Structural Strain Responses Research Articles

Shaking-table tests and numerical simulations study of subway stations in loess region

The loess region in China, known for its high seismic activity and the extreme vulnerability of loess to earthquakes, presents a considerable threat to the seismic safety of underground structures, including subway stations. With the Xi'an Aerospace City subway station as the background, a subway station model was designed, and shaking table tests were conducted under various conditions to analyze the seismic damage and dynamic responses in this study. The study reveals the seismic vulnerable areas and dynamic response patterns of the subway station under different spectral earthquake actions. Simultaneously, a three-dimensional numerical modeling analysis accounting for soil-structure interaction, was conducted to explore the displacement response of the structure and soil. Results indicate a noticeable amplification effect on the acceleration of both the structure and the soil under seismic motion. The structure exhibits a suppressive effect on the acceleration amplification of the surrounding soil, diminishing with increasing seismic motion intensity. Different depths show varying differences in acceleration responses between the structure and adjacent soil. Structural strain responses and damage phenomena suggest that the central column is the seismic vulnerable area of the subway station. Additionally, the strain distribution of the structure exhibits a distinct spatial effect. The relative horizontal displacement between the structure and the soil follows certain patterns in the depth direction, with the structure experiencing smaller relative horizontal displacement than the soil under seismic action due to the constraining effect of surrounding soil on its displacement response. The experimental conclusions provide valuable reference points for the seismic design and analysis of subway station structures in loess regions.

Model Updating and Assessment using Axial Strains for a Statically Determinate Truss Bridge

The safety of aging structures over the course of their service life is given by structural reliability. Additionally, there is a lack of techniques available for processing raw structural health monitoring (SHM) data in a way that makes it possible to validate and/or update structural reliability. Along with the diagnosis of structural anomaly using SHM techniques, the prognosis of structural condition is also an important aspect. Field-measured strain responses can be used as an indication of structural safety and provide information regarding the load-carrying capacity of the structure. Reliability assessment can be conducted only if a more accurate digital analogue is obtained for the actual bridge and when the structural responses can be predicted accurately. This paper validates a digital model of a real-life railway truss bridge. Pamban bridge is a century-old railway bridge having one track and four internally statically determinate truss leaves. Electrical strain gauges are instrumented to measure the axial strain responses in 84 out of 92 members of the bridge. Strain responses of 1072 train movements that traversed the bridge from 1st January 2021 to 14th July 2021 are recorded, and root mean square error (RMSE) between these field-measured strain responses and analytically computed strain responses from the digital model are estimated. The stationary trend of the time history of RMSE values depicts constant structural reliability. A technique is also proposed to forecast structural strain responses, which is validated with field-measured strain responses.

Data-driven identification of structural damage under unknown seismic excitations using the energy integrals of strain signals transformed from transmissibility functions

In recent years, the methodology based on the energy variations of structural dynamic strain responses has been developed. Although it is an efficient data-driven structural damage identification approach, it can only be used for structures under impulse or known excitations. To overcome this limitation, an improved method is proposed in this paper for the rapid damage identification of beam-like structures using structural dynamic strain responses under unknown seismic excitations. The influence of different excitations to an intact and damaged structure, in which the intact structure is under ambient excitations and structural damage is caused by unknown seismic excitations, is eliminated via the transmissibility functions (TFs) of measured structural strain responses. Contrary to the previous TFs of structural acceleration or displacement responses, TFs of structural strains is more sensitive to local damage in beam-like structures. Moreover, power spectral density transmissibility functions (PSDTFs) are used instead of the traditional Fourier transform TFs. Then, through the inverse Fourier transformation in the frequency domain, the PSDTFs are returned to the time-domain signals to avoid the problem of selecting the proper frequency band in previous studies on identifying structural damage based on TFs in the frequency domain. Finally, structural damage can be identified based on the energy variations of the time-domain strain signals from the intact and damaged structures. Numerical examples of structural damage identification of a seven-storey planar frame and a cable-stayed bridge girder under unknown seismic excitations are provided to demonstrate the effectiveness and robustness of the improved method. Moreover, structural damage identification of a laboratory cable-stayed bridge tower model subjected to shaking table tests is performed to validate the good performance of the proposed method.

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.

Bayesian dynamic forecasting of structural strain response using structural health monitoring data

Bayesian dynamic forecasting of structural strain response using structural health monitoring data

Damage identification in a plate structure based on a cross-direction strain measurement method

Structural strain responses are extensively applied in the field of structural health monitoring (SHM); however, previous studies have focused on the damage identification methods based on strain responses. This paper focuses on measurement technology and proposes a new strain measurement method. Four strain gauges are set in a cross direction on the surface of the structure. Then, the Wheatstone full-bridge connection is used to measure the combined strain of the four strain gauges. The peak picking method is applied to estimate the strain modal shape and frequency of the structure under ambient excitation. The strain mode difference is used as a damage identification index. Finally, clamped plate experiments, including single-damage, multi-damage and damage distance experiments, are successfully implemented. Compared with traditional half-bridge strain measurements, the results show that the cross-direction strain measurement method is more sensitive to minor damage and has higher identification accuracy.

A Novel Fatigue Crack Monitoring Method Based on Nonlinear Dynamic Characteristics of Strain

This paper presents a novel fatigue crack monitoring method for steel specimens based on the smoothness priors method (SPM) and Tsallis entropy (TE) of strain measurements. The aim of the study is to detect initiation of a crack in steel specimens and subsequently to monitor its propagation under the fatigue load, based on real-time strain measurements. The nonlinear dynamic response of the structure was exploited since it degrades due to the initiation and subsequent propagation of the crack under the external dynamic excitation. The proposed method was experimentally validated. Here, the SPM is applied to decomposing the structural strain response into a nearly-stationary (NS) component and a low frequency aperiodic trend (LFAT) component. Features associated with crack initiation can be extracted from the NS component. The LFAT component, on the other hand, can be used to identify crack propagation. To tackle the singularity of the structural responses associated with a crack, the TE of the NS component was used in detection and monitoring of the crack in the steel specimen. Two other techniques, namely, acoustic emission (AE) sensor and crack opening displacement (COD) gauge were used for the purpose of calibration and comparison. The results show remarkable resemblance in terms of crack initiation and propagation identification exhibited by all three types of sensors, highlighting the potential of the proposed method for real-time detection and subsequent monitoring of crack propagation in steel structures.

A damage identification method for a thin plate structure based on PVDF sensors and strain mode

Damage identification methods for engineering structures based on vibration parameters have the advantages of easy detection and high precision; however, structural strain information is more sensitive to structural damage than displacement information. Traditional resistance strain sensors have low accuracy and poor stability when measuring structural strains. Therefore, this paper uses a highly sensitive polyvinylidene fluoride dynamic strain sensor to identify structural damage in a thin plate. The polyvinylidene fluoride sensor is used to obtain structural strain response information, and structural modal parameters are identified using operational modal identification methods based on the natural excitation technique and the eigensystem realization algorithm. This paper uses a damage index based on mode shape and flexibility. A new damage index based on the LU decomposition of the flexibility matrix is used to identify the damage of the thin plate structure. The effectiveness of the modal identification methods and the new damage index is validated via an elastic thin plate experiment. The results show that the modal identification method and the new damage index proposed in this paper can identify damage in a thin plate structure. Sensor comparison experiments also show that compared with a resistance strain sensor, the polyvinylidene fluoride sensor has higher damage sensitivity, better damage recognition and the ability to recognize farther from the sensor.

Identification of moving train loads on railway bridgebased on strain monitoring

Moving train load parameters, including train speed, axle spacing, gross train weight and axle weights, are identified based on strain-monitoring data. In this paper, according to influence line theory, the classic moving force identification method is enhanced to handle time-varying velocity of the train. First, the moments that the axles move through a set of fixed points are identified from a series of pulses extracted from the second derivative of the structural strain response. Subsequently, the train speed and axle spacing are identified. In addition, based on the fact that the integral area of the structural strain response is a constant under a unit force at a unit speed, the gross train weight can be obtained from the integral area of the measured strain response. Meanwhile, the corrected second derivative peak values, in which the effect of time-varying velocity is eliminated, are selected to distribute the gross train weight. Hence the axle weights could be identified. Afterwards, numerical simulations are employed to verify the proposed method and investigate the effect of the sampling frequency on the identification accuracy. Eventually, the method is verified using the real-time strain data of a continuous steel truss railway bridge. Results show that train speed, axle spacing and gross train weight can be accurately identified in the time domain. However, only the approximate values of the axle weights could be obtained with the updated method. The identified results can provide reliable reference for determining fatigue deterioration and predicting the remaining service life of railway bridges.

Nonlinear interaction between underwater explosion bubble and structure based on fully coupled model

The interaction between an underwater explosion bubble and an elastic-plastic structure is a complex transient process, accompanying violent bubble collapsing, jet impact, penetration through the bubble, and large structural deformation. In the present study, the bubble dynamics are modeled using the boundary element method and the nonlinear transient structural response is modeled using the explicit finite element method. A new fully coupled 3D model is established through coupling the equations for the state variables of the fluid and structure and solving them as a set of coupled linear algebra equations. Based on the acceleration potential theory, the mutual dependence between the hydrodynamic load and the structural motion is decoupled. The pressure distribution in the flow field is calculated with the Bernoulli equation, where the partial derivative of the velocity potential in time is calculated using the boundary integral method to avoid numerical instabilities. To validate the present fully coupled model, the experiments of small-scale underwater explosion near a stiffened plate are carried out. High-speed imaging is used to capture the bubble behaviors and strain gauges are used to measure the strain response. The numerical results correspond well with the experimental data, in terms of bubble shapes and structural strain response. By both the loosely coupled model and the fully coupled model, the interaction between a bubble and a hollow spherical shell is studied. The bubble patterns vary with different parameters. When the fully coupled model and the loosely coupled model are advanced with the same time step, the error caused by the loosely coupled model becomes larger with the coupling effect becoming stronger. The fully coupled model is more stable than the loosely coupled model. Besides, the influences of the internal fluid on the dynamic response of the spherical shell are studied. At last, the case that the bubble interacts with an air-backed stiffened plate is simulated. The associated interesting physical phenomenon is obtained and expounded.

Development of High-Sensitivity and Low-Cost Electroluminescent Strain Sensor for Structural Health Monitoring

Visualized strain/stress sensing is receiving interests in structural health monitoring area, but former approaches cannot be effectively applied to civil infrastructure monitoring, because of limited sensitivity to low-level response and/or limited response to civil structures for both static and dynamic strain/stress. This paper presents a high-sensitivity and low-cost method to visualize the strain by employing the precisely controllable Wheatstone bridge and monochrome electroluminance (EL) technology. A sensor prototype is developed to convert the structural strain responses into visible brightness change with brightness change mode (BCM) and to color change with color alternation mode (CAM). In the BCM, the brightness can be changed according to the strain change. In the CAM, two different-color EL panels are used to light on to indicate the necessity for the professional structural health evaluation. Brightness linearity evaluation results show that the BCM is suitable for both static and dynamic low-level strain visualization. Bill of materials shows the cost of the prototype is as low as U.S. $46 including the EL panels.

Structural identification of short/middle span bridges by rapid impact testing: theory and verification

A structural strain flexibility identification method by processing the multiple-reference impact testing data is proposed. First, a kind of novel long-gauge fiber optic sensor is developed for structural macro-strain monitoring. Second, the multiple-reference impact testing technology is employed, during which both the impacting force and structural strain responses are measured. The impact testing technology has unique merit because it is able to extract exact structural frequency response functions (FRFs), while other test methods, for instance ambient tests, can only output the FRFs with scaled magnitudes. Most importantly, the originality of the article is that a method of identifying the structural strain flexibility characteristic from the impact test data has been proposed, which is useful for structural static strain prediction and capacity evaluation. Examples of a six meter simple supported beam and a multiple-span continuous beam bridge have successfully verified the effectiveness of the proposed method.

Synthesis of In-Flight Strains Using Flight Parameters for a Fighter Aircraft

This paper describes the methodology for synthesis of structural strains during flight using recorded flight parameters of a fighter aircraft. This technique can be applied to regenerate data in the event of data loss due to faults in transmission or recording or due to faulty strain gauges. In such a case, flight parameters recorded during the flight can be employed to synthesize the strain response at the structural locations for which the data loss is observed. Artificial neural networks are used in this study to model and synthesize structural strain response using a prescribed set of flight parameters. The approach is demonstrated through the examples of typical wing root and fin root fittings of a fighter aircraft. The neural network model is developed using available data of an instrumented aircraft. The results provide not only the proof-of-concept but also indicate the acceptable efficacy of the model in terms of the accuracy of predictions. Accuracy of predictions is used to identify a more suitable set of flight parameters for efficient implementation of this model. Predicted strain responses for a number of flights are shown to be within 7% of the measured data for the entire flight duration.

빠른 조류 환경에서의 재킷식 해양구조물 시공 중 및 운영 중 장기 변형률 계측 및 분석

In this study, structural strain responses of the jacket-type Uldolmok tidal current power plant structure under severe tidal environments were measured and analyzed using long-term measurement system during construction and also operation. It was observed that there were significant changes in strain responses at the steps of jacket lifting, block loading, pile ejection and insertion. Strains due to dead loads and tidal loads were analyzed before and after removal of a jacket leg, and it was also found that the strains due to dead load were much significantly changed after jacket leg removal. From the measurement data during operation, it was found that strain responses were fluctuated with M2 and M4 tidal periods and also relatively short period of about 10 min due to the peculiar tidal characteristics in the Uldolmok strait. Finally, the neural network-based non-parametric estimation models were investigated to build up the signal-based structural damage monitoring system.

Static and dynamic measurement of low-level strains with carbon fibers

A signal processing method used to treat the structural strain responses of long-gage carbon fiber (CF) sensors for static and dynamic strain measurements is developed in this paper. This method includes a high-confidence denoising method for continuous static monitoring and a wavelet transform (WT)-based signal processing method for dynamic strain measurements. The static denoising method is based on the measuring error range of CF sensors which defined by a long-term continuous loading and unloading experiments. For the experiment involving 1-measure-time at each strained point, the influence of noise on CF sensors follows a normal distribution with a standard deviation of 50 microstrain. For the experiment involving 25-measure-time at each strained point, the signals are concentrated in a smaller range, and the probability density clearly increases. For a high-confidence signal selection based on multiple-measure-time, the range of error measurement of CF sensors is concentrated from 50 microstrain to 10 microstrain. Furthermore, a study on the signal processing of the dynamic strain signal of CF sensors through WT-based signal processing method is presented. Due to the influence of the dynamic measurement of small strains, the baseline of the dynamic signal of CF sensors drifts. With the WT denoising method, the discrete degree of the dynamic signals was reduced. It is beneficial to find the peak position of each wave in the vibration response of CF sensors. With the peak positions, the baseline drift can be removed by a mathematical regression. Finally, the experimental results are presented to show the effectiveness of the processed signal of the CF sensors studied herein.

Numerical simulation of shaking table test on utility tunnel under non-uniform earthquake excitation

This paper studies numerical simulation of shaking table test on utility tunnel model subjected to non-uniform earthquake excitation. The experimental work is first introduced with focuses on experimental strategy, structural model and soil, instrumentation and test cases. Followed are numerical modeling details of the shaking table tests, including modeling of shear box, soil–structure interaction and initial stress equilibrium. Numerical results are presented and compared with experimental records in terms of boundary effect of the shear box, soil and structure model acceleration response, soil displacement and structural strain responses. It is found that the utility tunnel has a bending deformation and its acceleration response is larger than the surrounding soil for high shaking intensity. The proposed numerical model is found to be satisfactory in predicting many details of the experimental results. The modeling methodology suggested in this paper is reasonable for representing the shaking table test and it can be used for further analysis.

Probabilistic Characterization of Live Load Using Visual Counts and In-Service Strain Monitoring

It has been argued that the AASHTO LRFD design code for maximum live loads on highway bridges is overly conservative. In an attempt to determine the level of conservativeness, if any, the writers developed a methodology incorporating real-time visual data collection from traffic cameras coupled with structural strain response of girder bridges. Average daily truck traffic along with frequency of multiple presences (same lane as well as adjacent lanes) and lane-wise truck traffic distribution were estimated for a steel-girder highway bridge on I-95 in Delaware. These data compared well with predictions from a Poisson process based model developed for this study. Statistical properties of girder moments in single and multiple presence conditions were determined as well. In this particular example, the girder design moment on the 24.6 foot approach span according to AASHTO specifications was found to be about 3.5 times higher than that estimated from the in-service data.