• Earthquakes and Structures
• /
• v.12 no.6
• /
• pp.673-687
• /
• 2017

A three-dimensional; structural health monitoring; vertical; planar; cross-sample entropy; multiscaleA three-dimensional structural health monitoring (SHM) system based on multiscale entropy (MSE) and multiscale cross-sample entropy (MSCE) is proposed in this paper. The damage condition of a structure is rapidly screened through MSE analysis by measuring the ambient vibration signal on the roof of the structure. Subsequently, the vertical damage location is evaluated by analyzing individual signals on different floors through vertical MSCE analysis. The results are quantified using the vertical damage index (DI). Planar MSCE analysis is applied to detect the damage orientation of damaged floors by analyzing the biaxial signals in four directions on each damaged floor. The results are physically quantified using the planar DI. With progressive vertical and planar analysis methods, the damaged floors and damage locations can be accurately and efficiently diagnosed. To demonstrate the performance of the proposed system, performance evaluation was conducted on a three-dimensional seven-story steel structure. According to the results, the damage condition and elevation were reliably detected. Moreover, the damage location was efficiently quantified by the DI. Average accuracy rates of 93% (vertical) and 91% (planar) were achieved through the proposed DI method. A reference measurement of the current stage can initially launch the SHM system; therefore, structural damage can be reliably detected after major earthquakes.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2006.04a
• /
• pp.21-28
• /
• 2006

Structural health monitoring (SHM) is a new technology that will be increasingly applied at the industrial field as a potential approach to improve cost and convenience of structural inspection. Recently, the development of smart sensor is very active for real application. This study has focused on preparation and application study of SAL sensor. In order to detect elastic wave, smart piezoelectric sensor, SAL, is fabricated by using a piezoelectric element, shielding layer and protection layer. This protection layer plays an important role in a patched network of distributed piezoelectric sensor and shielding treatment. Four types of SAL sensor are designed/prepared/tested, and these details will be discussed in the paper. In this study, SAL sensor can be feasibly applied to perform structural health monitoring and to detect damage sources which result in elastic waves.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.23 no.4
• /
• pp.347-355
• /
• 2013

A damage estimation method for monopile support structure of offshore wind turbine using modal properties and committee of neural networks is presented for effective structural health monitoring. An analytical model for a monopile support structure is established, and the natural frequencies, mode shapes, and mode shape slopes for the support structure are calculated considering soil condition and added mass. The input to the neural networks consists of the modal properties and the output is composed of the stiffness indices of the support structure. Multiple neural networks are constructed and each individual network is trained independently with different initial synaptic weights. Then, the estimated stiffness indices from different neural networks are averaged. Ten damage cases are estimated using the proposed method, and the identified damage locations and severities agree reasonably well with the exact values. The accuracy of the estimation can be improved by applying the committee of neural networks which is a statistical approach averaging the damage indices in the functional space.

• Smart Structures and Systems
• /
• v.4 no.1
• /
• pp.47-66
• /
• 2008

The present work utilizes system identification technique for health monitoring of shear building, wherein Parametric State Space modeling has been adopted. The method requires input excitation to the structure and also output acceleration responses of both undamaged and damaged structure obtained from numerically simulated model. Modal parameters like eigen frequencies and eigen vectors have been extracted from the State Space model after introducing appropriate transformation. Least square technique has been utilized for the evaluation of the stiffness matrix after having obtained the modal matrix for the entire structure. Highly accurate values of stiffness of the structure could be evaluated corresponding to both the undamaged as well as damaged state of a structure, while considering noise in the simulated output response analogous to real time scenario. The damaged floor could also be located very conveniently and accurately by this adopted strategy. This method of damage detection can be applied in case of output acceleration responses recorded by sensors from the actual structure. Further, in case of even limited availability of sensors along the height of a multi-storeyed building, the methodology could yield very accurate information related to structural stiffness.

• Smart Structures and Systems
• /
• v.15 no.3
• /
• pp.807-830
• /
• 2015

When a building structure requires both health monitoring system and vibration control system, integrating the two systems together will be cost-effective and beneficial for creating a smart building structure with its own sensors (nervous system), processors (brain system), and actuators (muscular system). This paper presents a real-time integrated procedure to demonstrate how health monitoring and vibration control can be integrated in real time to accurately identify time-varying structural parameters and unknown excitations on one hand, and to optimally mitigate excessive vibration of the building structure on the other hand. The basic equations for the identification of time-varying structural parameters and unknown excitations of a semi-active damper-controlled building structure are first presented. The basic equations for semi-active vibration control of the building structure with time-varying structural parameters and unknown excitations are then put forward. The numerical algorithm is finally followed to show how the identification and the control can be performed simultaneously. The results from the numerical investigation of an example building demonstrate that the proposed method is feasible and accurate.

• Smart Structures and Systems
• /
• v.7 no.2
• /
• pp.83-102
• /
• 2011

As a testbed for various structural health monitoring (SHM) technologies, a super-tall structure - the 610 m-tall Guangzhou Television and Sightseeing Tower (GTST) in southern China - is currently under construction. This study aims to explore state-of-the-art wireless sensing technologies for monitoring the ambient vibration of such a super-tall structure during construction. The very nature of wireless sensing frees the system from the need for extensive cabling and renders the system suitable for use on construction sites where conditions continuously change. On the other hand, unique technical hurdles exist when deploying wireless sensors in real-life structural monitoring applications. For example, the low-frequency and low-amplitude ambient vibration of the GTST poses significant challenges to sensor signal conditioning and digitization. Reliable wireless transmission over long distances is another technical challenge when utilized in such a super-tall structure. In this study, wireless sensing measurements are conducted at multiple heights of the GTST tower. Data transmission between a wireless sensing device installed at the upper levels of the tower and a base station located at the ground level (a distance that exceeds 443 m) is implemented. To verify the quality of the wireless measurements, the wireless data is compared with data collected by a conventional cable-based monitoring system. This preliminary study demonstrates that wireless sensing technologies have the capability of monitoring the low-amplitude and low-frequency ambient vibration of a super-tall and slender structure like the GTST.

• Proceeding of KASS Symposium
• /
• 2008.05a
• /
• pp.189-194
• /
• 2008

The main object of this paper is that it's possible to monitoring the deformation of cable in the tensegrity structure. always monitoring system of Fiber Bragg Grating(FBG)Sensor is described. The measurement of parts on the cable is very important. We make an experiment with measuring deformation of cable in the tensegrity structure to the pressure conditions. In the result of experiment, the fiber sensors showed good response to the pressure conditions. Therefore, We could calculate the deformation of cable structure and be possible health monitoring of the tensegrity structure.

• Steel and Composite Structures
• /
• v.29 no.6
• /
• pp.703-716
• /
• 2018

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, $Nystr{\ddot{o}}m$ method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.

• Journal of the Korea Society for Simulation
• /
• v.19 no.2
• /
• pp.147-155
• /
• 2010

It often occur to nature disaster that like earthquake, typhoon, etc. around KOREA. A Haiti and Chile also metropolitan area of KOREA occur earthquake. in result, People consider nature disaster. Structures of present age are easily affected by nature disaster. So we are important that warn of dangerous situation as soon as possible. On this study, I introduce Integrated Health Monitoring System for Infra-structure. I develop Structure Health Monitoring System on web-site. Administrator always monitor structure on real-time using internet network. As Administrator using mobile device like PDA, Administrator always monitor structure. As using this system, Damage of nature disaster is minimized and is prevented post damage.

• Advanced Composite Materials
• /
• v.16 no.2
• /
• pp.135-149
• /
• 2007

In the automobile industry, structural health monitoring of fiber reinforced polymer composite parts is a widespread need for maintenance before breakdown of the functional elements or a complete vehicle. High performance sensors are generally used in many of the structural health monitoring operations. Within this study, a carbon fiber sewing thread has been used as a low cost laminate failure sensing element. The experimentation plan was set up according to the electrical conductance and flexibility of carbon fiber threads, advantages of preforming operations, and sewing mechanisms. The influence of the single thread damages by changing the electrical resistance and monitoring the impact location by using carbon thread sensors has been performed. Innovative utilization of relatively cost-effective carbon threads for monitoring the delamination of metallic inserts from the basic composite laminate structure is a highlighting feature of this study.