Structural Health Monitoring by Accelerometric Data of a Continuously Monitored Structure with Induced Damages

The possibility of determining the integrity of a real structure subjected to non-invasive and non-destructive monitoring,such as that carried out by a series of accelerometers placed on the structure,is certainly a goal of extreme and current interest.In the present work,the results obtained from the processing of experimental data of a real structure are shown.The analyzed structure is a lattice structure approximately 9 m high,monitored with 18 uniaxial accelerometers positioned in pairs on 9 different levels.The data used refer to continuous monitoring that lasted for a total of 1 year,during which minor damage was caused to the structure by alternatively removing some bracings and repositioning them in the structure.Two methodologies detecting damage based on decomposition techniques of the acquired data were used and tested,as well as a methodology combining the two techniques.The results obtained are extremely interesting,as all the minor damage caused to the structure was identified by the processing methods used,based solely on the monitored data and without any knowledge of the real structure being analyzed.The results use 15 acquisitions in environmental conditions lasting 10 min each,a reasonable amount of time to get immediate feedback on possible damage to the structure.

Fuzzy Control Model for Structural Health Monitoring of Civil Infrastructure Systems

This paper presents a Fuzzy Control Model for SHM （Structural Health Monitoring） of civil infrastructure systems. Two important considerations of this model are （a） effective control of structural mechanism to prevent damage of civil infrastructure systems, and （b） energy-efficient data transmissions. Fuzzy Logic is incorporated into the model to provide （a） capability for handling imprecision and non-statistical uncertainty associated with structural monitoring, and （b） framework for effective control of the mechanism of civil infrastructure systems. Moreover, wireless smart sensors are deployed in the model to measure dynamic response of civil infrastructure systems to structural excitation. The operation of these wireless smart sensors is characterized as discounted SMDP （Semi-Markov Decision Process） consisting of two states, namely： sensing/processing and transmitting/receiving. The objective of the SMDP-based measurement scheme is to choose policy that offers optimal energy-efficient transmission of measured value of vibration-based dynamic response. Depending on the net magnitude of measured dynamic responses to excitation signals, data may （or may not） be transmitted to the Fuzzy control segment for appropriate control of the mechanism of civil infrastructure systems. The efficacy of this model is tested via numerical analysis, which is implemented in MATLAB software. It is shown that this model can provide energy-efficient structural health monitoring and effective control of civil infrastructure systems.

Operational modal identification of suspension bridge based on structural health monitoring system

An output-only modal identification method by a combination use of the peak-picking method and the cross spectrum methods are presented. Meanwhile, a novel mode shape optimum method of the deck is proposed. The methods are applied to the operational modal identification system of the Runyang Suspension Bridge, which can be used to obtain the modal parameters of the bridge from out-only data sets collected by its structural health monitoring system （SHMS）. As an example, the vibration response data of the deck, cable and tower recorded during typhoon Matsa excitation are used to illustrate the program application. Some of the modal frequencies observed from deck vibration responses are also found in the vibration responses of the cable and the tower. The results show that some modal shapes of the deck are strongly coupled with the cable and the tower. By comparing the identification results from the operational modal system with those from field measurements, a good agreement between them is achieved, but some modal frequencies identified from the operational modal identification system （OMIS）, such as L1 and L2, obviously decrease compared with those from the field measurements.

Energy evolution and structural health monitoring of coal under different failure modes:An experimental study

Structural instability in underground engineering,especially in coal-rock structures,poses significant safety risks.Thus,the development of an accurate monitoring method for the health of coal-rock bodies is crucial.The focus of this work is on understanding energy evolution patterns in coal-rock bodies under complex conditions by using shear,splitting,and uniaxial compression tests.We examine the changes in energy parameters during various loading stages and the effects of various failure modes,resulting in an innovative energy dissipation-based health evaluation technique for coal.Key results show that coal bodies go through transitions between strain hardening and softening mechanisms during loading,indicated by fluctuations in elastic energy and dissipation energy density.For tensile failure,the energy profile of coal shows a pattern of “high dissipation and low accumulation” before peak stress.On the other hand,shear failure is described by “high accumulation and low dissipation” in energy trends.Different failure modes correlate with an accelerated increase in the dissipation energy before destabilization,and a significant positive correlation is present between the energy dissipation rate and the stress state of the coal samples.A novel mathematical and statistical approach is developed,establishing a dissipation energy anomaly index,W,which categorizes the structural health of coal into different danger levels.This method provides a quantitative standard for early warning systems and is adaptable for monitoring structural health in complex underground engineering environments,contributing to the development of structural health monitoring technology.

Comparative Analysis of ARIMA and LSTM Model-Based Anomaly Detection for Unannotated Structural Health Monitoring Data in an Immersed Tunnel

Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.

Improving autoencoder-based unsupervised damage detection in uncontrolled structural health monitoring under noisy conditions

Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.

Big Model Strategy for Bridge Structural Health Monitoring Based on Data-Driven, Adaptive Method and Convolutional Neural Network (CNN) Group

This study introduces an innovative“Big Model”strategy to enhance Bridge Structural Health Monitoring(SHM)using a Convolutional Neural Network(CNN),time-frequency analysis,and fine element analysis.Leveraging ensemble methods,collaborative learning,and distributed computing,the approach effectively manages the complexity and scale of large-scale bridge data.The CNN employs transfer learning,fine-tuning,and continuous monitoring to optimize models for adaptive and accurate structural health assessments,focusing on extracting meaningful features through time-frequency analysis.By integrating Finite Element Analysis,time-frequency analysis,and CNNs,the strategy provides a comprehensive understanding of bridge health.Utilizing diverse sensor data,sophisticated feature extraction,and advanced CNN architecture,the model is optimized through rigorous preprocessing and hyperparameter tuning.This approach significantly enhances the ability to make accurate predictions,monitor structural health,and support proactive maintenance practices,thereby ensuring the safety and longevity of critical infrastructure.

A time reversal damage imaging method for structure health monitoring using Lamb waves

This paper investigates the Lamb wave imaging method combining time reversal for health monitoring of a metallic plate structure. The temporal focusing effect of the time reversal Lamb waves is investigated theoretically. It demonstrates that the focusing effect is related to the frequency dependency of the time reversal operation. Numerical simulations are conducted to study the time reversal behaviour of Lamb wave modes under broadband and narrowband excitations. The results show that the reconstructed time reversed wave exhibits close similarity to the reversed narrowband tone burst signal validating the theoretical model. To enhance the similarity, the cycle number of the excited signal should be increased. Experiments combining finite element model are then conducted to study the imaging method in the presence of damage like hole in the plate structure. In this work, the time reversal technique is used for the recompression of Lamb wave signals. Damage imaging results with time reversal using broadband and narrowband excitations are compared to those without time reversal. It suggests that the narrowband excitation combined time reversal can locate and determine the size of structural damage more precisely, but the cycle number of the excited signal should be chosen reasonably.

Fatigue Performance Analysis and Evaluation for Steel Box Girder Based on Structural Health Monitoring System

Taizhou Yangtze River Bridge as a long-span suspension bridge,the finite element model(FEM)of it is established using the ANSYS Software.The beam4 element is used to simulate the main beam to establish the“spine beam”model of the Taizhou Yangtze River Bridge.The calculated low-order vibration mode frequency of the FEM is in good agreement with the completion test results.The model can simulate the overall dynamic response of the bridge.Based on the vehicle load survey,the Monte Carlo method is applied to simulate the traffic load flow.Then the overall dynamic response analysis of FEM is car-ried out.Taking the bending moment of the main beam as the control index,the fatigue sensitive section in the steel box girder of FEM is analyzed.Based on the strain time history data of steel box girder recorded by the structural health mon-itoring system(SHM),the true stress response of steel box girder under vehicle load is extracted.Taking the cumulative fatigue damage increment as the evalua-tion index,the fati gue performance evaluation of the steel box girders is con-ducted based on the collected health monitoring data.The fatigue effect of the beam section near the steel tower,especially the first section of the middle tower,is the key section of the fatigue analysis by health morning system,which is con-sistent with the calculation results of FEM.

Review and Prospect of Research on Structural Health Monitoring Technology for Bridges

As a crucial infrastructure in the transport system,the safe operation of bridges is directly related to all aspects of people’s daily lives.The development of bridge structural health monitoring technology and its application play an important role in ensuring the safety and extending the service life of bridges.This paper carries out in-depth research and analysis on the related technology of bridge structural health monitoring.Firstly,the existing monitoring technologies at home and abroad are sorted out,and the advantages and problems of various methods are compared and analyzed,including nondestructive testing,stress measurement,vibration characteristic identification,and other commonly used monitoring technologies.Secondly,the key technologies and equipment in the bridge health monitoring system,such as sensor technology,data acquisition,and processing technology,are introduced in detail.Finally,the development trend in the field of bridge health monitoring is prospected from both theoretical research and technical application.In the future,with the development of emerging technologies such as big data,cloud computing,and the Internet of Things,it is expected that bridge health monitoring with intelligent and systematic features will be more widely applied to provide a stronger guarantee for the safe and efficient operation of bridges.

A framework for computer vision-based health monitoring of a truss structure subjected to unknown excitations

Computer vision(CV)methods for measurement of structural vibration are less expensive,and their application is more straightforward than methods based on sensors that measure physical quantities at particular points of a structure.However,CV methods produce significantly more measurement errors.Thus,computer vision-based structural health monitoring(CVSHM)requires appropriate methods of damage assessment that are robust with respect to highly contaminated measurement data.In this paper a complete CVSHM framework is proposed,and three damage assessment methods are tested.The first is the augmented inverse estimate(AIE),proposed by Peng et al.in 2021.This method is designed to work with highly contaminated measurement data,but it fails with a large noise provided by CV measurement.The second method,as proposed in this paper,is based on the AIE,but it introduces a weighting matrix that enhances the conditioning of the problem.The third method,also proposed in this paper,introduces additional constraints in the optimization process;these constraints ensure that the stiffness of structural elements can only decrease.Both proposed methods perform better than the original AIE.The latter of the two proposed methods gives the best results,and it is robust with respect to the selected coefficients,as required by the algorithm.

A correlation between pulse diagnosis of human body and health monitoring of structures

The concept of health monitoring is a key aspect of the field of medicine that has been practiced for a long time. A commonly used diagnostic and health monitoring practice is pulse diagnosis, which can be traced back approximately five thousand years in the recorded history of China. With advances in the development of modern technology, the concept of health monitoring of a variety of engineering structures in several applications has begun to attract widespread attention. Of particular interest in this study is the health monitoring of civil structures. It seem natural, and even beneficial, that these two health-monitoring methods, one as applies to the human body and the other to civil structures, should be analyzed and compared. In this paper, the basic concepts and theories of the two monitoring methods are first discussed. Similarities are then summarized and commented upon. It is hoped that this correlation analysis may help provide structural engineers with some insights into the intrinsic concept of using pulse diagnosis in human health monitoring, which may of be some benefit in the development of modern structural health monitoring methods.

On the Impact of Manufacturing Uncertainty in Structural Health Monitoring of Composite Structures: A Signal to Noise Weighted Neural Network Process

This article investigates the potential impact of manufacturing uncertainty in composite structures here in the form of thickness variation in laminate plies, on the robustness of commonly used Artificial Neural Networks (ANN) in Structural Health Monitoring (SHM). Namely, the robustness of an ANN SHM system is assessed through an airfoil case study based on the sensitivity of delamination location and size predictions, when the ANN is imposed to noisy input. In light of the observed poor performance of the original network, even when its architecture was carefully optimized, it had been proposed to weigh the input layer of the ANN by a set of signal-to-noise (SN) ratios and then trained the network. Both damage location and size predictions of the latter SHM approach were increased to above 90%. Practical aspects of the proposed robust SN-ANN SHM have also been discussed.

New Developments in Structural Health Monitoring Based on Diagnostic Lamb Wave

Structure health monitoring based on diagnostic Lamb waves has been found to be one of the most promising techniques recently. This paper has a brief review of the new developments on this method including the basic novel of the method, fundamentals and mathematics of Lamb wave propagation, narrowband and wideband Lamb wave excitation methods, optimization of excitation factors and diagnostic Lamb wave interpretation methods.

Structural health monitoring of long-span suspension bridges using wavelet packet analysis

During the service life of civil engineering structures such as long-span bridges, local damage at key positions may continually accumulate, and may finally result in their sudden failure. One core issue of global vibration-based health monitoring methods is to seek some damage indices that are sensitive to structural damage, This paper proposes an online structural health monitoring method for long-span suspension bridges using wavelet packet transform （WPT）. The WPT- based method is based on the energy variations of structural ambient vibration responses decomposed using wavelet packet analysis. The main feature of this method is that the proposed wavelet packet energy spectrum （WPES） has the ability to detect structural damage from ambient vibration tests of a long-span suspension bridge. As an example application, the WPES-based health monitoring system is used on the Runyang Suspension Bridge under daily environmental conditions. The analysis reveals that changes in environmental temperature have a long-term influence on the WPES, while the effect of traffic loadings on the measured WPES of the bridge presents instantaneous changes because of the nonstationary properties of the loadings. The condition indication indices VD reflect the influences of environmental temperature on the dynamic properties of the Runyang Suspension Bridge. The field tests demonstrate that the proposed WPES-based condition indication index VD is a good candidate index for health monitoring of long-span suspension bridges under ambient excitations.

Finite element model updating of existing steel bridge based on structural health monitoring

Based on the physical meaning of sensitivity,a new finite element(FE) model updating method was proposed. In this method,a three-dimensional FE model of the Nanjing Yangtze River Bridge(NYRB) with ANSYS program was established and updated by modifying some design parameters. To further validate the updated FE model,the analytical stress-time histories responses of main members induced by a moving train were compared with the measured ones. The results show that the relative error of maximum stress is 2.49% and the minimum relative coefficient of analytical stress-time histories responses is 0.793. The updated model has a good agreement between the calculated data and the tested data,and provides a current baseline FE model for long-term health monitoring and condition assessment of the NYRB. At the same time,the model is validated by stress-time histories responses to be feasible and practical for railway steel bridge model updating.

Structural health monitoring of bridges in the State of Connecticut

A joint effort between the Connecticut Department of Transportation and the University of Connecticut has been underway for more than 20 years to utilize various structural monitoring approaches to assess different bridges in Connecticut. This has been done to determine the performance of existing bridges, refine techniques needed to evaluate different bridge components, and develop approaches that can be used to provide a continuous status of a bridge＇s structural integrity. This paper briefly introduces the background of these studies, with emphasis on recent research and the development of structural health monitoring concepts. This paper presents the results from three different bridge types： a post-tensioned curved concrete box girder bridge, a curved steel box-girder bridge, and a steel multi-girder bridge. The structural health monitoring approaches to be discussed have been successfully tested using field data collected during multi-year monitoring periods, and are based on vibrations, rotations and strains. The goal has been to develop cost-effective strategies to provide critical information needed to manage the State of Connecticut＇s bridge infrastructure.

Strain Transfer Mechanism of Grating Ends Fiber Bragg Grating for Structural Health Monitoring

The grating ends bonding fiber Bragg grating(FBG)sensor has been widely used in sensor packages such as substrate type and clamp type for health monitoring of large structures.However,owing to the shear deformation of the adhesive layer of FBG,the strain measured by FBG is often different from the strain of actual matrix,which causes strain measurement errors.This investigation aims at improving the measurement accuracy of strain for the grating ends surface-bonded FBG.To fulfill this objective,a strain transfer equation of the grating ends bonding FBG is derived,and a theoretical model of the average strain transfer from the matrix to the optical fiber is developed.Moreover,parameters that influence the average strain transfer rate from the matrix to the optical fiber are analyzed.A selection scheme of bonding parameters by numerical simulation is provided,which is significantly advantageous over that of the grating bonding FBG.The theoretical equation is verified by finite element method(FEM).Compared with the existing model,the proposed model has higher measurement accuracy.Experimental tests are performed to validate the effectiveness of the proposed model on the equalintensity cantilever beam,whose surface is attached to the bare FBG with grating ends bonding and strain gauge by using epoxy glue.The results show that there is a great agreement between the outcome of the bare FBG and that of the strain gauge,and the corrected strain is closer to the true strain.The proposed model provides a theoretical basis for the design of the grating ends surface-bonded FBG strain sensor for health monitoring of large structures.

Engineering approach to in-situ bridge health monitoring with fiber bragg gratings

In this presentation the feasibility and capability of fiber Bragg gratings （FBG） employed in bridge health monitoring are demonstrated on a real bridge. FBG＇ s wavelength shift depending on strain variance has been tested. The technique of FBG installation on bridges has been developed. 12 FBG strain sensors and 3 temperature sensors have been successfully embedded in the prestressed concrete box girder during the construction of Heilongjiang Hulan River Bridge. The prestressing tension process and quasi-static loading process of the girder were monitored with those sensors before it was installed onto the bridge. After the bridge was completed, the FBG sensors embedded have been utilized to monitor the strain shift of the beam under quasi-static load, traffic load and temperature. The results show that the traffic fluxes, possible tatigue damage and deflection of the bridge can be revealed conveniently through strain measurements with these FBG sensors, which provide key information for structural health diagnosis. The fact that the FBG strain sensors have withstood the ordeal of harsh construction process and lasted for more than one year proves that their durability and stability can satisfy the requirements for bridge health monitoring. It is also shown that the FBG strain sensor is more adaptive to long-term structural health monitoring than the electric resistance strain gauge.

Remote structural health monitoring with serially multiplexed fiber optic acoustic emission sensors

Development and testing of a serially multiplexed fiber optic sensor system is described.The sensor differs from conventional fiber optic acoustic systems,as it is capable of sensing AE emissions at several points along the length of a single fiber.Multiplexing provides for single channel detection of cracks and their locations in large structural systems. An algorithm was developed for signal recognition and tagging of the AE waveforms for detection of' crack locations,Labora- tory experiments on plain concrete beams and post-tensioned FRP tendons were pcrlormed to evaluate the crack detection capability of the sensor system.The acoustic emission sensor was able to detect initiation,growth and location of the cracks in concrete as well as in the FRP tendons.The AE system is potentially suitable lot applications involving health monitoring of structures following an earthquake.