• Structural Engineering and Mechanics
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• v.63 no.6
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• pp.809-824
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• 2017

The accurate evaluation of wind characteristics and wind-induced structural responses during a typhoon is of significant importance for bridge design and safety assessment. This paper presents an expectation maximization (EM) algorithm-based angular-linear approach for probabilistic modeling of field-measured wind characteristics. The proposed method has been applied to model the wind speed and direction data during typhoons recorded by the structural health monitoring (SHM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. In the summer of 2015, three typhoons, i.e., Typhoon Chan-hom, Typhoon Soudelor and Typhoon Goni, made landfall in the east of China and then struck the Jiubao Bridge. By analyzing the wind monitoring data such as the wind speed and direction measured by three anemometers during typhoons, the wind characteristics during typhoons are derived, including the average wind speed and direction, turbulence intensity, gust factor, turbulence integral scale, and power spectral density (PSD). An EM algorithm-based angular-linear modeling approach is proposed for modeling the joint distribution of the wind speed and direction. For the marginal distribution of the wind speed, the finite mixture of two-parameter Weibull distribution is employed, and the finite mixture of von Mises distribution is used to represent the wind direction. The parameters of each distribution model are estimated by use of the EM algorithm, and the optimal model is determined by the values of $R^2$ statistic and the Akaike's information criterion (AIC). The results indicate that the stochastic properties of the wind field around the bridge site during typhoons are effectively characterized by the proposed EM algorithm-based angular-linear modeling approach. The formulated joint distribution of the wind speed and direction can serve as a solid foundation for the purpose of accurately evaluating the typhoon-induced fatigue damage of long-span bridges.

• Smart Structures and Systems
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• v.29 no.3
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• pp.475-484
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• 2022

The loss of cable tension for civil infrastructure reduces structural bearing capacity and causes harmful deformation of structures. Currently, most of the structural health monitoring (SHM) approaches for cables rely on contact transducers. This paper proposes a cable tension identification technology using percussion sound, which provides a fast determination of steel cable tension without physical contact between cables and sensors. Notably, inspired by the concept of tensioning strings for piano tuning, this proposed technology predicts cable tension value by deep learning assisted classification of "percussion" sound from tapping a steel cable. To simulate the non-linear mapping of human ears to sound and to better quantify the minor changes in the high-frequency bands of the sound spectrum generated by percussions, Mel-frequency cepstral coefficients (MFCCs) were extracted as acoustic features to train the deep learning network. A convolutional neural network (CNN) with four convolutional layers and two global pooling layers was employed to identify the cable tension in a certain designed range. Moreover, theoretical and finite element methods (FEM) were conducted to prove the feasibility of the proposed technology. Finally, the identification performance of the proposed technology was experimentally investigated. Overall, results show that the proposed percussion-based technology has great potentials for estimating cable tension for in-situ structural safety assessment.

• Smart Structures and Systems
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• v.3 no.3
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• pp.299-320
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• 2007

Smart sensors densely distributed over structures can provide rich information for structural monitoring using their onboard wireless communication and computational capabilities. However, issues such as time synchronization error, data loss, and dealing with large amounts of harvested data have limited the implementation of full-fledged systems. Limited network resources (e.g. battery power, storage space, bandwidth, etc.) make these issues quite challenging. This paper first investigates the effects of time synchronization error and data loss, aiming to clarify requirements on synchronization accuracy and communication reliability in SHM applications. Coordinated computing is then examined as a way to manage large amounts of data.

• Journal of the Korean Society for Nondestructive Testing
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• v.27 no.3
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• pp.268-279
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• 2007

The reliability of cold-bonding repair technique of carbon-fiber reinforced plastics (CFRP) laminates, often used as a temporary repair for the airplane maintenance, has been evaluated during cyclic loading and localized heating by nondestructive methods. Major concern was given to the evolution of damage after repair in the form of delaminations due to localized heating and cyclic loading combined. An area of interest both on the specimen repaired by cold-bonding and the specimen without repair where delaminations were induced by localized heating and cyclic loading was monitored by acoustic emission (AE) testing and further examined by pitch-catch low-frequency bond testing, and pulse-echo high-frequency ultrasonic testing. The results showed that the reliability of cold-bonding repair would be significantly reduced by the localized heating and cyclic loading combined rather than by the cyclic loading only. AE monitoring appeared to be an effective and reliable tool to monitor the integrity of temporarily repaired CFRP laminates in terms of the structural health monitoring (SHM) philosophy.

• Smart Structures and Systems
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• v.5 no.4
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• pp.345-355
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• 2009

This article describes the development of a fiber optic accelerometer based on Fiber Bragg Gratings (FBG). The accelerometer utilizes the stiffness of the optical fiber and a lumped mass in the design. Acceleration is measured by the FBG in response to the vibration of the fiber optic mass system. The wavelength shift of FBG is proportional to the change in acceleration, and the gauge factor pertains to the shift in wavelength as a function of acceleration. Low frequency version of the accelerometer was developed for applications in monitoring bridges. The accelerometer was first evaluated in laboratory settings and then employed in a demonstration project for condition assessment of a bridge. Laboratory experiments involved evaluation of the sensitivity and resolution of measurements under a series of low frequency low amplitude conditions. The main feature of this accelerometer is single channel multiplexing capability rendering the system highly practical for application in condition assessment of bridges. This feature of the accelerometer was evaluated by using the system during ambient vibration tests of a bridge. The Frequency Domain Decomposition method was employed to identify the mode shapes and natural frequencies of the bridge. Results were compared with the data acquired from the conventional accelerometers.

• Earthquakes and Structures
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• v.8 no.4
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• pp.859-887
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• 2015

Structural health monitoring (SHM) has gained in popularity in recent years since it can assess the performance and condition of instrumented structures in real time and provide valuable information to the asset's manager and owner. Operational modal analysis plays an important role in SHM and it involves the determination of natural frequencies, damping ratios and mode shapes of a constructed structure based on measured dynamic data. This paper presents the operational modal analysis and seismic response characterization of the Tsing Ma Suspension Bridge of 2,160 m long subjected to different earthquake events. Three kinds of events, i.e., short-distance, middle-distance and long-distance earthquakes are taken into account. A fast Bayesian modal identification method is used to carry out the operational modal analysis. The modal properties of the bridge are identified and compared by use of the field monitoring data acquired before and after the earthquake for each type of the events. Research emphasis is given on identifying the predominant modes of the seismic responses in the deck during short-distance, middle-distance and long-distance earthquakes, respectively, and characterizing the response pattern of various structural portions (deck, towers, main cables, etc.) under different types of earthquakes. Since the bridge is over 2,000 m long, the seismic wave would arrive at the tower/anchorage basements of the two side spans at different time instants. The behaviors of structural dynamic responses on the Tsing Yi side span and on the Ma Wan side span under each type of the earthquake events are compared. The results obtained from this study would be beneficial to the seismic design of future long-span bridges to be built around Hong Kong (e.g., the Hong Kong-Zhuhai-Macau Bridge).

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.3
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• pp.241-247
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• 2015

Deflection and deformation occur easily in structures with long length, such as bridges and pipelines. Shape monitoring is required for ensuring their structural health. A fiber Bragg grating (FBG) sensor can be used for monitoring a large-scale structure because of its advantage of multiplexing. In this study, FBG sensors were used for monitoring a composite beam structure, and its strains were measured at multiple points. Thereafter, a shape estimation technique based on the strains was studied. Particularly, a three-dimensional shape estimation technique was proposed for accurate structural health monitoring. A simple experiment was conducted to verify the performance of the shape estimation technique. The result revealed that the estimated shape of the composite beam structure was in agreement with the actual shape obtained after the deformation of the specimen. Additionally, the deflection at a specific point was verified by comparing the estimated and actual deformations measured using a micrometer.

• Smart Structures and Systems
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• v.3 no.3
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• pp.373-384
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• 2007

For structural health monitoring (SHM) of civil infrastructures, displacement is a good descriptor of the structural behavior under all the potential disturbances. However, it is not easy to measure displacement of civil infrastructures, since the conventional sensors need a reference point, and inaccessibility to the reference point is sometimes caused by the geographic conditions, such as a highway or river under a bridge, which makes installation of measuring devices time-consuming and costly, if not impossible. To resolve this issue, a visionbased real-time displacement measurement system using digital image processing techniques is developed. The effectiveness of the proposed system was verified by comparing the load carrying capacities of a steel-plate girder bridge obtained from the conventional sensor and the present system. Further, to simultaneously measure multiple points, a synchronized vision-based system is developed using master/slave system with wireless data communication. For the purpose of verification, the measured displacement by a synchronized vision-based system was compared with the data measured by conventional contact-type sensors, linear variable differential transformers (LVDT) from a laboratory test.

• Journal of the Korea institute for structural maintenance and inspection
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• v.17 no.2
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• pp.1-9
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• 2013

In recent years, numerous mega-size and complex civil infrastructures have been constructed worldwide. For the more precise construction and maintenance process management of these civil infrastructures, the application of a variety of smart sensor-based structural health monitoring (SHM) systems is required. The efficient management of both sensors and collected databases is also very important. Recently, several kinds of database access technologies using Quick Response (QR) code and Augmented Reality (AR) applications have been developed. These technologies provide software tools incorporated with mobile devices, such as smart phone, tablet PC and smart pad systems, so that databases can be accessed very quickly and easily. In this paper, an AR-based structural health monitoring technique is suggested for sensor management and the efficient access of databases collected from sensor networks that are distributed at target structures. The global positioning system (GPS) in mobile devices simultaneously recognizes the user location and sensor location, and calculates the distance between the two locations. In addition, the processed health monitoring results are sent from a main server to the user's mobile device, via the RSS (really simple syndication) feed format. It can be confirmed that the AR-based structural health monitoring technique is very useful for the real-time construction process management of numerous mega-size and complex civil infrastructures.

• Smart Structures and Systems
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• v.32 no.4
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• pp.267-279
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• 2023

Data modelling and interpretation for structural health monitoring (SHM) field data are critical for evaluating structural performance and quantifying the vulnerability of infrastructure systems. In order to improve the data modelling accuracy, and extend the application range from data regression analysis to out-of-sample forecasting analysis, an improved most likely heteroscedastic Gaussian process (iMLHGP) methodology is proposed in this study by the incorporation of the outof-sample forecasting algorithm. The proposed iMLHGP method overcomes this limitation of constant variance of Gaussian process (GP), and can be used for estimating non-stationary typhoon-induced response statistics with high volatility. The first attempt at performing data regression and forecasting analysis on structural responses using the proposed iMLHGP method has been presented by applying it to real-world filed SHM data from an instrumented cable-stay bridge during typhoon events. Uncertainty quantification and correlation analysis were also carried out to investigate the influence of typhoons on bridge strain data. Results show that the iMLHGP method has high accuracy in both regression and out-of-sample forecasting. The iMLHGP framework takes both data heteroscedasticity and accurate analytical processing of noise variance (replace with a point estimation on the most likely value) into account to avoid the intensive computational effort. According to uncertainty quantification and correlation analysis results, the uncertainties of strain measurements are affected by both traffic and wind speed. The overall change of bridge strain is affected by temperature, and the local fluctuation is greatly affected by wind speed in typhoon conditions.