• Smart Structures and Systems
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• v.28 no.6
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.

• Geomechanics and Engineering
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• v.30 no.3
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• pp.313-324
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• 2022

With the development of the economy, people's utilization of underground space are also improved, and a large number of cities have begun to build subways to relieve traffic pressure. The choice of subway station construction method is crucial. If an inappropriate construction method is selected, it will not only waste costs but also cause excessive deformation that may also threaten construction safety. In this paper, a subway station construction scheme selects model based on the AHP-fuzzy comprehensive evaluation. The rationality of the model is verified using numerical simulation and monitoring measurement data. Firstly, considering the economy and safety, a comprehensive evaluation system is established by selecting several indicators. Then, the analytic hierarchy process is used to determine the weight of the evaluation index, and the dimensionless membership in the fuzzy comprehensive evaluation method is used to evaluate the advantages and disadvantages of the construction method. Finally, the method is applied to Liaoyang east road station of Qingdao metro Line 2, and the results are verified by numerical simulation and monitoring measurement data. The results show that the model is scientific, practical and applicable.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.05a
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• pp.359-362
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• 2005

Self diagnosis monitoring system is defined as concrete structural carbon and glass hybrid fiber materials, in response to the change in external disturbance and environments, toward structural safety and serviceability as well as the extension of structural service life. In this study, carbon and glass hybrid fiber materials were investigated fundamentally for the applicability of self diagnosis in smart concrete structural system as embedded functions of sensors.

• Journal of the Korea institute for structural maintenance and inspection
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• v.6 no.3
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• pp.103-110
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• 2002

Maintenance monitoring of the tunnel which offers continuous data during and after tunneling has been applying to tunnels in order to meet the maintenance of tunnel and to confirm continuous security of the tunnel after tunneling. But, the maintenance monitoring of tunnel results for long period is not easy to find, and moreover, the rational analysis method on tunnel monitoring has not been established yet. In this study, the relationships between displacement and stress of the tunnel concrete lining using various analysis methods are compared with maintenance monitoring. The tunnel behavior were measured in the subway tunnel passing comparative soft the weathering and analyzed both security and mechanical characteristics of the tunnel concrete lining. Also, analyzed relationship between residual water pressure applied on tunnel design and one obtained by monitoring.

• Wind and Structures
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• v.21 no.6
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• pp.693-707
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• 2015

The efficiency of wind turbines (WT) is primarily reflected in their ability to generate electricity at any time. Downtimes of WTs due to "conventional" inspections are cost-intensive and undesirable for investors. For this reason, there is a need for structural health monitoring (SHM) systems, to enable service and maintenance on demand and to increase the inspection intervals. In general, monitoring increases the cost effectiveness of WTs. This publication concentrates on the application of two vibration-based SHM algorithms for stability and structural change monitoring of offshore WTs. Only data driven, output-only algorithms based on stochastic subspace identification (SSI) in time domain are considered. The centerpiece of this paper deals with the rough mathematical description of the dynamic behavior of offshore WTs and with the basic presentation of stochastic subspace-based algorithms and their application to these structures. Due to the early stage of the industrial application of SHM on offshore WT on the one side and the required confidentiality to the plant manufacturer and operator on the other side, up to now it is not possible to analyze different isolated structural damages resp. changes in a systematic manner, directly by means of in-situ measurement and to make these "acknowledgements" publicly available. For this reason, the sensitivity of the methods for monitoring purposes are demonstrated through their application on long time measurements from a 1:10 large scale test rig of an offshore WT under different conditions: undamaged, different levels of loosened bolt connections between tower parts, different levels of fouling, scouring and structure inclination. The limitation and further requirements for the approaches and their applicability on real foundations are discussed along the paper.

• Smart Structures and Systems
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• v.10 no.3
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• pp.271-298
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• 2012

Researchers have made significant progress in recent years towards realizing effective structural health monitoring (SHM) utilizing wireless smart sensor networks (WSSNs). These efforts have focused on improving the performance and robustness of such networks to achieve high quality data acquisition and distributed, in-network processing. One of the primary challenges still facing the use of smart sensors for long-term monitoring deployments is their limited power resources. Periodically accessing the sensor nodes to change batteries is not feasible or economical in many deployment cases. While energy harvesting techniques show promise for prolonging unattended network life, low power design and operation are still critically important. This research presents the WiSeMote: a new, fully integrated ultra-low power wireless smart sensor node and a flexible base station, both designed for long-term SHM deployments. The power consumption of the sensor nodes and base station has been minimized through careful hardware selection and the implementation of power-aware network software, without sacrificing flexibility and functionality.

• Structural Monitoring and Maintenance
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• v.1 no.2
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• pp.197-211
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• 2014

An investigation of tunnel linings is performed at two tunnels in the US using complimentary noncontact techniques: air-coupled ground penetrating radar (GPR), and a vehicle-mounted scanning system (SPACETEC) that combines laser, visual, and infrared thermography scanning methods. This paper shows that a combination of such techniques can maximize inspection coverage in a comprehensive and efficient manner. Since ground-truth is typically not available in public tunnel field evaluations, the noncontact techniques used are compared with two reliable in-depth contact nondestructive testing methods: ground-coupled GPR and ultrasonic tomography. The noncontact techniques are used to identify and locate the reinforcement mesh, structural steel ribs, internal layer interfaces, shallow delamination, and tile debonding. It is shown that this combination of methods can be used synergistically to provide tunnel owners with a comprehensive and efficient approach for monitoring tunnel lining conditions.

• Journal of the Korea institute for structural maintenance and inspection
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• v.2 no.3
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• pp.194-204
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• 1998

This study was performed to suggest the proper maintenance method for the deteriorated gerber type PC box girder bridge by using the long term displacement monitoring data. For this study, the monitoring system which can measure the long term displacement and the concrete surface temperature was designed and operated. From the measurement and structural analysis results, the cause of the permanent deformation which the bridge has already was estimated, and based on this result, the allowable permanent displacement value at the hinge was suggested. From this study, it was known that the long term monitoring system can be applied to the active maintenance of the deteriorated bridge and the suggested allowable permanent displacement could be used for the maintenance of the bridge.

• Journal of Communications and Networks
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• v.11 no.4
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• pp.350-358
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• 2009

In a wireless sensor network (WSN) setting, this paper presents a distributed decision-making framework and illustrates its application in an online structural health monitoring (SHM) system. The objective is to recover a damage severity vector, which identifies, localizes, and quantifies damages in a structure, via distributive and collaborative decision-making among wireless sensors. Observing the fact that damages are generally scarce in a structure, this paper develops a nonlinear 0-norm minimization formulation to recover the sparse damage severity vector, then relaxes it to a linear and distributively tractable one. An optimal algorithm based on the alternating direction method of multipliers (ADMM) and a heuristic distributed linear programming (DLP) algorithm are proposed to estimate the damage severity vector distributively. By limiting sensors to exchange information among neighboring sensors, the distributed decision-making algorithms reduce communication costs, thus alleviate the channel interference and prolong the network lifetime. Simulation results in monitoring a steel frame structure prove the effectiveness of the proposed algorithms.

• Wind and Structures
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• v.15 no.4
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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.