• Structural Monitoring and Maintenance
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• 제9권2호
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• pp.107-127
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• 2022

Structural health monitoring (SHM) has been related to damage identification with either operational loads or other environmental loading playing a significant complimentary role in terms of structural safety. In this study, a non-parametric method of time frequency analysis on the measurement is used to address the time-frequency representation for modal parameter estimation and system damage identification of structure. The method employs the wavelet decomposition of dynamic data by using the modified complex Morlet wavelet with variable central frequency (MCMW+VCF). Through detail discussion on the selection of model parameter in wavelet analysis, the method is applied to study the dynamic response of both steel structure and reinforced concrete frame under white noise excitation as well as earthquake excitation from shaking table test. Application of the method to building earthquake response measurement is also examined. It is shown that by using the spectrogram generated from MCMW+VCF method, with suitable selected model parameter, one can clearly identify the time-varying modal frequency of the reinforced concrete structure under earthquake excitation. Discussions on the advantages and disadvantages of the method through field experiments are also presented.

• Smart Structures and Systems
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• 제24권4호
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• pp.507-524
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• 2019

A Bayesian dynamic linear model (BDLM) is presented for a data-driven analysis for response prediction and load effect separation of a revolving auditorium structure, where the main loads are self-weight and dead loads, temperature load, and audience load. Analyses are carried out based on the long-term monitoring data for static strains on several key members of the structure. Three improvements are introduced to the ordinary regression BDLM, which are a classificatory regression term to address the temporary audience load effect, improved inference for the variance of observation noise to be updated continuously, and component discount factors for effective load effect separation. The effects of those improvements are evaluated regarding the root mean square errors, standard deviations, and 95% confidence intervals of the predictions. Bayes factors are used for evaluating the probability distributions of the predictions, which are essential to structural condition assessments, such as outlier identification and reliability analysis. The performance of the present BDLM has been successfully verified based on the simulated data and the real data obtained from the structural health monitoring system installed on the revolving structure.

• Smart Structures and Systems
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• 제16권4호
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• pp.607-621
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• 2015

Wireless sensor technology has been opened up numerous opportunities to advanced health and maintenance monitoring of civil infrastructure. Compare to the traditional tactics, it offers a better way of providing relevant information regarding the condition of building structure health at a lower price. Numerous domestic buildings, especially longer-span buildings have a low frequency response and challenging to measure using deployed numbers of sensors. The way the sensor nodes are connected plays an important role in providing the signals with required strengths. Out of many topologies, the dense and sparse topologies wireless sensor network were extensively used in sensor network applications for collecting health information. However, it is still unclear which topology is better for obtaining health information in terms of greatest components, node's size and degree. Theoretical and computational issues arising in the selection of the optimum topology sensor network for estimating coverage area with sensor placement in building structural monitoring are addressed. This work is an attempt to fill this gap in high-rise building structural health monitoring application. The result shows that, the sparse topology sensor network provides better performance compared with the dense topology network and would be a good choice for monitoring high-rise building structural health damage.

• Structural Monitoring and Maintenance
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• 제5권2호
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• pp.273-295
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• 2018

In this study, an investigation of a damage detection methodology for global condition assessment is presented. A particular emphasis is put on the utilization of wireless sensors for more practical, less time consuming, less expensive and safer monitoring and eventually maintenance purposes. Wireless sensors are deployed with a sensor roving technique to maintain a dense sensor field yet requiring fewer sensors. The time series analysis method called ARX models (Auto-Regressive models with eXogeneous input) for different sensor clusters is implemented for the exploration of artificially induced damage and their locations. The performance of the technique is verified by making use of the data sets acquired from a 4-span bridge-type steel structure in a controlled laboratory environment. In that, the free response vibration data of the structure for a specific sensor cluster is measured by both wired and wireless sensors and the acceleration output of each sensor is used as an input to ARX model to estimate the response of the reference channel of that cluster. Using both data types, the ARX based time series analysis method is shown to be effective for damage detection and localization along with the interpretations and conclusions.

• Smart Structures and Systems
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• 제4권6호
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• pp.795-807
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• 2008

A challenging problem in structural damage detection based on vibration data is the requirement of a large number of sensors and the numerical difficulty in obtaining reasonably accurate results when the system is large. To address this issue, the substructure identification approach may be used. Due to practical limitations, the response data are not available at all degrees of freedom of the structure and the external excitations may not be measured (or available). In this paper, an adaptive damage tracking technique, referred to as the sequential nonlinear least-square estimation with unknown inputs and unknown outputs (SNLSE-UI-UO) and the sub-structure approach are used to identify damages at critical locations (hot spots) of the complex structure. In our approach, only a limited number of response data are needed and the external excitations may not be measured, thus significantly reducing the number of sensors required and the corresponding computational efforts. The accuracy of the proposed approach is illustrated using a long-span truss with finite-element formulation and an 8-story nonlinear base-isolated building. Simulation results demonstrate that the proposed approach is capable of tracking the local structural damages without the global information of the entire structure, and it is suitable for local structural health monitoring.

• Structural Engineering and Mechanics
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• 제31권5호
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• pp.527-544
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• 2009

In this study, the periodic seismic performance evaluation scheme is proposed using a structural health monitoring system in terms of seismic fragility. An instrumented highway bridge is used to demonstrate the evaluation procedure involving (1) measuring ambient vibration of a bridge under general vehicle loadings, (2) identifying modal parameters from the measured acceleration data by applying output-only modal identification method, (3) updating a preliminary finite element model (obtained from structural design drawings) with the identified modal parameters using real-coded genetic algorithm, (4) analyzing nonlinear response time histories of the structure under earthquake excitations, and finally (5) developing fragility curves represented by a log-normal distribution function using maximum likelihood estimation. It is found that the seismic fragility of a highway bridge can be updated using extracted modal parameters and can also be monitored further by utilizing the instrumented structural health monitoring system.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 추계학술대회논문집
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• pp.619-624
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• 2004

In this research, the structural health monitoring method using wavelet packet analysis and artificial neural network (ANN) is developed. Wavelet packet Transform (WPT) is applied to the response acceleration of a 3 element-cantilever beam which is subjected to impulse load and Gaussian random load to decompose the response signal, then the energy of each component is calculated. The first ten largest components in magnitude among the decomposed components are selected as input to an ANN to identify the damage location and severity. This method successfully predicted the amount of damage in the structure when the structure is subjected to impulse load. However, when the beam is subjected to Gaussian random load which can be considered as ambient vibration it did not yield satisfactory results. This method is applicable to structures such as machinery gears that are subjected to repetitive loads.

• Structural Engineering and Mechanics
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• 제49권4호
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• pp.509-521
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• 2014

In this paper two protocols of Wireless Sensor Networks (WSN) are examined through both a simulation and a case study. The simulation was performed with the optimized network (OPNET) simulator while comparing the performance of the Ad-Hoc on demand Distance Vector (AODV) and the Dynamic Source Routing (DSR) protocols. This is compared and shown with real-world measurement of deflection from eight wireless sensor nodes. The wireless sensor response results were compared with accelerometer sensors for validation purposes. It was found that although the computer simulation suggests the AODV protocol is more accurate, in the case study no distinct difference was found. However, it was shown that AODV is still more beneficial in the field as it has a longer battery life enabling longer surveying times. This is a significant finding as a large factor in determining the use of wireless network sensors as a method of assessing structural response has been their short battery life. Thus if protocols which enhance battery life, such as the AODV protocol, are employed it may be possible in the future to couple wireless networks with solar power extending their monitoring periods.

• Wind and Structures
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• 제15권4호
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• pp.285-299
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• 2012

This paper describes the development and calibration of a structural monitoring system installed in a 80 meters high steel wind tower supporting a 2.1 MW turbine Wind Class III IEC2a erected in the central part of Portugal. The several signals are measured at four different levels and include accelerations, strains on the tower wall and inside the connection bolts, inclinations and temperature. In order to correlate measurements with the wind velocity and direction and with the turbine operational parameters the corresponding signals are obtained directly from the turbine own monitoring system and are incorporated in the developed system. Results from the system calibration, the structural identification and the initial period of data acquisition are presented in this paper.

• 한국소음진동공학회논문집
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• 제25권12호
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• pp.843-855
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• 2015

This study developed a band-pass filter based artificial filter bank(BAFB) based on that in order to efficiently obtain the significant dynamic responses. The BAFB was then optimized about the El-centro earthquake wave which was often used in the construction research, and the software implementation of BAFB was finally embedded in the wireless unified management system(WiUMS). For the evaluation of the developed BAFB, a real time dynamic response experiment was performed on a cable-stayed bridge model, and the response of the cable-stayed bridge model was measured using both the traditional wired system and the developed BAFB-based WiUMS. The experiment results showed that the compressed dynamic response acquired by the BAFB-based WiUMS matched significantly with that of the traditional wired system while still carrying sufficient modal information of the cable-stayed bridge. Finally, the developed BAFB was able to reconstruct or re-sample the dynamic response wholly from the compressed response signal, and it can be applied as a new kind of measurement system for a wireless sensor networks based structural health monitoring system that secures both economy and efficiency.