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Continuous structural health monitoring of the Sayano-Shushenskaya Dam using off-site seismic station data accounting for environmental effects
(2020-01-01)
Hsu, Ting-Yu
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Valentino, Arygianni
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Liseikin, Aleksei
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Krechetov, Dmitry
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Chen, Chun-Chung
Damage to a huge dam can cause great loss of human life and property, but disasters and their consequences can be minimized by implementing effective dam safety monitoring strategies. However, establishing a permanent monitoring system on a huge darn is costly. Additionally, for reasons of national security, many darns and information about them may not be able to be accessed by researchers. Accordingly, continuously monitoring the structural health of a dam by measurement may be difficult. This study presents a way to continuously monitor the health of a dam using vibration signals that are measured not on the dam but close to it. The Sayano-Shushenskaya Dam in Russia is used to demonstrate the idea. Intensive ambient vibration measurements were firstly made once to determine the natural frequencies of the dam. Then the natural frequencies of the dam under varying environmental effects are obtained from the spectra of the seismic records obtained at Cheryomushki seismic station, which is located 4.4 km northeast of the darn. To account for the effects of varying environmental conditions on the natural frequencies, an autoencoder in the form of an unsupervised learning neural network, was employed. The autoencoder was trained using the natural frequencies without using any environmental factors to learn the intrinsic behavior of the darn under varying environmental conditions. The errors between input data to the trained autoencoder and the regenerated data from the autoencoder can be used to determine whether the dam is under normal conditions. A finite element model of the dam was constructed to simulate changes of natural frequencies due to cracks in the dam structure. The results demonstrate that the proposed method can feasibly monitor the structural health of the dam.
Integration of Refined Composite Multiscale Cross-Sample Entropy and Backpropagation Neural Networks for Structural Health Monitoring
(2020-02-01)
Lin, Tzu-Kang
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Chen, Yu-Ching
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土木工程學系
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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.
Visible Light Communication System for Offshore Wind Turbine Foundation Scour Early Warning Monitoring
(2019-07-01)
Lin, Yung-Bin
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Lin, Tzu-Kang
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Chang, Cheng-Chun
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Huang, Chang-Wei
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Chen, Ben-Ting
Offshore wind farms have a superior wind source to terrestrial wind farms, but they also face more severe environmental conditions such as severe storms, typhoons, and sea waves. Scour leads to the excavation of sediments around the foundations of structures, reducing the safe capacity of the structures. The phenomenon of pier scour is extremely complex because of the combined effects of the vortex system involving time-dependent flow patterns and sediment transport mechanisms. A real-time scour monitoring system can improve the safety of structures and afford cost-effective operations by preventing premature or unnecessary maintenance. This paper proposes an on-site scour monitoring system using visible light communication (VLC) modules for offshore wind turbine installations. A flume experiment revealed that the system was highly sensitive and accurate in monitoring seabed scour processes. This arrayed-VLC sensory system, proposed in this paper, has considerable potential for safety monitoring and also can contribute to improving the accuracy of empirical scour formulas for sustainable maintenance in the life cycle of offshore structures.
Development and Application of a Vibration Isolation System with Adaptive Stiffness Considering Potential Energy
(2015-01-01)
Chen, Chi-Jen
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Lin, Tzu-Kang
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土木工程學系
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Department of Civil Engineering
In recent years, a study of a semi-active isolation system named the Leverage-type Stiffness Controllable Isolation System (LSCIS) was proposed. The main concept of the LSCIS is to adjust the stiffness in the isolator for the fundamental period of the superstructure by a simple leverage mechanism. Although great performance has been achieved with the support of the least input energy method (LIEM) in far-field earthquakes, some results still reveal that the proposed system is not suitable for application in near-fault strong ground motion. To overcome this problem, two algorithms that consider the potential energy effect in the semi-active structural control system are proposed in this study. The optimal weightings between the potential and kinetic energy are first determined through a series of near-fault earthquake simulations. The proposed algorithms are then developed with the combination of the potential energy (Ep) and the kinetic energy (Ep) as the control objective to reduce the structural displacement responses efficiently. In order to demonstrate the performance of the proposed algorithm, a two-degree-of-freedom structure is used as a benchmark in both numerical simulation and experimental verification. Numerical results have shown that the dynamic response of the structure can be effectively alleviated by the proposed algorithm under both far-field and near-fault earthquakes, while the structural responses by the LIEM may be worse than the pure passive control. The feasibility of implementing the proposed system has also been experimentally verified.
Performance Evaluation of an Entropy-Based Structural Health Monitoring System Utilizing Composite Multiscale Cross-Sample Entropy
(2019-01-01)
Lin, Tzu-Kang
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Chien, Yi-Hsiu
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土木工程學系
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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.
Applications of neural network models for structural health monitoring based on derived modal properties
(2018-12-01)
Chang, Chia-Ming
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Lin, Tzu-Kang
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Chang, Chih-Wei
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土木工程學系
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Department of Civil Engineering
Structural health monitoring is required to interpret damaged structures in terms of locations and severity, even remaining performance of the damaged members. Therefore, this study proposes a new artificial intelligence-based structural health monitoring strategy based on neural network modeling. A neural network model is developed in accordance with a numerical model which is derived from the identified modal properties under ambient vibrations. The stochastic subspace system identification is first implemented to derive the natural frequencies and mode shapes of a healthy structure. These natural frequencies and mode shapes are then employed to derive a simplified model of this structure, allowing changing stiffness terms to construct various damage patterns. A neural network model is trained and built by the modal properties of the structure with these damage patterns. After a critical event occurs (e.g., earthquakes), this neural network model can be employed to estimate the damage patterns in terms of stiffness reduction. In this study, a numerical example consisting of two damage scenarios is carried out. This example studies a seven-story building with a single and multiple damaged columns in order to evaluate performance of the proposed structural health monitoring strategy. Moreover, the proposed structural health monitoring strategy is also applied to an experimental test of a scaled twin-tower building with weak braces in some floors. Partially modal properties of the structure are obtained from the stochastic subspace system identification, while a simplified model is developed in accordance to the identified modal properties of the healthy building. Then, a neural network model is established based on this simplified model. After seismic events, this neural network model is employed to carry damage detection of this building in terms of damage locations and levels. As a result, the proposed artificial intelligence-based structural health monitoring strategy is quite effective to locate damage if the identified modal properties are relatively accurate.
Compressive Column Load Identification in Steel Space Frames Using Second-Order Deflection-Based Methods
(2018-07-01)
Bonopera, Marco
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Chang, Kuo-Chun
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Chen, Chun-Chung
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Lin, Tzu-Kang
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Tullini, Nerio
This paper presents a comparison of two static nondestructive methods used to assess compressive loads in columns of steel space frames. The first method requires knowledge of the flexural rigidity of the column under investigation, whereas the second method requires knowledge of the column's buckling load. In each method, short-term displacements are measured at given cross-sections along the member under examination, which is subjected to an additional transverse load. The two methods were verified in this study through experimental and numerical tests on a column of a small-scale space frame prototype with generic connections and end conditions. Estimations of compressive forces were generally reliable when second-order effects were accurately considered. In conclusion, the two methods can be successfully used to test steel space frames in a laboratory or under real conditions.
Analytical and experimental studies on building mass damper system with semi-active control device
(2018-06-01)
Liu, Yu-Fang
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Lin, Tzu-Kang
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Chang, Kuo-Chun
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土木工程學系
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Department of Civil Engineering
A concept of improving an optimum building mass damper (OBMD) system through a semi-active control method for enhancing the efficiency of controlling structural responses is proposed. The proposed concept entails replacing the passive fluid viscous dampers used in an OBMD system with semi-active magnetorheological dampers, thus transforming the system into a novel semi-active OBMD (SOBMD) system for better seismic performance. A numerical model based on the Bouc-Wen model with the interpolation technique was established to represent the behavior of the semi-active magnetorheological dampers. The SOBMD system was then developed on the basis of linear-quadratic regulator method and a continuous-optimal control concept. The damping force in the SOBMD system can be manipulated according to the structural response; therefore, the SOBMD system affords the possibility of improving the displacement or acceleration response compared with the original OBMD system. Both of the numerical analysis and the experimental verification demonstrated the superior seismic mitigation ability of the SOBMD system. Several earthquake events with different characteristics were simulated in a series of numerical analysis to further verify the performance of the proposed SOBMD system. In short, the benefits of both the active and passive control system are successfully combined in the proposed SOBMD system.
Semi-active leverage-type isolation system considering minimum structural energy
(2018-03-01)
Lin, Tzu-Kang
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Lu, Lyan-Ywan
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Chen, Chi-Jen
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土木工程學系
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Department of Civil Engineering
Semi-active isolation systems based on leverage-type stiffness control strategies have been widely studied. The main concept behind this type of system is to adjust the stiffness in the isolator to match the fundamental period of the isolated system by using a simple leverage mechanism. Although this system achieves high performance under far-field earthquakes, it is unsuitable for near-fault strong ground motion. To overcome this problem, this study considers the potential energy effect in the control law of the semi-active isolation system. The minimal energy weighting (MEW) between the potential energy and kinetic energy was first optimized through a series of numerical simulations. Two MEW algorithms, namely generic and near-fault MEW control, were then developed to efficiently reduce the structural displacement responses. To demonstrate the performance of the proposed method, a two-degree-of-freedom structure was employed as a benchmark. Numerical results indicate that the dynamic response of the structure can be effectively dampened by the proposed MEW control under both far-field and near-fault earthquakes, whereas the structural responses resulting from conventional control methods may be greater than those for the purely passive control method. Moreover, according to experimental verifications, both the generic and near-fault MEW control modes yielded promising results under impulse-like earthquakes. The practicability of the proposed control algorithm was verified.
Entropy-Based Structural Health Monitoring System for Damage Detection in Multi-Bay Three-Dimensional Structures
(2018-01-01)
Lin, Tzu-Kang
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Lainez, Ana Gabriela
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土木工程學系
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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.

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