Strain Monitoring Data Research Articles

Strain Prediction of Bridge SHM Based on CEEMDAN-ARIMA Model

In this paper, a model based on CEEMDAN-ARIMA is proposed to predict the strain monitoring data for bridge SHM. In view of the problem that the classical time series theory cannot predict the modal overlap-ping data effectively, the CEEMDAN method was used to decompose the strain monitoring data for the bridge SHM. To deal with the large number of components after using CEEMDAN, the PE method (permutation entropy) was used to generate a series of new data sequences according to the degree of randomness. Finally, each new data sequence was predicted and the final prediction is obtained by ARIMA model. The method was used to predict the SHM strain data of a cable-stayed bridge in Shanghai. The results show that the proposed combination method is more accurate than the classical time series theory and is promising for engineering applications.

A Bayesian machine learning approach for online detection of railway wheel defects using track-side monitoring

Wheel condition assessment is of great significance to ensure the operation safety of trains and metro systems. This study is intended to develop a Bayesian probabilistic method for online and quantitative assessment of railway wheel conditions using track-side strain-monitoring data. The proposed method is a fully data-driven, nonparametric approach without the need of a physical model. To enable defect identification using only response measurement, the measured dynamic strain responses of rail tracks during the passage of trains are processed to elicit the normalized cumulative distribution function values representative of the effect of individual wheels, which in conjunction with the frequency points are used to formulate a probabilistic reference model in terms of sparse Bayesian learning. Through cleverly realizing sparsity by introducing hyper-parameters and their priors, the sparse Bayesian learning makes the resulting model to exempt from overfitting and generalize well on unseen data. Only the monitoring data in healthy state are needed in formulating the reference model. A novel Bayesian null hypothesis significance testing in terms of scale-invariant intrinsic Bayes factor, which does not suffer from the Jeffreys–Lindley paradox, is then pursued in the presence of new monitoring data collected from possibly defective wheel(s) to detect wheel defects and quantitatively assess wheel condition. The proposed method in fully Bayesian inference framework is verified by utilizing the real-world monitoring data acquired by a distributed fiber Bragg grating–based track-side monitoring system and comparing with the offline inspection results.

Strain Data Analysis of Small and Medium Bridge Structures Based on Finite Element

Structural health monitoring technology is increasingly used in the operation and management of small and medium span bridge structures, which also brings the problem that it is difficult to fully interpret and utilize the mass monitoring data. In this paper, the Sanyang Bridge is used as the engineering background, based on the strain monitoring data, combined with the finite element calculation software MIDAS/civil, to evaluate and analyze the operating state of the bridge structure. The results show that the overall operation status of Sanyang Bridge is good, the evaluation method combining finite element calculation and monitoring data is reasonable, this method can be applied to data processing and condition assessment of small and medium span bridges.

Internal force monitoring and estimation of a long‐span ring beam using long‐gauge strain sensing

AbstractThe internal force information of curved beam members is important evidence for the stress state evaluation and design verification of long‐span space structure during construction. Many structural analysis methods for curved beams have been proposed, but most are used to handle the complex geometric shapes. For stress state evaluation of long‐span steel‐concrete composite (SCC) ring beam, this article presents a method that employs a sectional strain distribution model (SSDM) and a fiber model to estimate the internal force distribution depending on the long‐gauge strain. The SSDM of the ring beam used in the article is based on a mechanical model of two‐dimensional (2‐D) plane bending deformation and a three‐dimensional (3‐D) solid finite element model of curved beam members. The application range of the SSDM is defined by the divided different stress areas of ring beam based on the 3‐D solid finite element model. Combined with the established SSDM and the different stress areas, the axial force and bending moment along the span are then identified separately based on the fiber model. In consideration of field monitoring, the influence of different sensor layout on the establishment of SSDM and the result of internal force identification is discussed. The results of the numerical studies show that the proposed methodology can identify the internal force distribution accurately. Internal force of long‐span ring beam from Jiangsu Grand Theater during construction is also identified through the proposed method by using the strain monitoring data.

Real-time defect detection of high-speed train wheels by using Bayesian forecasting and dynamic model

High-speed rail (HSR) is being developed in Asian and European countries to satisfy the rapidly growing demand for intercity services and to shore up economic growth. The rapid growth of HSR, however, has posed great challenges regarding operation safety, reliability and ride comfort. Irregular wheel defects can induce high-magnitude impact forces hindering safety and ride comfort of HSR and may also cause damage to rail tracks and vehicles. The focus of this study is to develop a real-time defect detection methodology based on Bayesian dynamic linear model (DLM) enabling to detect potentially defective wheels in real time. The proposed methodology embraces logics for (i) prognosis, (ii) potential outlier detection, (iii) identification of change occurrence (change-point detection), and (iv) quantification of damage extent and uncertainty. Relying on the strain monitoring data acquired from high-speed train bogies, the Bayesian DLM for characterizing the actual stress ranges is established, by which one-step forecast distribution is elicited before proceeding to the next observation. The detection of change-point is executed by comparing the routine model (forecast distribution generated by the Bayesian DLM) and an alternative model (the mean value is shifted by a prescribed offset) to determine which better fits the actual observation. If the comparison results are in favor of the alternative model, it is claimed that a potential change has occurred. Whether such an observation is an outlier or the beginning of a genuine change (change-point), three metrics (i.e., Bayes factor, maximum cumulative Bayes factor and run length) are performed for further identification. Once a change-point is confirmed, Bayesian hypothesis testing is conducted for the purpose of damage extent assessment and uncertainty quantification. A severe change, if identified, implies that the quality of train wheels has suffered from a significant alteration due to defects. In the case study, two cases making use of strain monitoring data acquired by fiber Bragg grating (FBG) sensors affixed on bogies are illustrated to verify the performance of the proposed methodology for real-time wheel defect detection of in-service high-speed trains.

Convolutional neural network–based data recovery method for structural health monitoring

In this study, a structural response recovery method using a convolutional neural network is proposed. The aim of this study is to restore missing strain structural responses when they cannot be collected due to a sensor fault, data loss, or communication errors. To this end, a convolutional neural network model for data recovery is constructed using the strain monitoring data stably measured before the occurrence of data loss. Under the assumption that specific sensors fail among the multiple sensors installed on a structure, the structural responses of these specific sensors are intentionally excluded and the remaining structural responses are set as the input data of the convolutional neural network. In addition, the intentionally excluded structural responses are set as the output data of the convolutional neural network. In case of a sensor fault, the trained convolutional neural network is used to recover the missing strain responses using functional sensors alone. The applicability of the proposed method is verified by a numerical study on a beam structure and an experimental study on a frame structure. The data recovery performance of the proposed convolutional neural network is discussed according to the number of failed sensors and the types of structural members with the failed sensors. Finally, the field applicability of the proposed method is examined using strain monitoring data measured from an overpass bridge in use over a long period of time.

Damage detection of bridges monitored within one cluster based on the residual between the cumulative distribution functions of strain monitoring data

With the structural health monitoring technique, several medium- and small-span bridges possessing the same or similar structural characteristics located in a local road network are simultaneously monitored within one cluster. Nevertheless, insufficient research has been performed on detecting the damage of all the bridges monitored within one cluster under time-varying environmental temperatures. To address this issue, a method is proposed to assess the damage in all the bridges within one cluster utilizing the residual between the cumulative distribution functions of strain monitoring data. First, an algorithm for reconstructing the strain monitoring dataset is introduced, effectively improving the computational efficiency at removing as much measured noise from a large amount of monitoring data as possible. Second, a cluster analysis algorithm considering the similarity among the strain monitoring data at different measured points is proposed to classify the strain monitoring data for all the bridges monitored within one cluster. Different classes of strain monitoring data are established by identifying similar probability distribution patterns. Third, for each class, a damage detection index is proposed based on the residual between the cumulative distribution functions of strain monitoring data. All the bridges monitored within one cluster are acted upon by similar environmental temperatures during the same monitoring period; hence, the effects of the environmental temperature on the proposed index are mitigated indirectly during the same monitoring period. Thus, the proposed index is implemented to effectively detect the damage in all the bridges belonging to each class. Finally, the effectiveness of the proposed method is demonstrated through a numerical simulation and with strain monitoring data obtained from actual bridges.

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.

Strain Monitoring of Combustible Gas Implosion Test Based on Fiber Bragg Grating

Strain measurement is an important component in model tests of combustible internal explosions. Strain gauges are used to measure strain in traditional electrical measurement methods and have some limitations, such as susceptibility to electromagnetic interference, short life, and inability to distribute. Fiber Bragg gratings (FBGs) are developing into useful sensing tools that can respond to changes in stress, strain, and temperature by changing wavelengths. FBGs have excellent sensing performance, such as long life, antielectromagnetic interference, easy networking, and good reusability. In this paper, FBG sensors are applied to strain monitoring in ethylene flammable implosion experiments. In the ethylene flammable implosion tests, an FBG was placed on the inner surface of the bottom plate of the rectangular steel test device near the detonation vent by the sticking method. The reliability and repeatability of the strain change of the FBG affected by detonation overpressure and combustion were tested at this point. Four explosion tests were carried out. The test results showed that FBG sensors could obtain stable and reliable strain data in all four tests. The strain variation reflects the development of overpressure and combustion in the whole process from ignition. For the strain amplitude formed by overpressure, the minimum was 1.449 με (the third test), and the maximum was 48.181 με (the fourth test).For the strain amplitude step change formed by the deflagration flame front passing the measurement points, the minimum was 1.673 με (the second test), and the maximum was 19.724 με (the fourth test).The strain amplitude produced by the deflagration temperature effect in the four tests ranged from 72.803 με (the first test) to 143.381 με (the fourth test).The results show that FBG sensors can provide reliable and effective strain monitoring data for the experimental study of flammable implosions.

Local Stress-Field Reconstruction Around Holes in a Plate Using Strain Monitoring Data and Stress Function

This study reconstructs a two-dimensional stress field from measured strain data. The advantage of using stress functions is that the stress equilibrium and strain compatibility are automatically satisfied. We use the complex stress functions given by the finite series of polynomials. Then, we find the proper set of coefficients required to make the best fit to the measured strain data. Numerical examples represent the stress concentration problems around a hole(s) in a plate. It is demonstrated that the present method reconstructs the stress field around the hole(s), and the estimated stress agrees with the finite element (FE) analysis result.

Analyzing and modeling inter-sensor relationships for strain monitoring data and missing data imputation: a copula and functional data-analytic approach

Data analysis and modeling are very important in structural health monitoring. A good model is very conducive to better understanding, interpretation, estimation or prediction of structural performance, and dependence of variables. Strain responses are one of the most common measurements in structural health monitoring. Conventional modeling approaches in structural health monitoring for strain monitoring data have limitations in flexibly capturing the complex dependence structures of stochastic components between sensors. Also, addressing the nonlinear phase components is challenging when they are employed to model the inter-sensor relationships for temperature-induced strains. This article presents a refined inter-sensor modeling approach for strain monitoring data, which enables us to better understand the dependence of strain at different locations. Specifically, nonparametric copulas are employed to flexibly describe the dependence structures and the link marginal distributions to form the joint distribution to capture the inter-sensor relationship of stochastic strain responses. As opposed to traditional approaches in structural health monitoring, temperature-induced strains are treated as functional data; the phase and amplitude components are separately modeled by warping functions and a piecewise linear mapping model based on phase–amplitude separation technique; then, phase model and amplitude model are combined to form the final sensor-to-sensor mapping model. A similar functional modeling approach is also discussed for local strain responses resulting from wheel forces. Cooperated by a kernel regression model, applications of the established models in missing data imputation (i.e. replacing missing records by substitute values) are discussed for stochastic strain responses and temperature-induced strains using field monitoring data.

Deflection monitoring of thin-walled wing spar subjected to bending load using multi-element FBG sensors

This paper presents a measurement technology based on the multi-element fiber Bragg grating (FBG) sensors for monitoring the deflection of thin-walled wing spar. A finite element model of the tested thin-walled beam structure subjected to bending load is developed in order to analyze stress status and to derive the convenient position of the optical fiber sensors for a correct monitoring of the wing spar beam. FBG rosette has been designed between the shear web and spar cap as a new installation position of bonding optical fiber sensors for estimation the deflection. An experimental campaign is carried out in order to verify the proper behavior of the deflection of wing spar beam and sensing strategy. The loading test results revealed that multi-element FBG rosette was successfully implemented on the tested thin-walled wing spar beam as well as providing reliable strain and deflection monitoring data from the FBG sensors. The results show that a good correspondence between the input load and output strain of the FBG sensing system with good reproducibility, and the comparative analysis between FBG and resistance strain gage have been achieved, which measurement results of both are consistent. These data and monitoring method are useful for fatigue life prediction of the potential thin-walled I-beam structure.

Fatigue Reliability Assessment for Orthotropic Steel Decks Based on Long-Term Strain Monitoring.

A time-dependent fatigue reliability assessment approach is proposed for welded details of orthotropic steel decks (OSDs) using long-term strain monitoring data. The fatigue reliability limit function of the welded details is established based on the Eurocode specifications. Depending on the distribution characteristics of the measured daily equivalent stress range, either the lognormal distribution or Gaussian mixture model (GMM) is selected to quantify its uncertainty. Subsequently, the fatigue reliability can be calculated using either an explicit formula or the Monte Carlo method. This proposed approach is applied for the fatigue reliability evaluation of two rib-to-deck and two rib-to-rib welded fatigue details of an in-service suspension bridge. The results show that the reliability indices decrease significantly with bridge’s service life. Except for a rib-to-deck detail, all other three welded details cannot meet the target fatigue reliability during this bridge’s 100-year service life. The proposed approach can help bridge owners and operators make informed decisions regarding maintenance and repair of potential fatigue cracks.

Probabilistic corrosion fatigue life assessment of a suspension bridge instrumented with long-term structural health monitoring system

The long-term performance of engineering structures in a corrosive environment will be significantly affected by the coupled action of corrosion and fatigue. In this article, a probabilistic corrosion fatigue analytical model is proposed by taking into account the effects of corrosion-induced reduction of the cross-sectional area and deterioration of the fatigue strength of structural components. The proposed model is exemplified to evaluate the probabilistic corrosion fatigue life of a typical welded joint in the suspension Tsing Ma Bridge instrumented with a long-term structural health monitoring system. A genetic algorithm–based mixture parameter estimation method is developed to facilitate the multimodal modeling of stress spectrum derived from the long-term monitoring data of dynamic strain. The achieved results demonstrate that with the increase in the service life, the reliability index of the investigated typical welded joint is dramatically reduced under the combined effect of corrosion and fatigue.

Analysis of Fiber-Optic Strain-Monitoring Data from a Prestressed Concrete Bridge

This paper presents data from fiber-optic strain monitoring of the Nine Wells Bridge, which is a three-span, pretensioned, prestressed concrete beam-and-slab bridge located in Cambridgeshire in the United Kingdom. The original deployment at the site and the challenges associated with collecting distributed strain data using the Brillouin optical time domain reflectometry (BOTDR) technique are described. In particular, construction and deployment issues of fiber robustness and temperature effects are highlighted. The challenges of interpreting the collected data as well as the potential value of information that may be obtained are discussed. Challenges involved with relating measurements to the expected levels of prestress, including the effects due to debonding, creep, and shrinkage, are discussed and analyzed. This paper provides an opportunity to study whether two commonly used models for creep and shrinkage, adequately model data collected in field conditions.

The time variations in the parameters of the volumetric strain response to the tidal and baric impacts

The parameters describing the state of the geological medium include its response to the continuous external impacts, which characterizes the structure of the medium and the stresses accumulated in it. In the present paper, through analyzing the long time series of the volumetric strain monitoring data in the nearsurface crustal layer, which were obtained by the American geophysicists under the Plate Boundary Observatory (PBO) project within the Parkfield segment of the San Andreas fault, the time behavior of the volumetric strain response to the separate components of the tides and the air pressure impacts is considered.

Extrapolation of extreme traffic load effects on bridges based on long-term SHM data

In the design and condition assessment of bridges, it is usually necessary to take into consideration the extreme conditions which are not expected to occur within a short time period and thus require an extrapolation from observations of limited duration. Long-term structural health monitoring (SHM) provides a rich database to evaluate the extreme conditions. This paper focuses on the extrapolation of extreme traffic load effects on bridges using long-term monitoring data of structural strain. The suspension Tsing Ma Bridge (TMB), which carries both highway and railway traffic and is instrumented with a long-term SHM system, is taken as a testbed for the present study. Two popular extreme value extrapolation methods: the block maxima approach and the peaks-over-threshold approach, are employed to extrapolate the extreme stresses induced by highway traffic and railway traffic, respectively. Characteristic values of the extreme stresses with a return period of 120 years (the design life of the bridge) obtained by the two methods are compared. It is found that the extrapolated extreme stresses are robust to the extrapolation technique. It may owe to the richness and good quality of the long-term strain data acquired. These characteristic extremes are also compared with the design values and found to be much smaller than the design values, indicating conservative design values of traffic loading and a safe traffic-loading condition of the bridge. The results of this study can be used as a reference for the design and condition assessment of similar bridges carrying heavy traffic, analogous to the TMB.

An Integrated Geomechanical Investigation, Multi-Parameter Monitoring and Analyses of Babadağ-Gündoğdu Creep-like Landslide

A creep-like landslide in the Gundogdu district of Babadag town in Denizli (Turkey), where about 2000 people lived within the damaged houses, has been moving with a velocity of 4–14 cm/year since 1940s. Field observations and monitoring together with geomechanical laboratory tests were carried out to investigate the causative factors of the landslide. These studies were conducted as a part of an international research project performed by Turkish and Japanese scientists since 2000. Long-term monitoring stations established involved measurements of meteorological parameters, displacements, acoustic emission counts, variations in groundwater table, borehole strain measurement, in situ permeability and infiltration characteristics of the slope forming materials, and vibrations induced by weaving machines during their operation. Geomechanical properties of the sandstone and marl, which form the unstable slope, were determined from laboratory tests. In addition to the use of conventional 2-D equilibrium method of analyses, a new approach for modelling the long-term creep-like behaviour of the landslide body, based on discrete finite element method, was also proposed and used to analyse the landslide. It was found that the sliding mass has been involving several zones of weakness (interface) between the sandstone and marl layers through in situ monitoring. The monitoring data of pipe strain, groundwater level fluctuation and rainfall, and AE data showed that slope movement accelerated during and after rainy seasons. It was obtained that the proposed numerical method based on discrete finite element method (DFEM), which considers the softening and hardening of stiffness of the weakness zone as a function of rainfall and, is capable of simulating creep-like behaviour of the landslide. Disaster and Emergency Management Authority of Turkey also considered the results of this research and the landslide area was designated as a Natural Disaster Area and the people living in the unstable part of the town were re-settled at a new area.

Using Multi-sensor Data Fusion in Outlier Detection of Building Strain Monitoring Data

Using Multi-sensor Data Fusion in Outlier Detection of Building Strain Monitoring Data

Experimental study on pullout performance of sensing optical fibers in compacted sand

The distributed optical fiber sensing systems have played an increasingly important role in monitoring civil infrastructures over the past few years. One of the main challenges of their applications to geotechnical monitoring is to increase the reliability of strain sensing optical fibers in measuring the deformation of surrounding soil masses. In this paper, a pullout test method is proposed to characterize the deformation compatibility between an optical fiber and soil. A series of pullout tests on three types of sand-embedded optical fibers are conducted to investigate the performance of the fiber–sand interface. Based on the test results, an explicit tri-linear pullout force–displacement relationship is proposed to describe the mechanical behavior of the fiber–sand interface. The performance of the three fibers regarding fiber–sand interaction mechanism is evaluated in terms of ratio of effective pullout displacement to diameter, ratio of residual pullout displacement to diameter, peak shear strength and residual shear strength. All four parameters of the three fibers are found to have approximately linear relationships with the applied confining pressure, which reveals that the deformation compatibility of the fiber–sand interface is utterly dependent on the confining pressure. For all the three fibers, the first shear stiffness coefficient is about 8N/mm and the ratio of residual to peak shear strength is about 0.5. Furthermore, the Mohr–Coulomb failure criterion is used to get the cohesions and friction angles of the three fiber–sand interfaces. Through a comparison of the pullout performance, one out of three types of fibers tested is found to be more preferable for soil deformation measurement in laboratory-scale tests. The conclusions can provide valuable references for predicting the fiber–soil interface behavior and evaluating the reliability of strain monitoring data.