A Cyber-Physical System for Girder Hoisting Monitoring Based on Smartphones

Han, Ruonan; Zhao, Xuefeng; Yu, Yan; Guan, Quanhua; Hu, Wei; Li, Mingchu

doi:10.3390/s16071048

Cited by 34 publications

(12 citation statements)

References 27 publications

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“…Combining smartphones with structural health monitoring not only can improve the possibility of public participation but also can greatly reduce the requirements of professional equipment. Zhao et al applied smartphones in the field of structural health monitoring. Their experimental results showed that various sensors embedded in the smartphone can meet the requirements of structural damage detection.…”

Section: Methodsmentioning

confidence: 99%

Bolt loosening angle detection technology using deep learning

Zhao

Zhang

Wang

2018

Struct Control Health Monit

Self Cite

102

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As an important part of the steel structure, the bolt damage will affect the safety of the structure and even cause severe accidents. However, it is difficult to detect the bolt loosening from the perspective of the conventional dynamics, due to the complex vibration characteristics of the bolt joints. In order to detect structural damage intuitively, machine vision has been introduced into the field of structural health monitoring. Therefore, this paper combines deep learning and machine vision to propose a bolt loosening angle detection technology. First, the data sets with bolts were collected and divided into training sets, validation sets, and test sets. Second, the data sets were trained using Single Shot MultiBox Detector. And the recognition accuracy of the model was evaluated, which can reach 0.914. Thereafter, the images obtained from different angles and lighting conditions were detected by the training model;the results showed that this method still has high recognition accuracy and meets the requirements of engineering. Finally, the training model was migrated to the smartphone to achieve quick and simple bolt loosening monitoring.KEYWORDS bolt loosening angle, deep learning, machine vision, smartphone, SSD | INTRODUCTIONWith the advancement of steel structures, it has developed rapidly in high-rise and supertall buildings. Compared with the concrete structure, the steel structure adopts steel plate instead of reinforced concrete, which has higher strength and better seismic performance. And its components can be prefabricated at the factory, which greatly reduces the construction period. As an important joint, bolts are widely used in steel structures. Thus, the bolt loosening damage not only affects the safety of the building, but it also causes severe accidents. The conventional structural health monitoring methods mainly collects acceleration, 1 displacement, 2 and strain. 3 Then these characteristics are analyzed through wavelet transform 4,5 and Bayesian transformation 6-8 to obtain the location and size of the damage. However, these methods rely on a large number of distributed sensors, and the feature extraction process need to intervention. The damage feature parameters directly determine the results of the damage detection. However, machine vision can complete damage monitoring from another perspective. Many damages on the structure can be detected more intuitively by vision. The bolt damage is mainly divided into rusty, shedding, and loosening, of which the most difficult to detect is loosening. To date, many researchers also used machine vision methods to monitor bolt loosening damage. For instance, Park et al. 9 proposed a bolt loosening angle detection method using Hough transformation, which detects bolt

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Section: Methodsmentioning

confidence: 99%

Bolt loosening angle detection technology using deep learning

Zhao

Zhang

Wang

2018

Struct Control Health Monit

Self Cite

102

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“…Short-term bridge monitoring (e.g., during the construction phase) is important for quality assurance as demonstrated by Han et al (2016). They proposed to employ smartphones for girder hoisting monitoring.…”

Section: Contact Sensorsmentioning

confidence: 99%

Health monitoring of bridges

Kromanis

2020

Start-Up Creation

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“…Zhang et al suggested a smart health system which stores and analyzes large amounts of information such as environmental sensors, biosensors, and personal information collected from cameras in the healthcare domain [ 7 ]. To secure the operator hoisting the girder in the offshore, Han monitored the location information through the smartphone and built an alarm system based on it [ 8 ]. Jia et al proposed a tracking method using the Wi-Fi of a smartphone rather than GPS or Bluetooth to track the user’s behavior [ 9 ].…”

Section: Related Workmentioning

confidence: 99%

Sensor Information Fusion by Integrated AI to Control Public Emotion in a Cyber-Physical Environment

Choi

Cho

2018

Sensors

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The cyber-physical system (CPS) is a next-generation smart system that combines computing with physical space. It has been applied in various fields because the uncertainty of the physical world can be ideally controlled using cyber technology. In terms of environmental control, studies have been conducted to enhance the effectiveness of the service by inducing ideal emotions in the service space. This paper proposes a CPS control system for inducing emotion based on multiple sensors. The CPS can expand the constrained environmental sensors of the physical space variously by combining the virtual space with the physical space. The cyber space is constructed in a Unity 3D space that can be experienced through virtual reality devices. We collect the temperature, humidity, dust concentration, and current emotion in the physical space as an environmental control elements, and the control illumination, color temperature, video, sound and volume in the cyber space. The proposed system consists of an emotion prediction module using modular Bayesian networks and an optimal stimulus decision module for deriving the predicted emotion to the target emotion based on utility theory and reinforcement learning. To verify the system, the performance is evaluated using the data collected from real situations.

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A Cyber-Physical System for Girder Hoisting Monitoring Based on Smartphones

Cited by 34 publications

References 27 publications

Bolt loosening angle detection technology using deep learning

Bolt loosening angle detection technology using deep learning

Health monitoring of bridges

Sensor Information Fusion by Integrated AI to Control Public Emotion in a Cyber-Physical Environment

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