Strain Monitoring Data Research Articles

A novel method to determine the critical buckling load for plates from load versus in-plane displacement diagram

Based on the literature, the load vs. in-plane displacement diagram (P-u curve) is not sufficiently indicative to provide a reliable determination of the critical buckling load, Pcr. This attitude is rooted in three issues. First, the P-u curve often exhibits very gradual slope changes, second, it concerns unavoidable graph fluctuations caused by imperfections in specimens or boundary conditions, and third, the P-u curve behavior during the buckling phenomenon is not fully understood. In the present work, a narrative review is conducted to gain a better understanding of the location of Pcr on the P-u curve. Accordingly, a novel method is introduced to correctly extract Pcr from the load vs. in-plane displacement diagram as the most accessible buckling test output, compared to out-of-plane displacement and strain monitoring data, which require additional equipment. The proposed method is highly sensitive to changes in the slope of the graph while significantly reducing the adverse effects of undesired fluctuations. This method is applied to several experimental buckling data for composite laminates as well as sandwich panels, and its reliability and accuracy are confirmed by comparison with other well-known methods.

Modeling and Data Mining Analysis for Long-Term Temperature-Stress-Strain Monitoring Data of a Concrete Gravity Dam

The safety condition of concrete gravity dams is influenced by multiple factors, and assessing their safety solely based on a single factor is difficult to comprehensively evaluate. Therefore, this paper proposes a comprehensive modeling and analysis approach to assess dam safety by considering long-term temperature, stress, and strain monitoring data of actual concrete gravity dams. Firstly, the K-means clustering algorithm is utilized to classify the data. Then, the study area of the dam is meshed and three indicator evaluation values for all the elements are calculated. The other elements’ evaluation values can be obtained by the Inverse Distance Weighting (IDW) method. Finally, the analytic hierarchy process extended by the D numbers preference relation (D-AHP) method is applied to compute the weights of temperature, stress, and strain and evaluate the dam’s safety comprehensively. The effectiveness of this method is validated through application to specific engineering cases. The results demonstrate that compared to assessing methods considering only single factors, the comprehensive evaluation method proposed in this paper can more comprehensively and accurately reflect the actual safety condition of concrete gravity dams, providing important references for engineering decision-making.

ALSTNet: Autoencoder fused long‐ and short‐term time‐series network for the prediction of tunnel structure

AbstractIt is crucial to predict future mechanical behaviors for the prevention of structural disasters. Especially for underground construction, the structural mechanical behaviors are affected by multiple internal and external factors due to the complex conditions. Given that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models, this study proposed an improved prediction model through the autoencoder fused long‐ and short‐term time‐series network driven by the mass number of monitoring data. Then, the proposed model was formalized on multiple time series of strain monitoring data. Also, the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction model. As the results indicate, the proposed model shows obvious superiority in predicting the future mechanical behaviors of structures. As a case study, the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future.

A method for suspenders tension identification of bridges based on the spatio‐temporal correlation between the girder strain and suspenders tension

AbstractIn the actual structural health monitoring system of suspension bridges, only part of suspenders tension can be monitored, but not all the suspenders tension can be obtained. To solve this problem, a method for suspenders tension identification of bridges based on the spatio‐temporal correlation between the girder strain and suspenders tension is proposed. By using actual monitoring data of vehicle loads, a spatio‐temporal correlation model of the girder strain and tension forces of all suspenders is constructed based on the combined application of stacked denoising autoencoder and convolutional neural networks‐long short‐term memory model, so as to realize the preliminary identification of suspenders tension. On this basis, by using the actual monitoring data of suspenders tension and the strain monitoring data obtained through the distributed optical fiber sensors, the delicate identification of tension forces of all suspenders is realized based on the error interpolation of preliminary identification results. The results of the example bridge show that the method in this paper can effectively identify tension forces of all suspenders of the suspension bridge, and identification results are more accurate than the method using only the monitoring data of suspenders.

Improvement of axial deformation prediction in high-rise buildings with field monitoring and adaptive unscented Kalman filter

The deformation of vertical reinforced concrete members in high-rise buildings during their construction is constantly changing due to the complexity of the deformation mechanism, as well as the uncertainties that arise during the construction process; in some cases, accurate estimation of the vertical displacement is crucial. Based on on-site strain monitoring data and an adaptive unscented Kalman filter, this paper establishes a state equation for the axial deformation process. The elastic, inelastic and temperature deformation of concrete during the entire construction process are reasonably considered. Deformation is predicted using both the traditional Kalman filter and unscented Kalman filter, both of which achieved satisfactory results. But the unscented Kalman filter can improve the prediction accuracy and robustness by utilizing unscented transformations. Anomaly detection is integrated into every inference step to detect abrupt changes in external conditions and dynamically adjust calculation parameters, thereby reducing the dependency of the prediction results on input parameters.

Full-field deformation reconstruction for large-scale cryogenic composite tanks with limited strain monitoring data

Online deformation monitoring, while of paramount importance in safety evaluation for aerospace composite tanks, is highly challenging due to the complex strain distributions in the composite tank and the strict restrictions of sensor placement. In this study, full-field deformation of large-scale cryogenic composite tanks were reconstructed under thermo-mechanical coupling conditions. In essence, the inner surface strains in the junction area of the head and cylindrical shell of the tank, defined as the ‘H-C portion’, was derived theoretically based on outer-surface strain measurement. The inverse finite element method (iFEM) was then applied using the measured and derived strains to reconstruct the full-field deformed shape of the tank. A systematic and efficient parametric discussion was conducted using an orthotropic model equivalent with composite laminated models with different lay-ups. The influences of various factors relevant to the material and geometries of the tank on the accuracy of deformation reconstruction were unveiled. Finally, a numerical experiment was carried out to reconstruct the full-field deformation of a large-scale aerospace composite tank with a specific lay-up, where limited strain data analogous to those sparely measured using distributed optical fiber sensors was used. It was found that the sensor placement strategy markedly affects the accuracy of deformation reconstruction.

A fatigue crack quantification model for metallic structure based on strain monitoring data

Obtaining the real-time fatigue crack length of a metallic structure is the prerequisite of the fatigue life monitoring and residual life estimation for an aircraft. This paper proposed a metallic structure's fatigue crack prediction model using strain monitoring data based on deep learning method. A cycle consistent adversarial network was developed to map the strain monitoring data from experimental measurement with those from finite element modeling. A crack size classification model and a crack length quantification model were proposed to classify the crack size range and identify the exact crack length, respectively. The proposed model was applied to predict the fatigue crack growth in centeral hole metallic plates subjected to random loading spectrum. The results showed that the prediction is effective and accurate.

FBG strain monitoring data denoising in wind turbine blades based on parameter-optimized variational mode decomposition method

Herein, we aimed to solve the problem of difficulty in filtering the noise components in the monitoring of strain on wind turbine blades using fiber bragg grating, a denoising method based on parameter-optimized variational mode decomposition (VMD) is proposed. This method uses the minimum envelope entropy as the fitness function and the slime mould algorithm for self-adaptive optimization to find the optimal combination of modal decomposition components K and the quadratic penalty factor α of VMD. The optimized VMD was used to decompose the strain data of wind turbine blades over time into K intrinsic mode components, and the noise mode was removed using the sample entropy to obtain the effective signal. The proposed method was compared to ensemble empirical mode decomposition and complementary ensemble empirical mode decomposition using the simulated signals and engineering data. The experimental results show that the proposed method can effectively remove noise from the strain data of wind turbine blades and has better denoising performance than the other two methods, which provides a reliable basis for analyzing the strain data of wind turbine blades.

Fatigue Life Evaluation of Wind Turbine Tower Based on Measured Data

Fatigue life is a crucial design factor which dictates the safe operation of the wind turbines, but it is influenced by uncertain factors such as environmental loads, analytical models, material properties, and manufacturing methods. In this study, a 1.5 MW wind turbine was monitored in operation to understand the fatigue mechanism and enhance wind turbine design. The influence of different operating conditions on fatigue damage was analyzed by correlating strain monitoring data with supervisory control and data acquisition (SCADA) data. Furthermore, a fatigue evaluation method based on measured strain data was proposed. Fatigue damage increases with the increase of wind and rotation speed. More than 50% of the damage occurred at the rated rotation speed state, the corresponding wind speed was greater than the rated wind speed and the pitch control system was active. The findings of this study provide insights for investigating the real fatigue state of similar wind turbine towers and improving the return on investment by closely estimating their service life.

A Cross-Diagnosis Method for Determining the Structural Condition of a Long-Span Suspension Bridge Based on the Spatial Windows of Distributed Strain Data

Using structural health monitoring (SHM) techniques, Brillouin optical time-domain analysis (BOTDA) sensors can be mounted along the main box girder entire length of a long-span suspension bridge, and the high-density measured points strain monitoring data can be obtained. However, insufficient research has been conducted on accurately diagnosing the structural condition of a long-span suspension bridge by using the abovementioned strain monitoring data. To address this issue, a cross-diagnosis method that determines the structural condition of long-span suspension bridges based on the distributed strain data spatial window is proposed in this study. First, the distributed strain data spatial window based on a long-span suspension bridge structural symmetry is defined. Then, a method that divides the distributed strain data of the bridge main box girder into different spatial windows using mutual information between the strain data from BOTDA sensors is presented. The special symmetry of the environmental temperature effect on the spatial window structural performance is carried out to separate the temperature effect from the strain monitoring data; this process can effectively reduce the interference of ambient temperature on the results of the structural condition diagnosis. Second, using a convolutional neural network, a diagnosis index of the structural condition is generated by using the correlation model between the high-density measured points and the distributed strain data belonging to one whole spatial window. Regarding one spatial window, the proposed diagnosis index can effectively reflect the variation in the distributed strain correlation model caused by the damaged condition of the long-span suspension bridge to achieve cross-diagnosis of the structural condition of the bridge. Finally, the effectiveness of the proposed method is demonstrated through a numerical simulation using strain monitoring data obtained from a real bridge.

A New Method for Separating Temperature Effect of Bridge Strain Monitoring

Temperature has a significant influence on bridge strain monitoring data. To improve the accuracy of temperature effect separation in strain monitoring data, this paper proposes a temperature effect separation method comprising variational nonlinear chirp mode decomposition (VNCMD), principal component analysis (PCA) and blind source separation. Firstly, VNCMD was used to decompose the monitoring data of strain and temperature, and the intrinsic mode functions (IMF) of strain and temperature signals were obtained. Secondly, PCA was used to reduce the dimension of IMF, and the false components were eliminated to select the optimal components. After reducing the dimension, the components were used as the input of fast independent component analysis model for blind source separation. Finally, the feasibility and accuracy of the method was verified via the signal-to-noise ratio (SNR) in the simulated signal, and the separation results were evaluated using the Pearson correlation coefficient between the strain component and the corresponding temperature component in real bridge monitoring data. The proposed method performed better than the empirical mode decomposition (EMD) method. The signal-to-noise ratio (SNR) of VNCMD improved 51.80% for daily temperature difference effect and 32.41% for annual temperature difference effect in the numerical study, respectively; the correlation coefficients of VNCMD improved 52.90% for daily temperature difference effect and 4.26% for annual temperature difference effect in practical verification, respectively.

Permanent Deformation Monitoring and Remaining Life Prediction of Asphalt Pavement Combining Full-Scale Accelerated Pavement Testing and FEM

Permanent deformation (rutting) is one of the typical failure modes of asphalt pavement with a semirigid base in high-temperature areas. To address issues of pavement structure deformation and remaining life prediction, a method of combining accelerated pavement testing and finite element (FE) simulation was proposed. A modified Burgers model considering creep parameter damage was obtained using strain monitoring data and laboratory test values, which was performed in the FE model to analyze rutting development. The results showed that high temperature and heavy axle loads greatly influenced rutting development: the tested road was loaded 360,000 times at high temperature, contributing 50% to accumulated rutting; rutting increased by 90.7% under the condition of 50% overload under high-temperature seasons from the simulated results. Then, the ratio of the bulge area to the depression area (KR) was proposed to define the rutting deformation mechanism. The KR analysis results illustrated that rutting development was a transition process from compaction rutting to flow rutting. Finally, the relationship between the action times of axle load in the tested and in-service roads was established based on the rutting equivalence principle. When the action times of axle load reach approximately 25 million times (insufficient remaining life) for the investigated Hu-Ning highway, necessary maintenance measures should be taken to treat rutting deformation.

A Nonparametric Bayesian Approach for Bridge Reliability Assessment Using Structural Health Monitoring Data

Integrating structural health monitoring (SHM) data into reliability assessment has increasingly been practiced in the condition evaluation of in-service bridges over the past decade. The selection of probability distribution models for load- and resistance-related random variables is a prerequisite for monitoring-based reliability assessment. However, the underlying probabilistic assumptions of the used models could be restrictive and unverifiable especially when dealing with real-world heterogeneous monitoring data, weakening the confidence on the estimated reliability index. This study aims to develop a nonparametric Bayesian model with the Dirichlet process prior for bridge reliability assessment, where the model order constraint can be released such that the complexity of the model adapts to the observed data. Reliability analysis via the nonparametric Bayesian model allows the aleatory uncertainty and the epistemic uncertainty arising from monitoring data to be concurrently accounted for in the formulated reliability index. A numerical example is presented to verify the effectiveness of the nonparametric Bayesian model for dealing with multimodal data. The feasibility of the proposed approach for reliability assessment is then demonstrated with one-year strain monitoring data acquired from a large-scale bridge instrumented with the SHM system.

Damage identification method on shield tunnel based on PLSR and equivalent damage analysis

Damage identification plays a critical role in the maintenance of the shield tunnel structure. This study proposes a damage identification method for shield tunnel lining based on back analysis, which lays emphasis on the mechanical responses of the structure. The connection between mechanical parameters and the structural response is extracted by the finite element model. With the employment of Partial Least Squares Regression (PLSR), the load on shield tunnel lining can be determined by the monitoring data, the variation of which before and after structural damage, regarded as damage index, is used to identify the damage position. The equivalent damage load is defined to establish the equations of the load, deformation and damage, which solves the problems induced by the randomness of damage position. To clarify the validation, the method is verified by previous experimental results. Involving the structural damage of various positions and intensities, this method is applied with the numerical models based on circular shield tunnel. The parametric study shows the accuracy of the method under different damage conditions. The effects of monitoring data of radial displacement and circumferential strain were also compared. As a result, the primary input from the circumferential strain has better sensitivity than that of the radial displacement.

A method for convergence monitoring considering the flattening effect in a shield tunnel with BOTDA sensors

The convergence deformation of shield tunnels, which is induced by the combination of the local circumferential load and the flattening effect under longitudinal differential settlement, is difficult to comprehensively monitor with point sensing technology. To address this problem, a method for convergence monitoring considering the flattening effect in shield tunnels is proposed based on distributed Brillouin optical time-domain analysis (BOTDA) technology. First, the convergence deformation under sectional circumferential pressure is explored based on the conjugated beam method; second, the additional influence of the flattening effect, including extrusion and shear effects, on settlement is analyzed using the force method; third, considering the actual circumferential layout of the distributed optical fiber sensors, the multisection convergence is determined from the spatial correlations of the cross-sectional strain monitoring data by means of an artificial neural network (ANN); then, finally, using the time-series monitoring data, the trend of the convergence deformation can be predicted. In this study, both numerical simulation and practical application are carried out to validate the effectiveness of the proposed method, which is compared with the existing method without consideration of the flattening effect. The results indicate that the proposed method enables high accuracy and robustness in convergence monitoring, thus providing a referential basis for further tunnel operation and maintenance.

Final Setting Judgment and Safety Evaluation of Mass Concrete Based on Strain Monitoring

A judgment method of the final setting time of mass concrete based on strain monitoring and the selection criterion of the starting point of effective deformation are proposed, and the safety of the concrete structure is evaluated based on this. Firstly, the strain monitoring theory of mass concrete is analyzed to clarify the temperature effect component and monitoring mechanism of an early strain of concrete. Then, taking the mass concrete plate of an engineering project as the research object combined with the mechanical analysis, the early strain monitoring data are segmented, and the relationship between the strain response and the temperature in different stages is fitted based on robust regression, so as to explore the correlation between strain and temperature in each stage and better explain the setting process of the concrete structure. Finally, taking the final setting time as the starting point of the effective strain, the safety of the concrete structure is further evaluated. The results show that there is a clear linear relationship between the strain response and temperature, and a high negative correlation between strain and temperature after the final setting. The time point when the frequency modulus corresponding to the strain enters a steady change and decreases with the increase of temperature is taken as the final setting time, which conforms to the concrete condensation mechanism and the temperature effect of early strain monitoring.

Steady-State Data Baseline Model for Nonstationary Monitoring Data of Urban Girder Bridges

In bridge structural health monitoring systems, an accurate baseline model is particularly important for identifying subsequent structural damage. Environmental and operational loads cause nonstationarity in the strain monitoring data of urban girder bridges. Such nonstationary monitoring data can mask damage and reduce the accuracy of the established baseline model. To address this problem, a steady-state data baseline model for bridges is proposed. First, for observable effects such as ambient temperature, a directional projection decoupling method for strain monitoring data is proposed, which can reduce the nonstationary effect of ambient temperature, and the effectiveness of this method is proven using equations. Second, for unobservable effects such as traffic load, a k-means clustering method for steady state of traffic loads is proposed; using this method, which can divide the steady and nonsteady states of traffic loads and reduce the nonstationary effect of traffic loads on strain monitoring data, a steady-state baseline model is established. Finally, the effectiveness of the steady-state baseline model is verified using an actual bridge. The results show that the proposed baseline model can reduce the error caused by nonstationary effects, improve the modelling accuracy, and provide useful information for subsequent damage identification.

Damage Detection of Steel Truss Bridges Based on Gaussian Bayesian Networks

This paper proposes the use of Gaussian Bayesian networks (GBNs) for damage detection of steel truss bridges by using the strain monitoring data. Based on the proposed damage detection procedure, a three-layer GBN model is first constructed based on the load factors, structural deflections, and the stress measurements of steel truss bridges. More specifically, the load factors of the structures are defined as the first-layer network nodes, structural deflections are considered as the second-layer network nodes, and the third-layer nodes of the GBN model are built based on the stress data of the truss elements. To achieve the training for the constructed GBN model, the finite element analysis of the bridge structures under the different load factors is performed. Then, the training of the network is performing by using the maximum likelihood estimation approach, and the optimized network parameters are obtained. Based on the trained network model, the measured load factors and the corresponding stress monitoring data of a limited number of truss elements are considered as input, and the stress measurements of all truss elements of bridges can be accurately estimated by searching the optimized topological information among network nodes. For a steel truss bridge, when the truss elements are damaged, the stress states of the damaged elements will be changed. Therefore, a damage index is further constructed for damage detection of steel truss bridges based on the changed stress states of those damaged elements. To verify the feasible and effective use of the proposed damage detection approach, an 80 m steel truss bridge with various damage cases was conducted as numerical simulations, and the investigation results show that the trained GBN can be accurately used for stress prediction of steel truss bridges, and the proposed damage index with the estimated stress data can be further applied for structural damage localization and quantification with a better accuracy. Furthermore, the results also suggest that the proposed damage detection procedure is accurate and reliable for steel truss bridges under vehicle loads.

A novel Bayesian blind source separation approach for extracting non-stationary and discontinuous components from structural health monitoring data

We propose a new method to explore the blind source separation (BSS) of heterogeneous structural health monitoring (SHM) data containing non-stationary and temporally discontinuous components in the framework of Bayesian inference. Specifically, Gaussian process (GP) with a specially defined time-varying kernel function is introduced to encode the prior information about the unknown sources. The time-varying kernel function encompasses a state indicator hyperparameter which enables the expressivity of intermittently active and inactive source signals and incorporates smooth switch and composite kernels catering for the interpretation of complex sources. With the likelihood function elicited from monitoring data alongside with the priors for sources, mixing matrix and noise, the unknown sources are extracted from the posterior distributions formalized by Bayes’ theorem, where a sequential Metropolis − Hasting sampling algorithm is adopted to numerically compute the source statistics in a high-dimensional realm. Numerical simulations demonstrate that the proposed method performs satisfactorily in (i) extracting intermittently active and inactive (abruptly appearing and disappearing) non-stationary source signals, (ii) estimating inconsistent levels of noise contaminated in different sensors, and (iii) handling unknown number of sources. In the verification using real-world strain monitoring data collected from the Tsing Ma Bridge carrying both highway and railway traffic, it is shown that the proposed method well extracts the railway-induced non-stationary strain component with intermittence, and the structural condition index formulated by the Bayesian dynamic linear model (BDLM) is more robust when adopting the separated highway-induced strain data than using the combined railway- and highway-induced strain data. The results obtained by the proposed method are also compared with those by the two most common BSS techniques — the independent component analysis (ICA) and second-order statistics (SOS) methods.

Time-Domain Inversion Method of Impact Loads Based on Strain Monitoring Data

A helicopter deck is the main load-bearing component under the emergency landing conditions for helicopters. However, it is generally difficult to directly obtain the landing load from measurements due to the high randomness of the landing position. As the main design load of the helicopter deck, the emergency landing load is very important to its structural design. A large design load value leads to an overly conservative structural design and affects the control of the ship’s weight and center of gravity, while a small design load may lead to a lack of security and affect the safety of the helicopter and the ship. As a result, the time domain inversion method, which is based on strain monitoring data, is an important and effective method for obtaining the helicopter emergency landing load. In this study, a grillage model experiment was conducted to study the time domain inversion method. The helicopter impact load was simulated by falling body impact, and the impact load history and structural strain response were recorded by sensors. The grillage model impact load was calculated with different inversion methods, including the direct inverse, truncated singular value decomposition (TSVD), and Tikhonov regularization methods. The solution accuracy of different methods and number of sensors needed were compared. The results demonstrated that the Tikhonov regularization method based on four measurement points along with the L-curve determination criterion showed a better performance for capturing the impact load time history features.