algorithm is proposed in this paper. First, by considering the wavelet energy distribution in the nonlinear element become different from those of other linear elements, relative wavelet entropy which is able to quantify the difference or similarity between two wavelet energy distributions is employed to localize structural nonlinear elements based on the sparsity of structural nonlinearity. In the localization process, structural response data of the baseline linear structure are not required. Then, nonlinear restoring forces from nonlinear elements are regarded as ‘unknown virtual forces’ to the underlying linear structure. The generalized Kalman filter with unknown input, which was recently proposed by the authors, is used to identify the ‘unknown virtual forces’ and reconstruct the characteristics of nonlinear restoring forces without the models of nonlinear restoring forces. The proposed method is verified by numerical simulations of different types of nonlinear elements in different structures. Furthermore, it is tested by locating the MR damper and identifying its model-free nonlinear restoring forces in a laboratory four-story shear-type frame.

EESD special issue: AI and data‐driven methods in earthquake engineering – (Part 1)

EESD special issue: AI and data‐driven methods in earthquake engineering – (Part 1)

Relational database for building strong motion recordings used for seismic impact assessments

Historical data play a primary role in reconstructing building seismic responses and assessing damage in near-real time. While these types of data sets exist, they are often fragmented, difficult to access, and require significant manual manipulation on the part of the user to obtain useful information. This article introduces a relational database that comprises historical data from 216 buildings subjected to [Formula: see text] earthquakes, spanning a 36-year period in California. It includes comprehensive information about the events and accelerometer-equipped buildings, parameters that are often used in post-earthquake impact assessments, and time-series structural response data recorded during real earthquakes. The database is designed to facilitate incremental updating as new events occur, and the associated data becomes available. It is also paired with a Python-based tool that reduces the barrier to user access with a few simple inputs. The long-term goal is to expand the database to include additional seismic events and spur the development of similar single- or multi-hazard repositories.

Mechanism of Phase-Locked Ice Crushing against Offshore Structures

This paper addresses a detailed analysis of the ice–structure interaction process of the phase-locked ice crushing (PLC) against offshore structures. Directly measured ice load, structure response data, and in situ observation from the field measurements on the Molikpaq lighthouse and jacket platform were used in the study. This paper summarizes a new ductile damage-collapse (DDC) failure mechanism for the PLC process. The DDC mechanism shows that the ice failure is a discrete ductile crushing process rather than a ductile–brittle transition process. The analysis identifies that the ice has a failure length in PLC and this failure length plays an important role in understanding the interaction. It reveals that PLC can occur on most vertical-sided offshore structures when the velocity of the ice sheet falls within the range of the failure length divided by the natural period of the structure. This paper proposes that this relationship between ice failure length and the natural period of the structure can be used as one of the PLC occurrence conditions. The DDC failure mechanism provides a basis for another technical route to solve the PLC problem.