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Integration of Refined Composite Multiscale Cross-Sample Entropy and Backpropagation Neural Networks for Structural Health Monitoring
(2020-02-01)
Lin, Tzu-Kang
;
Chen, Yu-Ching
;
土木工程學系
;
Department of Civil Engineering
This study developed a structural health monitoring (SHM) system based on refined composite multiscale cross-sample entropy (RCMCSE) and an artificial neural network for monitoring structures under ambient vibrations. RCMCSE was applied to enhance the reliability of entropy estimations. First, RCMCSE was implemented to extract damage features, and finite element analysis software was then used to generate training samples, which included stiffness reductions to achieve various damage patterns. A neural network model was constructed and trained using entropy values for these damage patterns. An experiment was conducted on a seven-story steel benchmark structure to validate the performance of the proposed system. Additionally, a confusion matrix was established to evaluate the performance of the proposed system. The results obtained for a scaled-down benchmark structure indicated that 89.8% of the floors were accurately classified, and 90% of the practical damaged floors were correctly diagnosed. The performance evaluation demonstrated that the proposed SHM system exhibited increased damage location accuracy.
Performance Evaluation of an Entropy-Based Structural Health Monitoring System Utilizing Composite Multiscale Cross-Sample Entropy
(2019-01-01)
Lin, Tzu-Kang
;
Chien, Yi-Hsiu
;
土木工程學系
;
Department of Civil Engineering
The aim of this study was to develop an entropy-based structural health monitoring system for solving the problem of unstable entropy values observed when multiscale cross-sample entropy (MSCE) is employed to assess damage in real structures. Composite MSCE was utilized to enhance the reliability of entropy values on every scale. Additionally, the first mode of a structure was extracted using ensemble empirical mode decomposition to conduct entropy analysis and evaluate the accuracy of damage assessment. A seven-story model was created to validate the efficiency of the proposed method and the damage index. Subsequently, an experiment was conducted on a seven-story steel benchmark structure including 15 damaged cases to compare the numerical and experimental models. A confusion matrix was applied to classify the results and evaluate the performance over three indices: accuracy, precision, and recall. The results revealed the feasibility of the modified structural health monitoring system and demonstrated its potential in the field of long-term monitoring.
Entropy-Based Structural Health Monitoring System for Damage Detection in Multi-Bay Three-Dimensional Structures
(2018-01-01)
Lin, Tzu-Kang
;
Lainez, Ana Gabriela
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土木工程學系
;
Department of Civil Engineering
In this paper, a structural health monitoring (SHM) system based on multi-scale cross-sample entropy (MSCE) is proposed for detecting damage locations in multi-bay three-dimensional structures. The location of damage is evaluated for each bay through MSCE analysis by examining the degree of dissimilarity between the response signals of vertically-adjacent floors. Subsequently, the results are quantified using the damage index (DI). The performance of the proposed SHM system was determined in this study by performing a finite element analysis of a multi-bay seven-story structure. The derived results revealed that the SHM system successfully detected the damaged floors and their respective directions for several cases. The proposed system provides a preliminary assessment of which bay has been more severely affected. Thus, the effectiveness and high potential of the SHM system for locating damage in large and complex structures rapidly and at low cost are demonstrated.
Integration of High-Resolution Laser Displacement Sensors and 3D Printing for Structural Health Monitoring
(2018-01-01)
Chang, Shu-Wei
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Lin, Tzu-Kang
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Kuo, Shih-Yu
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Huang, Ting-Hsuan
;
土木工程學系
This paper presents a novel experimental design for complex structural health monitoring (SHM) studies achieved by integrating 3D printing technologies, high-resolution laser displacement sensors, and multiscale entropy SHM theory. A seven-story structure with a variety of composite bracing systems was constructed using a dual-material 3D printer. A wireless Bluetooth vibration speaker was used to excite the ground floor of the structure, and high-resolution laser displacement sensors (1-mu m resolution) were used to monitor the displacement history on different floors. Our results showed that the multiscale entropy SHM method could detect damage on the 3D-printed structures. The results of this study demonstrate that integrating 3D printing technologies and high-resolution laser displacement sensors enables the design of cheap, fast processing, complex, small-scale civil structures for future SHM studies. The novel experimental design proposed in this study provides a suitable platform for investigating the validity and sensitivity of SHM in different composite structures and damage conditions for real life applications in the future.
Damage detection of structures with detrended fluctuation and detrended crosscorrelation analyses
(2017-03-01)
Lin, Tzu-Kang
;
Fajri, Haikal
;
土木工程學系
;
Department of Civil Engineering
Recently, fractal analysis has shown its potential for damage detection and assessment in fields such as biomedical and mechanical engineering. For its practicability in interpreting irregular, complex, and disordered phenomena, a structural health monitoring (SHM) system based on detrended fluctuation analysis (DFA) and detrended cross-correlation analysis (DCCA) is proposed. First, damage conditions can be swiftly detected by evaluating ambient vibration signals measured from a structure through DFA. Damage locations can then be determined by analyzing the cross correlation of signals of different floors by applying DCCA. A damage index is also proposed based on multi-scale DCCA curves to improve the damage location accuracy. To verify the performance of the proposed SHM system, a four-story numerical model was used to simulate various damage conditions with different noise levels. Furthermore, an experimental verification was conducted on a seven-story benchmark structure to assess the potential damage. The results revealed that the DFA method could detect the damage conditions satisfactorily, and damage locations can be identified through the DCCA method with an accuracy of 75%. Moreover, damage locations can be correctly assessed by the damage index method with an improved accuracy of 87.5%. The proposed SHM system has promising application in practical implementations.
A structural health monitoring system based on multifractal detrended cross-correlation analysis
(2017-09-25)
Lin, Tzu-Kang
;
Chien, Yi-Hsiu
;
土木工程學系
;
Department of Civil Engineering
In recent years, multifractal-based analysis methods have been widely applied in engineering. Among these methods, multifractal detrended cross-correlation analysis (MFDXA), a branch of fractal analysis, has been successfully applied in the fields of finance and biomedicine. For its great potential in reflecting the subtle characteristic among signals, a structural health monitoring (SHM) system based on MFDXA is proposed. In this system, damage assessment is conducted by exploiting the concept of multifractal theory to quantify the complexity of the vibration signal measured from a structure. According to the proposed algorithm, the damage condition is first distinguished by multifractal detrended fluctuation analysis. Subsequently, the relationship between the q-order, q-order detrended covariance, and length of segment is further explored. The dissimilarity between damaged and undamaged cases is visualized on contour diagrams, and the damage location can thus be detected using signals measured from different floors. Moreover, a damage index is proposed to efficiently enhance the SHM process. A seven-story benchmark structure, located at the National Center for Research on Earthquake Engineering (NCREE), was employed for an experimental verification to demonstrate the performance of the proposed SHM algorithm. According to the results, the damage condition and orientation could be correctly identified using the MFDXA algorithm and the proposed damage index. Since only the ambient vibration signal is required along with a set of initial reference measurements, the proposed SHM system can provide a lower cost, efficient, and reliable monitoring process.
Application of multi-scale (cross-) sample entropy for structural health monitoring
(2015-08-01)
Lin, Tzu-Kang
;
Liang, Jui-Chang
;
土木工程學系
;
Department of Civil Engineering
This study proposes an information-theoretic structural health monitoring (SHM) system based on multi-scale entropy (MSE) and multi-scale cross-sample entropy (MSCE). By measuring the ambient vibration signal from a structure, the damage condition can be rapidly evaluated via MSE analysis. The damage location can then be detected by analyzing the signals of different floors under the same damage condition via MSCE analysis. Moreover, a damage index is proposed to efficiently quantify the SHM process. Unlike some existing SHM methods, no experimental database or numerical model is required. Instead, a reference measurement of the current stage can initiate and launch the SHM system. A numerical simulation of a four-story steel structure is used to verify that the damage location and condition can be detected by the proposed SHM algorithm, and the location can be efficiently quantified by the damage index. A seven-story scaled-down benchmark structure is then employed for experimental verification. Based on the results, the damage condition can be correctly assessed, and average accuracy rates of 63.4 and 86.6% for the damage location can be achieved using the MSCE and damage index methods, respectively. As only the ambient vibration signal is required with a set of initial reference measurements, the proposed SHM system can be implemented practically with low cost.
Identifications of symmetric ductile braced structure by pseudo-single-degree-of-freedom procedure
(2014-08-01)
Huang, Ming-Chih
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Wang, Yen-Po
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Lin, Tzu-Kang
;
Wang, Jer-Fu
;
土木工程學系
For energy-dissipation devices, identification of the physical behavior is a more difficult task than their design. This study develops an identification technique that is useful for the acquirement of the dynamic characteristics of buildings with symmetric ductile braces (SDBs) which exhibit bilinear behavior. The considered system is a linear multi-story building with SDBs regarded as a bilinear hysteretic model. The complex multi-degree-of-freedom problem was solved in the pseudo-single-degree-of-freedom domain, and the dynamic parameters of primary building as well as the SDBs are sequentially identified from top to bottom stories based on the input and output responses of floors. To simplify the nonlinear problem, a backbone curve, where the multi-values restoring force is transformed into a single-valued function, is applied to characterize the hysteretic model. A numerical study demonstrates that the proposed identification technique was able to extract the physical parameters of the primary building and the SDBs individually from the floor responses. It may be applied to the health monitoring of buildings protected by nonlinear energy-dissipation devices.

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