Analytical and Experimental Studies into Structural Health Monitoring

2000 ◽  
Author(s):  
Sami F. Masri
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Experimental Studies ◽  
Structural Health

Dynamic response reconstruction for structural health monitoring using densely connected convolutional networks

2020 ◽  
pp. 147592172091688
Author(s):  
Gao Fan ◽  
Jun Li ◽  
Hong Hao
Keyword(s):  
Structural Health Monitoring ◽  
Dynamic Response ◽  
Health Monitoring ◽  
Network Architecture ◽  
Experimental Studies ◽  
Modal Identification ◽  
Structural Health ◽  
Convolutional Networks ◽  
Response Reconstruction ◽  
Training Efficiency

This article proposes a novel dynamic response reconstruction approach for structural health monitoring using densely connected convolutional networks. Skip connection and dense block techniques are carefully applied in the designed network architecture, which greatly facilitates the information flow, and increases the training efficiency and accuracy of feature extraction and propagation with fewer parameters in the network. Sub-pixel shuffling and dropout techniques are used in the designed network and applied to reduce the computational demand and improve training efficiency. The network is trained in a supervised manner, where the input and output are the measurements of the available channels at response available locations and desired channels at response unavailable locations. The proposed densely connected convolutional networks automatically extract the high-level features of the input data and construct the complicated nonlinear relationship between the responses of available and desired locations. Experimental studies are conducted using the measured acceleration responses from Guangzhou New Television Tower to investigate the effects of the locations of available responses, the numbers of available and unavailable channels, and measurement noise. The results demonstrate that the proposed approach can accurately reconstruct the responses in both time and frequency domains with strong noise immunity. The reconstructed response is further used for modal identification to demonstrate the usability and accuracy of the reconstructed responses. The applicability of the proposed approach for structural health monitoring is further proved by the highly consistent modal parameters identified from the reconstructed and true responses.

scholarly journals Deep residual network framework for structural health monitoring

2020 ◽  
pp. 147592172091837 ◽  
Author(s):  
Ruhua Wang ◽  
Chencho ◽  
Senjian An ◽  
Jun Li ◽  
Ling Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Structural Health Monitoring ◽  
Convolutional Neural Network ◽  
Health Monitoring ◽  
Damage Identification ◽  
State Of The Art ◽  
Experimental Studies ◽  
The State ◽  
Residual Network ◽  
Structural Health

Convolutional neural networks have been widely employed for structural health monitoring and damage identification. The convolutional neural network is currently considered as the state-of-the-art method for structural damage identification due to its capabilities of efficient and robust feature learning in a hierarchical manner. It is a tendency to develop a convolutional neural network with a deeper architecture to gain a better performance. However, when the depth of the network increases to a certain level, the performance will degrade due to the gradient vanishing issue. Residual neural networks can avoid the problem of vanishing gradients by utilizing skip connections, which allows the information flowing to the next layer through identity mappings. In this article, a deep residual network framework is proposed for structural health monitoring of civil engineering structures. This framework is composed of purely residual blocks which operate as feature extractors and a fully connected layer as a regressor. It learns the damage-related features from the vibration characteristics such as mode shapes and maps them into the damage index labels, for example, stiffness reductions of structures. To evaluate the efficacy and robustness of the proposed framework, an intensive evaluation is conducted with both numerical and experimental studies. The comparison between the proposed approach and the state-of-the-art models, including a sparse autoencoder neural network, a shallow convolutional neural network and a convolutional neural network with the same structure but without skip connections, is conducted. In the numerical studies, a 7-storey steel frame is investigated. Four scenarios with considering measurement noise and finite element modelling errors in the data sets are studied. The proposed framework consistently outperforms the state-of-the-art models in all the scenarios, especially for the most challenging scenario, which includes both measurement noise and uncertainties. Experimental studies on a prestressed concrete bridge in the laboratory are conducted. The proposed framework demonstrates consistent damage prediction results on this beam with the state-of-the-art models.

scholarly journals Dynamic Modelling and Experimental Characterization of a Self-Powered Structural Health-Monitoring System with MFC Piezoelectric Patches

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 950
Author(s):  
Gianpietro Di Rito ◽  
Mario Rosario Chiarelli ◽  
Benedetto Luciano
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Fiber Composite ◽  
Experimental Studies ◽  
Dynamic Modelling ◽  
Structural Response ◽  
False Alarms ◽  
Electromechanical Impedance ◽  
Structural Health ◽  
Self Powered

The paper deals with theoretical and experimental studies for the development of a self-powered structural health monitoring (SHM) system using macro-fiber composite (MFC) patches. The basic idea is to integrate the actuation, sensing, and energy harvesting capabilities of the MFC patches in a SHM system operating in different regimes. As an example, during flight, under the effects of normal structural vibrations, the patches can work as energy harvesters by maintaining or restoring the battery charge of the stand-by SHM electronic board; on the other hand, if relevant/abnormal loadings are applied, or if local faults produce a noticeable stiffness variation of the monitored component, the patches can act as sensors for the power-up SHM board. During maintenance, the patches can then work as actuators, to stress the structure with pre-defined load profiles, as well as sensors, to monitor the structural response. In this paper, the investigation, based on the electromechanical impedance technique, is carried out on a system prototype made of a cantilevered composite laminate with six MFC patches. A high-fidelity nonlinear model of the system, including the piezoelectric hysteresis of the patches and three vibration modes of the laminate beam, is presented and validated with experiments. The results support the potential feasibility of the system, pointing out that the energy storage can be used for recharging a 3V-65mAh Li-ion battery, suitable for low-power electronic boards. The model is finally used to characterize a condition-monitoring algorithm in terms of false alarms rejection and vulnerability to dormant faults, by simulating built-in tests to be performed during maintenance.

CLoVER: an alterntive concept for damage interrogation in structural health monitoring systems

2009 ◽  
Vol 113 (1144) ◽  
pp. 339-356 ◽  
Author(s):  
K. I. Salas ◽  
C. E. S. Cesnik
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Experimental Studies ◽  
Guided Wave ◽  
Structural Defects ◽  
Pattern Design ◽  
Structural Health ◽  
Damage Prognosis ◽  
Ring Configuration ◽  
Variable Direction

AbstractStructural Health Monitoring (SHM) is the component of damage prognosis systems responsible for interrogating a structure to detect, locate, and identify any damage present. Guided wave (GW) testing methods are attractive for this application due to the GW ability to travel over long distances with little attenuation and their sensitivity to different damage types. The Composite Long-range Variable-direction Emitting Radar (CLoVER) transducer is introduced as an alternative concept for efficient damage interrogation in GW SHM systems. This transducer has an overall ring geometry, but is composed of individual wedge-shaped anisotropic piezocomposite sectors that can be individually excited to interrogate the structure in a particular direction. The transducer is shown to produce actuation amplitudes larger than those of a similarly sized ring configuration for the same electric current input. The electrode pattern design used allows each sector to act as an independent actuator and sensor element, decreasing the number of separate transducers needed for inspection. The fabrication and characterisation procedures of these transducers are described, and their performance is shown to be similar to that of conventional piezocomposite transducers. Experimental studies of damage detection demonstrating the proposed interrogation approach are also presented for simulated structural defects.

Sign in / Sign up

Export Citation Format

Share Document