A Method Based on Probability Analysis for Structural Damage Early Warning

2014 ◽  
Vol 1079-1080 ◽  
pp. 386-389
Author(s):  
Yan Sheng Song ◽  
Wei Ning Ni ◽  
Zi Jun Li
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Natural Frequency ◽  
Health Monitoring ◽  
Early Warning ◽  
Structural Damage ◽  
Probability Analysis ◽  
Cable Stayed Bridge ◽  
Frequency Peak ◽  
Degree Of Damage

Damage early warning is the first stage of strucutural health monitoring and damage detection. Abnormal offset of natural frequency can reflect structure in some degree of damage. Based on the probability analysis of frequency peak offset, this paper presents a parameter and corresponding method for structural damage early warning. With this method, it achieves damage early warning for a cable stayed bridge of structural health monitoring.

scholarly journals A Time-Domain Structural Damage Detection Method Based on Improved Multiparticle Swarm Coevolution Optimization Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Shao-Fei Jiang ◽  
Si-Yao Wu ◽  
Li-Qiang Dong
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Damage ◽  
Damage Threshold ◽  
Optimization Techniques ◽  
Local Optimum ◽  
Structural Health ◽  
Civil Infrastructures ◽  
Standard Particle Swarm Optimization

Optimization techniques have been applied to structural health monitoring and damage detection of civil infrastructures for two decades. The standard particle swarm optimization (PSO) is easy to fall into the local optimum and such deficiency also exists in the multiparticle swarm coevolution optimization (MPSCO). This paper presents an improved MPSCO algorithm (IMPSCO) firstly and then integrates it with Newmark’s algorithm to localize and quantify the structural damage by using the damage threshold proposed. To validate the proposed method, a numerical simulation and an experimental study of a seven-story steel frame were employed finally, and a comparison was made between the proposed method and the genetic algorithm (GA). The results show threefold: (1) the proposed method not only is capable of localization and quantification of damage, but also has good noise-tolerance; (2) the damage location can be accurately detected using the damage threshold proposed in this paper; and (3) compared with the GA, the IMPSCO algorithm is more efficient and accurate for damage detection problems in general. This implies that the proposed method is applicable and effective in the community of damage detection and structural health monitoring.

Temperature effect on electromechanical admittance–based concrete structural health monitoring

2019 ◽  
Vol 19 (3) ◽  
pp. 661-692 ◽  
Author(s):  
Demi Ai ◽  
Chengxing Lin ◽  
Hui Luo ◽  
Hongping Zhu
Keyword(s):  
Numerical Analysis ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Temperature Effect ◽  
Health Monitoring ◽  
Structural Damage ◽  
Shield Tunnel ◽  
Engineering Practice ◽  
Damage Evaluation ◽  
Structural Health

Concrete structures in service are often subjected to environmental/operational temperature effects, which change their inherent properties and also inflict a challenge to their extrinsic monitoring systems. Recently, piezoelectric lead zirconate titanate (PZT)-based electromechanical admittance technique has been increasingly growing into an effective tool for concrete structural health monitoring; however, uncertainty in the changes of monitoring signals induced by temperature impact on concrete/PZT sensor would inevitably cause interference to structural damage detection, which adversely hinder its application from laboratory to engineering practice. This article, aiming at exploring the temperature effect on the electromechanical admittance–based concrete damage evaluation, primarily covered a series of theoretical/numerical analysis with rigorously experimental verifications. Three aspects of comparative studies were performed in theoretical/numerical analysis: (1) thermal-dependent parameters were inclusively evaluated in contribution to the electromechanical admittance characteristics via PZT-structure interaction models; (2) three-dimensional finite element analysis in multi-physics coupled field was employed to qualitatively assess the singular temperature effect on the electromechanical admittance behaviors of free-vibrated PZT, surface-bonded PZT/inside-embedded PZT coupled healthy concrete cubes; and (3) depending on the modeling of surface-bonded PZT-/inside-embedded PZT-cracked concrete cube, thermal effect on damage evaluation was addressed via quantification on the electromechanical admittance variations. In the experimental study, rigorous validation tests were carried out on a group of lab-scale concrete cubes, where surface-bonded PZT/inside-embedded PZT transducers were simultaneously employed for electromechanical admittance monitoring in view of thermal difference between concrete surface and its inner part. Correlation coefficient deviation value-based effective frequency shifts algorithm was also employed to compensate the temperature effect. Moreover, temperature effect was further testified on the monitoring of a full-scale shield-tunnel segment structure. Experimental results indicated that temperature triggered different behaviors of electromechanical admittance signatures for surface-bonded PZT/inside-embedded PZT transducers and contaminated the electromechanical admittance responses for damage detection. Structural damage severity level can be disadvantageously amplified by temperature increment even if under the same damage scenarios.

A hybrid learning strategy for structural damage detection

2020 ◽  
pp. 147592172096694
Author(s):  
Lorena Andrade Nunes ◽  
Rafaelle Piazzaroli Finotti Amaral ◽  
Flávio de Souza Barbosa ◽  
Alexandre Abrahão Cury
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Real Time ◽  
Health Monitoring ◽  
Time Domain ◽  
Structural Damage ◽  
Machine Learning Techniques ◽  
Hybrid Classifier ◽  
Detection Techniques ◽  
Structural Health

Over the past decades, several methods for structural health monitoring have been developed and employed in various practical applications. Some of these techniques aimed to use raw dynamic measurements to detect damage or structural changes. Desirably, structural health monitoring systems should rely on computational tools capable of evaluating the information acquired from the structure continuously, in real time. However, most damage detection techniques fail to identify novelties automatically (e.g. damage, abnormal behaviors, and among others), rendering human decisions necessary. Recent studies have shown that the use of statistical parameters extracted directly from raw time domain data, such as acceleration measurements, could provide more sensitive responses to damage with less computational effort. In addition, machine learning techniques have never been more in trend than nowadays. In this context, this article proposes an original approach based on the combination of statistical indicators—to characterize acceleration measurements in the time domain—and computational intelligence techniques to detect damage. The methodology consists in the combined use of supervised (artificial neural networks) and unsupervised ( k-means clustering) learning classification methods for the construction of a hybrid classifier. The objective is to detect not only structural states already known but also dynamic behaviors that have not been identified yet, that is, novelties. The main purpose is to allow a real-time structural integrity monitoring, providing responses in an automatic and continuous way while the structure is under operation. The robustness of the proposed approach is evaluated using data obtained from numerical simulations and experimental tests performed in laboratory and in situ. Results achieved so far attest a promising performance of the hybrid classifier.

Polynomial Interpolated Taylor Series Method for Parameter Identification of Nonlinear Dynamic System

2006 ◽  
Vol 1 (3) ◽  
pp. 248-256 ◽  
Author(s):  
Simon C. Wong ◽  
Alan A. Barhorst
Keyword(s):  
Structural Health Monitoring ◽  
Parameter Identification ◽  
Dynamic System ◽  
Damage Detection ◽  
Taylor Series ◽  
Health Monitoring ◽  
Structural Damage ◽  
Bolted Joints ◽  
Response Dynamics ◽  
Structural Health

This research work is in the area of structural health monitoring and structural damage mitigation. It addresses and advances the technique in parameter identification of structures with significant nonlinear response dynamics. The method integrates a nonlinear hybrid parameter multibody dynamic system (HPMBS) modeling technique with a parameter identification scheme based on a polynomial interpolated Taylor series methodology. This work advances the model based structural health monitoring technique, by providing a tool to accurately estimate damaged structure parameters through significant nonlinear damage. The significant nonlinear damage implied includes effects from loose bolted joints, dry frictional damping, large articulated motions, etc. Note that currently most damage detection algorithms in structures are based on finding changed stiffness parameters and generally do not address other parameters such as mass, length, damping, and joint gaps. This work is the extension of damage detection practice from linear structure to nonlinear structures in civil and aerospace applications. To experimentally validate the developed methodology, we have built a nonlinear HPMBS structure. This structure is used as a test bed to fine-tune the modeling and parameter identification algorithms. It can be used to simulate bolted joints in aircraft wings, expansion joints of bridges, or the interlocking structures in a space frame also. The developed technique has the ability to identify unique damages, such as systematic isolated and noise-induced damage in group members and isolated elements. Using this approach, not just the damage parameters, such as Young’s modulus, are identified, but other structural parameters, such as distributed mass, damping, and friction coefficients, can also be identified.

scholarly journals An Investigation of Structural Damage Location Based on Ultrasonic Excitation-Fiber Bragg Grating Detection

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Yuegang Tan ◽  
Li Cai ◽  
Bei Peng ◽  
Lijun Meng
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Fiber Bragg Grating ◽  
Health Monitoring ◽  
Structural Damage ◽  
Detection System ◽  
Bragg Grating ◽  
Alloy Plate ◽  
Structural Health ◽  
Sensing System

With the continuous development of mechanical automation, the structural health monitoring techniques are increasingly high requirements for damage detection. So structural health monitoring (SHM) has been playing a significant role in terms of damage prognostics. The main contribution pursued in this investigation is to establish a detection system based on ultrasonic excitation and fiber Bragg grating sensing, which combines the advantages of the ultrasonic detection and fiber Bragg grating (FBG). Differencing from most common approaches, a new way of damage detection is based on fiber Bragg grating (FBG), which can easily realize distributed detection. The basic characteristics of fiber Bragg grating sensing system are analyzed, and the positioning algorithm of structural damage is derived in theory. On these bases, the detection system was used to analyze damage localization in the aluminum alloy plate of a hole with diameters of 6 mm. Experiments have been carried out to demonstrate that the sensing system was feasible and that the estimation method of the location algorithm was easy to implement.

Structural Health Monitoring (SHM) of Offshore Jacket Platforms

2011 ◽  
Author(s):  
Mohammad Ali Lotfollahi-Yaghin ◽  
Sajad Shahverdi ◽  
Reza Tarinejad ◽  
Behrouz Asgarian
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Damage Assessment ◽  
Structural Damage ◽  
Wavelet Packet ◽  
Structural Health ◽  
Finite Element Software ◽  
Modal Properties ◽  
Large Structures

In the present paper, Structural health monitoring has become an evolving area of research in last few decades with increasing need of online monitoring the health of large structures. The damage detection by visual inspection of the structure can prove impractical, expensive and ineffective in case of large structures like offshore platforms, multistoried buildings and bridges. Structural health monitoring is defined as the process of detecting damage in a structural system. Damage in the system causes a change in dynamic properties of a system. The structural damage is typically a local phenomenon, which tends to be captured by higher frequency signals. Most of vibration-based damage detection methods require the modal properties that are obtained from measured signals through the system identification techniques. However, the modal properties such as natural frequencies and mode shapes are not such a good sensitive indication of structural damage. Structural damage detection and damage localization of jacket platforms, based on wavelet packet transforms is presented in this paper. Dynamic signals measured from the structure by the finite element software package ANSYS are first decomposed into wavelet packet components. Component energies are then calculated and used for damage assessment. The results show that the WPT-based component energies are good candidate indices that are sensitive to structural damage. These component energies can be used for damage assessment including identifying damage occurrence and location.

scholarly journals Impedance-based damage detection under noise and vibration effects

2017 ◽  
Vol 17 (3) ◽  
pp. 654-667 ◽  
Author(s):  
Leandro M Campeiro ◽  
Ricardo ZM da Silveira ◽  
Fabricio G Baptista
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Damage ◽  
Low Cost ◽  
Coherence Function ◽  
Mechanical Impedance ◽  
Noise And Vibration ◽  
Structural Health ◽  
Damage Indices

The electro-mechanical impedance technique has been extensively studied in recent decades as a non-destructive method for detecting structural damage in structural health monitoring applications using low-cost piezoelectric transducers. Although many studies have reported the effectiveness of this detection method, numerous practical problems, such as the effects of noise and vibration, need to be addressed to enable this method’s effective use in real applications. Therefore, this article presents an experimental analysis of noise and vibration effects on structural damage detection in impedance-based structural health monitoring systems. The experiments were performed on an aluminum bar using two piezoelectric diaphragms, where one diaphragm was used to measure the electrical impedance signatures and the other diaphragm was used as an actuator to generate noise and controlled vibration. The effects of noise and vibration on impedance signatures were evaluated by computing the coherence function and basic damage indices. The results indicate that vibration and noise significantly affect the threshold of the lowest detectable damage, which can be compensated by increasing the excitation signal of the piezoelectric transducer.

A Structural Damage Warning Process Based on Frequency Abnormal Development

2014 ◽  
Vol 1065-1069 ◽  
pp. 1390-1393
Author(s):  
Yan Sheng Song ◽  
Chuan Zheng Ma ◽  
Zong Guang Sun
Keyword(s):  
Structural Health Monitoring ◽  
Frequency Domain ◽  
Health Monitoring ◽  
Structural Damage ◽  
Steel Structure ◽  
Probability Analysis ◽  
Abnormal Development ◽  
Structural Health

This paper proposes a new damage warning process for structural health monitoring (SHM) system, which utilizes probability analysis on information of frequency abnormal development. This process was introduced to analyze information collected in frequency domain from a steel structure, and it achieves damage warning for the structure.

scholarly journals A new approach for structural damage detection exploring the singular spectrum analysis

2016 ◽  
Vol 28 (9) ◽  
pp. 1160-1174 ◽  
Author(s):  
Mario A de Oliveira ◽  
Jozue Vieira Filho ◽  
Vicente Lopes ◽  
Daniel J Inman
Keyword(s):  
Time Series ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Spectrum Analysis ◽  
Structural Damage ◽  
Singular Spectrum Analysis ◽  
Reconstruction Process ◽  
Structural Health ◽  
Singular Spectrum

This article presents a novel approach for damage detection applied to structural health monitoring systems exploring the residues obtained from singular spectrum analysis. In this technique, a lead zirconate titanate patch acting as actuator excites the structure, and three other patches are used as sensors to receive the structural responses. This method is based on a high-frequency excitation range in order to overcome the problem caused when the low-vibration modes are excited. In this method, a wideband chirp signal, with low amplitude and variable frequency, is used to excite the structure. The response signals are acquired in the time domain, and the singular spectrum analysis procedure is performed. The residues obtained between the reconstructed and original time series are used to compute statistical metrics. The residues calculated from singular spectrum analysis are used to compute the root mean square deviation and correlation coefficient deviation metric indices, rendering the damage detection approach more reliable. Tests were carried out on an aluminum plate, and the results have demonstrated the effectiveness of the proposed method making it an excellent approach for structural health monitoring applications. The results exploring different numbers of components used during the reconstruction process of time series are obtained, and the highlights are presented.

A Structural Health Monitoring System for Composite Beams With Coupled Bending-Torsion

2012 ◽  
Author(s):  
Zeaid Hasan ◽  
Ghassan Atmeh
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Natural Frequency ◽  
Health Monitoring ◽  
Composite Beam ◽  
Natural Frequencies ◽  
Mode Shapes ◽  
Current Time ◽  
Structural Health ◽  
Area Of Interest

Structural health monitoring (SHM) is the process of damage identification in structural systems which have been an area of interest and a well-recognized field of technology in the past decade. Such systems involve the integration of smart materials, sensors and decision-making algorithms into the structure to detect damage, evaluate the structural integrity and predict the remaining life time. These systems have the potential to replace traditional non-destructive evaluation (NDE) of structures. This study focuses on presenting an automated structural health monitoring (SHM) system based on detecting shifts in natural frequencies of the structure. The damage detection technique is implemented on a cracked composite beam vibrating in coupled bending-torsion where the crack is assumed open. Modal analysis is conducted on the composite beam in order to predict the natural frequency and the associated mode shapes. Based on this analysis, a database of information related to the specific composite beam being analyzed such as layups and natural frequencies are stored. The natural frequency will be measured and compared to that database for damage detection. A finite element model is also presented and compared with the analytical results. It is observed that the variation of natural frequencies in the presence of a crack is affected by the crack ratio, crack location and fiber orientation. In particular, the variation pattern is different as the magnitude of bending-torsion coupling changes due to different fiber angles. A simple circuit containing a microcontroller is implemented to simulate the automated SHM concept. The microcontroller serves as the data storage device as well as the decision maker based on the instantaneous comparison between the healthy and the damaged structure. The proposed system may be implemented in many structural components such as aircraft frames and bridges. This SHM technology may help replace the current time-based maintenance scheme with a condition-based one. The condition-based maintenance scheme relies on the ability to monitor the condition of the system and supply information of damage detection to allow a corrective action to be taken.

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