• Smart Structures and Systems
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• 제19권6호
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• pp.679-694
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• 2017

The complexity, enlargement and irregularity of structures and multi-directional dynamic loads acting on the structures can lead to unexpected structural behavior, such as torsion. Continuous torsion of the structure causes unexpected changes in the structure's stress distribution, reduces the performance of the structural members, and shortens the structure's lifespan. Therefore, a method of monitoring the torsional behavior is required to ensure structural safety. Structural torsion typically occurs accompanied by displacement, but no model has yet been developed to measure this type of structural response. This research proposes a model for measuring dynamic torsional response of structure accompanied by displacement and for identifying the torsional modal parameter using vision-based displacement measurement equipment, a motion capture system (MCS). In the present model, dynamic torsional responses including pure rotation and translation displacements are measured and used to calculate the torsional angle and displacements. To apply the proposed model, vibration tests for a shear-type structure were performed. The torsional responses were obtained from measured dynamic displacements. The torsional angle and displacements obtained by the proposed model using MCS were compared with the torsional response measured using laser displacement sensors (LDSs), which have been widely used for displacement measurement. In addition, torsional modal parameters were obtained using the dynamic torsional angle and displacements obtained from the tests.

• Smart Structures and Systems
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• 제30권6호
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• Smart Structures and Systems
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• 제12권3_4호
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• pp.381-397
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• 2013

In this paper, wireless health monitoring of stay cables using piezoelectric strain sensors and a smart skin technique is presented. For the cables, tension forces are estimated to examine their health status from vibration features with consideration of temperature effects. The following approaches are implemented to achieve the objective. Firstly, the tension force estimation utilizing the piezoelectric sensor-embedded smart skin is presented. A temperature correlation model to recalculate the tension force at a temperature of interest is designed by correlating the change in cable's dynamic features and temperature variation. Secondly, the wireless health monitoring system for stay cables is described. A piezoelectric strain sensor node and a tension force monitoring software which is embedded in the sensor are designed. Finally, the feasibility of the proposed monitoring technique is evaluated on stay cables of the Hwamyung Grand Bridge in Busan, Korea.

• Structural Monitoring and Maintenance
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• 제2권3호
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• pp.199-211
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• 2015

The safety and performance of bridges could be monitored and evaluated by Structural Health Monitoring (SHM) systems. These systems try to identify and locate the damages in a structure and estimate their severities. Current SHM systems are applied to a single bridge, and they have not been used to monitor the structural condition of a network of bridges. This paper propose a new method which will be used in Synthetic Rating Procedures (SRP) developed by the authors of this paper and utilizes SHM systems for monitoring and evaluating the condition of a network of bridges. Synthetic rating procedures are used to assess the condition of a network of bridges and identify their ratings. As an additional part of the SRP, the method proposed in this paper can continuously monitor the behaviour of a network of bridges and therefore it can assist to prevent the sudden collapses of bridges or the disruptions to their serviceability. The method could be an important part of a bridge management system (BMS) for managers and engineers who work on condition assessment of a network of bridges.

• Smart Structures and Systems
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• 제31권2호
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• pp.183-197
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• 2023

For long-span bridges with a structural health monitoring (SHM) system, environmental temperature-driven responses are proved to be a main component in measurements. However, anomalous structural behavior may be hidden incomplicated recorded data. In order to receive reliable assessment of structural performance, it is important to study therelationship between temperature and monitoring data. This paper presents an application of the cointegration based methodology to detect anomalies that may be masked by temperature effects and then forecast the temperature-induced deflection (TID) of long-span suspension bridges. Firstly, temperature effects on girder deflection are analyzed with fieldmeasured data of a suspension bridge. Subsequently, the cointegration testing procedure is conducted. A threshold-based anomaly detection framework that eliminates the influence of environmental temperature is also proposed. The cointegrated residual series is extracted as the index to monitor anomaly events in bridges. Then, wavelet separation method is used to obtain TIDs from recorded data. Combining cointegration theory with autoregressive moving average (ARMA) model, TIDs for longspan bridges are modeled and forecasted. Finally, in-situ measurements of Xihoumen Bridge are adopted as an example to demonstrate the effectiveness of the cointegration based approach. In conclusion, the proposed method is practical for actual structures which ensures the efficient management and maintenance based on monitoring data.

• 한국전산구조공학회논문집
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• 제33권2호
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• pp.113-119
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• 2020

사물인터넷 기반의 센서네트워크는 저렴한 비용으로 효율적으로 교량 등의 시설물 유지관리에 적용할 수 있는 한 방안이다. 본 연구에서는 사물인터넷 통신의 하나인, LoRa LPWAN 기반으로 교량 구조건전성모니터링을 위한 시스템을 개발하기 위해서 케이블 장력 모니터링을 위한 센서보드, 기존 계측 센서들과 함께 센서네트워크를 구축하기 위한 DAQ 보드, 데이터들 처리하고 LoRa 통신을 위한 스마트센서노드를 설계 및 제작하였으며 모니터링을 위한 센서네트워크를 구축하였다. 또한 본 시스템의 성능검증을 위해 영광대교에 Test Bed를 구축하여 교량 구조건전성 모니터링을 위한 센서네트워크에 적용가능성 여부를 살펴보았다. Test Bed 검증 결과 LoRa LPWAN 기반 센서네트워크는 데이터 전송률, 정확도, 경제성면에서 교량 구조 건전성 모니터링의 기술 중에 하나로 적용될 수 있으며, 향후 교량구조물 뿐만 아니라 다양한 공공기반 시설물에 유지관리를 위한 시스템으로 보급될 수 있기를 기대한다.

• Structural Monitoring and Maintenance
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• 제1권2호
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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.

• Smart Structures and Systems
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• 제10권4_5호
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• pp.313-329
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• 2012

A challenging issue in design and implementation of an effective structural health monitoring (SHM) system is to determine where a number of sensors are properly installed. In this paper, research on the optimal sensor placement (OSP) is carried out on the Canton Tower (formerly named Guangzhou New Television Tower) of 610 m high. To avoid the intensive computationally-demanding problem caused by tens of thousands of degrees of freedom (DOFs) involved in the dynamic analysis, the three dimension finite element (FE) model of the Canton Tower is first simplified to a system with less DOFs. Considering that the sensors can be physically arranged only in the translational DOFs of the structure, but not in the rotational DOFs, a new method of taking the horizontal DOF as the master DOF and rotational DOF as the slave DOF, and reducing the slave DOF by model reduction is proposed. The reduced model is obtained by IIRS method and compared with the models reduced by Guyan, Kuhar, and IRS methods. Finally, the OSP of the Canton Tower is obtained by a kind of dual-structure coding based generalized genetic algorithm (GGA).

• Smart Structures and Systems
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• 제15권1호
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• pp.191-207
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• 2015

Proper placement of sensors plays a key role in construction and implementation of an effective structural health monitoring (SHM) system. This paper proposes a novel methodology called the distributed monkey algorithm (DMA) for the optimum design of SHM system sensor arrays. Different from the existing algorithms, the dual-structure coding method is adopted for the representation of design variables and the single large population is partitioned into subsets and each subpopulation searches the space in different directions separately, leading to quicker convergence and higher searching capability. After the personal areas of all subpopulations have been finished, the initial optimal solutions in every subpopulation are extracted and reordered into a new subpopulation, and the harmony search algorithm (HSA) is incorporated to find the final optimal solution. A computational case of a high-rise building has been implemented to demonstrate the effectiveness of the proposed method. Investigations have clearly suggested that the proposed DMA is simple in concept, few in parameters, easy in implementation, and could generate sensor configurations superior to other conventional algorithms both in terms of generating optimal solutions as well as faster convergence.

• 한국구조물진단유지관리공학회지
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• 제20권1호
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• pp.42-45
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• 2016