Structural health monitoring for frame structure with semi-rigid joints

2009 ◽  
Author(s):  
Chun-Hung Lin ◽  
Henry T. Y. Yang
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Frame Structure ◽  
Structural Health ◽  
Rigid Joints

scholarly journals Infrared Thermography Measurement for Vibration-Based Structural Health Monitoring in Low-Visibility Harsh Environments

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7067
Author(s):  
Jia-Hao He ◽  
Ding-Peng Liu ◽  
Cheng-Hsien Chung ◽  
Hsin-Haou Huang
Keyword(s):  
Structural Health Monitoring ◽  
Infrared Thermography ◽  
Mode Shape ◽  
Health Monitoring ◽  
Large Scale ◽  
Frame Structure ◽  
Harsh Environments ◽  
Thermal Imager ◽  
Heat Sources ◽  
Structural Health

In this study, infrared thermography is used for vibration-based structural health monitoring (SHM). Heat sources are employed as sensors. An acrylic frame structure was experimentally investigated using the heat sources as structural marker points to record the vibration response. The effectiveness of the infrared thermography measurement system was verified by comparing the results obtained using an infrared thermal imager with those obtained using accelerometers. The average error in natural frequency was between only 0.64% and 3.84%. To guarantee the applicability of the system, this study employed the mode shape curvature method to locate damage on a structure under harsh environments, for instance, in dark, hindered, and hazy conditions. Moreover, we propose the mode shape recombination method (MSRM) to realize large-scale structural measurement. The partial mode shapes of the 3D frame structure are combined using the MSRM to obtain the entire mode shape with a satisfactory model assurance criterion. Experimental results confirmed the feasibility of using heat sources as sensors and indicated that the proposed methods are suitable for overcoming the numerous inherent limitations associated with SHM in harsh or remote environments as well as the limitations associated with the SHM of large-scale structures.

scholarly journals A Multiobjective Perspective to Optimal Sensor Placement by Using a Decomposition-Based Evolutionary Algorithm in Structural Health Monitoring

2020 ◽  
Vol 10 (21) ◽  
pp. 7710
Author(s):  
Tsung-Yueh Lin ◽  
Jin Tao ◽  
Hsin-Haou Huang
Keyword(s):  
Structural Health Monitoring ◽  
Evolutionary Algorithm ◽  
Health Monitoring ◽  
Sensor Placement ◽  
Frame Structure ◽  
Mode Shapes ◽  
Beam Model ◽  
Optimal Sensor Placement ◽  
Modal Assurance Criterion ◽  
Structural Health

The objective of optimal sensor placement in a dynamic system is to obtain a sensor layout that provides as much information as possible for structural health monitoring (SHM). Whereas most studies use only one modal assurance criterion for SHM, this work considers two additional metrics, signal redundancy and noise ratio, combining into three optimization objectives: Linear independence of mode shapes, dynamic information redundancy, and vibration response signal strength. A modified multiobjective evolutionary algorithm was combined with particle swarm optimization to explore the optimal solution sets. In the final determination, a multiobjective decision-making (MODM) strategy based on distance measurement was used to optimize the aforementioned objectives. We applied it to a reduced finite-element beam model of a reference building and compared it with other selection methods. The results indicated that MODM suitably balanced the objective functions and outperformed the compared methods. We further constructed a three-story frame structure for experimentally validating the effectiveness of the proposed algorithm. The results indicated that complete structural modal information can be effectively obtained by applying the MODM approach to identify sensor locations.

Structural Health Monitoring of a Concrete Frame Subjected to Shake Table Excitations Using Smart Aggregates

2014 ◽  
Author(s):  
Wei Chang ◽  
Juin-Fu Chai ◽  
Wen-I Liao
Keyword(s):  
Structural Health Monitoring ◽  
Earthquake Engineering ◽  
Health Monitoring ◽  
Health Condition ◽  
Frame Structure ◽  
Shake Table ◽  
Rc Structures ◽  
Engineering Research ◽  
Structural Health ◽  
Concrete Frame

Structural health monitoring of RC structures under seismic loads has recently attracted dramatic attention in the earthquake engineering research community. In this paper, a piezoceramic-based device called “smart aggregate” was used for the health monitoring of a two stories one bay RC frame structure under earthquake excitations. The RC moment frame instrumented with smart aggregates was tested using a shake table with different ground excitation intensities. The distributed piezoceramic-based smart aggregates embedded in the RC structure were used to monitor the health condition of the structure during the tests. The sensitiveness and effectiveness of the proposed piezoceramic-based approach were investigated and evaluated by analyzing the measured responses.

scholarly journals High-Dimensional Phase Space Reconstruction with a Convolutional Neural Network for Structural Health Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3514
Author(s):  
Yen-Lin Chen ◽  
Yuan Chiang ◽  
Pei-Hsin Chiu ◽  
I-Chen Huang ◽  
Yu-Bai Xiao ◽  
...  
Keyword(s):  
Neural Network ◽  
Structural Health Monitoring ◽  
Convolutional Neural Network ◽  
Health Monitoring ◽  
Frame Structure ◽  
High Dimensional ◽  
Structural Health ◽  
Multiple Sensors ◽  
Dimensional Phase Space ◽  
Indeterminate Structures

In order to accurately diagnose the health of high-order statically indeterminate structures, most existing structural health monitoring (SHM) methods require multiple sensors to collect enough information. However, comprehensive data collection from multiple sensors for high degree-of-freedom structures is not typically available in practice. We propose a method that reconciles the two seemingly conflicting difficulties. Takens’ embedding theorem is used to augment the dimensions of data collected from a single sensor. Taking advantage of the success of machine learning in image classification, high-dimensional reconstructed attractors were converted into images and fed into a convolutional neural network (CNN). Attractor classification was performed for 10 damage cases of a 3-story shear frame structure. Numerical results show that the inherently high dimension of the CNN model allows the handling of higher dimensional data. Information on both the level and the location of damage was successfully embedded. The same methodology will allow the extraction of data with unsupervised CNN classification to be consistent with real use cases.

scholarly journals Initial Implementation of Structural Health Monitoring System of a Railway Bridge

2021 ◽  
Vol 1200 (1) ◽  
pp. 012019
Author(s):  
D A Purnomo ◽  
W A N Aspar ◽  
W Barasa ◽  
S M Harjono ◽  
P Sukamdo ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Monitoring System ◽  
Health Monitoring ◽  
Frame Structure ◽  
Actual Condition ◽  
Railway Bridge ◽  
Design Review ◽  
Health Monitoring System ◽  
Structural Health ◽  
Structural Health Monitoring System

Abstract In order to determine the actual condition of the railway bridge structure in the field, predictive monitoring is needed by installing a structural health monitoring system (SHMS). In the process of applying the SHMS, a bridge design review was applied to have railway bridge characteristics. The purpose of conducting this design review is to determine the allowable threshold for deflection and vibration of the bridge. This paper will present the analysis of the steel frame structure; with a span of 51.60 meters, 4.45 meters wide, of 5.00 meters high, respectively. According to the applicable standards, the loads used following the function of the bridge on the railroad tracks are calculated. The purpose of this paper is to (1) analyze the strength of the attached profile against the working forces, especially the live load of the rail line, (2) to know the deflection that occurs, (3) to know the natural frequency that occurs, and (4) to develop expert systems. The simulation results are used as the basis for placing sensors on the bridge and as the basis for determining the threshold for the railway bridge SHMS.

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