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.

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.

Deformation warning index for reinforced concrete dam based on structural health monitoring data and numerical simulation

The material mechanical parameters of the dam body and foundation will change when a dam is reinforced during the aging process.This causes significant changes in the structural state of the project and makes it difficult to ensure its structural safety.In this study,a new deformation warning index for reinforced concrete dams was developed according to the prototype monitoring data,statistical models,three-dimensional finite element model(FEM)numerical simulation,and the critical conditions of the dam structure.A statistical model was established to separate the water pressure component.Then,a three-dimensional FEM of the reinforced concrete dam was constructed to simulate the water pressure component.Furthermore,the deformation components that affected the mechanical parameters of the dam under the same amount of reservoir water level change were separated and quantified accurately.In addition,the method for inversion of comprehensive mechanical parameters after dam reinforcement was used.The influence mechanisms of the deformation behavior of concrete dams under the reservoir water level and temperature changes were investigated.A new deformation warning index was developed by combining the forward-simulated critical water pressure component and temperature component in the period of extreme temperature decrease with the aging component separated by the statistical model.The new deformation warning index considers the structural state of the dam before and after reinforcement and links the structural strength criterion and the deformation evolution mechanisms.It provides a theoretical foundation and decision support for long-term service and operation management of reinforced dams.

Development of piezoelectric-based technology for application in civil structural health monitoring

Piezoelectric material,as one of the great potential materials,had attracted lots of attention all over the world due to its distinguish advantages.In this paper,the development of piezoelectric-based technology for application in the field of civil structural health monitoring(CSHM),was summarized and discussed.Based on the different identification mechanisms,the piezoelectric transducer-based technology can be divided into two main approaches as the active or passive sensing and detection methods.This paper summarized the development of these two approaches and discussed their applications in the area of civil structural health monitoring,such as structural and concrete engineering,bridge engineering,pipeline engineering,protection engineering for geological hazards and earthquake disasters,and so on.In addition,the electrical mechanical impedance(EMI)technique,as one of the active identification methods,was also detailly presented.Finally,its great potential for the piezoelectric-based technique was presented based on the detail discussion,especially in the areas of civil structural health monitoring.

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.

The State of the Art of Data Science and Engineering in Structural Health Monitoring

Structural health monitoring (SHM) is a multi-discipline field that involves the automatic sensing of structural loads and response by means of a large number of sensors and instruments, followed by a diagnosis of the structural health based on the collected data. Because an SHM system implemented into a structure automatically senses, evaluates, and warns about structural conditions in real time, massive data are a significant feature of SHM. The techniques related to massive data are referred to as data science and engineering, and include acquisition techniques, transition techniques, management techniques, and processing and mining algorithms for massive data. This paper provides a brief review of the state of the art of data science and engineering in SHM as investigated by these authors, and covers the compressive sampling-based data-acquisition algorithm, the anomaly data diagnosis approach using a deep learning algorithm, crack identification approaches using computer vision techniques, and condition assessment approaches for bridges using machine learning algorithms. Future trends are discussed in the conclusion.

Design and application of structural health monitoring system in long-span cable-membrane structure

Cable-membrane structures have small rigidity and are highly sensitive to wind. Structural health monitoring is necessary to ensure the serviceability and safety of the structure. In this research, the design method of a structural health monitoring system is using the characteristics of a cable-membrane structure. Taking the Yueyang Sanhe Airport Terminal as an example, a finite element model is established to determine the critical structural components. Next, the engineering requirements and the framework of the monitoring system are studied based on the results of numerical analysis. The specific implementation of the structural health monitoring is then carried out, which includes sensor selection, installation and wiring. The proposed framework is successfully applied to the monitoring system for the Yueyang Airport terminal building, and the synchronous acquisition of fiber Bragg grating and acceleration sensor signals is implemented in an innovative way. The successful implementation and operation of structural health monitoring will help to guarantee the safety of the cablemembrane structure during its service life.

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.

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.

Application of wavelet transform in structural health monitoring

Structural health monitoring(SHM)is a process of implementing a damage detection strategy in existing structures to evaluate their condition to ensure safety.The changes in the material,geometric and/or structural properties affect structural responses,which can be captured and analyzed for condition assessment.Various vibration-based damage detection algorithms have been developed in the past few decades.Among them,wavelet transform(WT)gained popularity as an efficient method of signal processing to build a framework to identify modal properties and detect damage in structures.This article presents the state-of-the-art implementation of various WT tools in SHM with a focus on civil structures.The unique features and limitations of WT,and a comparison of WT and other signal processing methods,are further discussed.The comprehensive literature review in this study will help interested researchers to investigate the use of WT in SHM to meet their specific needs.

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.

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.

Local measurement for structural health monitoring

Localized nature of damage in structures requires local measurements for structural health monitoring. The local measurement means to measure the local, usually higher modes of the vibration in a structure. Three fundamental issues about the local measurement for structural health monitoring including (1) the necessity of making local measurement, (2) the difficulty of making local measurement and (3) how to make local measurement are addressed in this paper. The results from both the analysis and the tests show that the local measurement can successfully monitor the structural health status as long as the local modes are excited. Unfortunately, the results also illustrate that it is difficult to excite local modes in a structure. Therefore, in order to carry structural health monitoring into effect, we must (1) ensure that the local modes are excited, and (2) deploy enough sensors in a structure so that the local modes can be monitored.

Effects of excitation frequency on detection accuracy of orthogonal wavelet decomposition for structural health monitoring

Accurate estimation of stiffness loss is a challenging problem in structural health monitoring.In this studyorthogonal wavelet decomposition is used for identifying the stiffness loss in a single degree of freedom spring-mass-dampersystem.The effects of excitation frequency on accuracy of damage detection is investigated.Results show that pseudo-aliaseffects caused by the orthogonal wavelet decomposition(OWD),affect damage detectability.It is demonstrated that theproposed approach is sunable for damage detection when the excitation frequency is relatively low.This study shows how apriori knowledge about the signal and ability to control the sampling frequency can enhance damage detectability.

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.

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 Offshore Buoyant Leg Storage and Regasification Platform:Experimental Investigations

Offshore platforms are of high strategic importance,whose preventive maintenance is on top priority.Buoyant Leg Storage and Regasification Platforms(BLSRP)are special of its kind as they handle LNG storage and processing,which are highly hazardous.Implementation of structural health monitoring(SHM)to offshore platforms ensures safe operability and structural integrity.Prospective damages on the offshore platforms under rare events can be readily identified by deploying dense array of sensors.A novel scheme of deploying wireless sensor network is experimentally investigated on an offshore BLSRP,including postulated failure modes that arise from tether failure.Response of the scaled model under wave loads is acquired by both wired and wireless sensors to validate the proposed scheme.Proposed wireless sensor network is used to trigger alert monitoring to communicate the unwarranted response of the deck and buoyant legs under the postulated failure modes.SHM triggers the alert mechanisms on exceedance of the measured data with that of the preset threshold values;alert mechanisms used in the present study include email alert and message pop-up to the validated user accounts.Presented study is a prima facie of SHM application to offshore platforms,successfully demonstrated in lab scale.