Real-time image processing for non-contact monitoring of dynamic displacements using smartphone technologies

Min, Jae Hong; Gelo, Nikolas J.; Jo, Hongki

doi:10.1117/12.2219418

Cited by 10 publications

(10 citation statements)

References 4 publications

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“…They used a smartphone and a wireless inclinometer to record the swing motion of a pendulum model. Min et al (2016) developed a smartphone application to measure dynamic displacements and process them in real time. The system allows up to 240 frames per second for displacement calculation and real-time display.…”

Section: Introductionmentioning

confidence: 99%

Reference-free dynamic displacements of railroad bridges using low-cost sensors

Gomez

Ozdagli

Moreu

2017

Journal of Intelligent Material Systems and Structures

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Displacements of railroad bridges under service loads are important parameters in assessing bridge conditions and risk of train derailment, according to railroad bridge managers. Measuring bridge responses in the field is often expensive and challenging due to the high costs of sensing equipment. Consequently, railroad bridge managers typically rent or subcontract field measurements to others or choose not to collect dynamic data in the field and make visual inspections. This article studies the use of a low-cost data acquisition platform to measure reference-free dynamic displacements of railroad bridges by combining low-cost microcontrollers and accelerometers. Researchers used off-the-shelf systems to measure accelerations and reconstructed reference-free displacements from several railroad bridge crossing events by running trains with different levels of serviceability in the laboratory. The results obtained from the proposed low-cost sensors were compared with those of commercial sensing equipment. The results show that low-cost sensors and commercial sensing systems have comparable accuracy. The results of this study show that the proposed platform estimates reference-free displacements with a peak error between 20% and 30% and a root mean square error between 10% and 20%, which is similar to commercial structural health monitoring systems. The proposed low-cost system is approximately 300 times less expensive than the commercial sensing equipment. The ultimate goal of this research is to increase the intelligent assessment of bridges by training owners and inspectors to collect dynamic data of their interest with their own resources.

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Section: Introductionmentioning

confidence: 99%

Reference-free dynamic displacements of railroad bridges using low-cost sensors

Gomez

Ozdagli

Moreu

2017

Journal of Intelligent Material Systems and Structures

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“…Feng et al [42] and Ozer et al [43] developed a crowdsourcing platform for SHM and a post-event damage assessment app. Min et al [44] developed a smartphone application called RIRO to measure absolute dynamic displacements by processing image frames of a color-patterned target. Oraczewski and Staszewski [45] developed a platform for crack detection based on nonlinear acoustics and validated the system using a simple example of fatigue crack detection in aluminum plates.…”

Section: Introductionmentioning

confidence: 99%

Shaking Table Tests and Validation of Multi-Modal Sensing and Damage Detection Using Smartphones

Han

Zhao

2021

Buildings

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Structural health monitoring (SHM) systems using modal- and vibration-based methods, particularly wireless systems, have been widely investigated in relation to the monitoring of damage states in civil infrastructures such as bridges and buildings. Unlike many current efforts in developing wireless sensors, one can instead leverage the suite of sensors, network transmission, data storage, and embedded processing capabilities built into modern smartphones for SHM. The objective of this work was to assess and validate the use of smartphones for the monitoring of artificial damage states in a three-story steel frame model subjected to shaking table-induced earthquake excitations. The steel frame was a 2D structure with six rotary viscous dampers installed at the beam–column joints, which were used for simulating different damage states at their respective locations; the columns were also replaced with ones of reduced cross-sectional areas to further emulate damage. In addition to instrumenting the frame with conventional tethered sensors, Apple iPhones (pre-loaded with customized smartphone apps to record acceleration and inter-story displacement) were also installed. Shaking table tests were then conducted on the undamaged and damaged frames, while conventional sensors’ and smartphones’ responses were collected and compared. Wavelet packet decomposition was employed to analyze the acceleration data to detect damage in two different cases. Structural displacements were also computed from acceleration measurements and compared with displacement measurements to further validate the quality of smartphone sensor measurements.

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“…Several vision‐based methods to determine dynamic displacements have also been proposed in the literature (S. W. Park, Park, Kim, & Adeli, 2015; Ye et al., 2015). Among those methods, template matching techniques, phase‐based approaches, and traditional optical flow‐based tracking algorithms were mostly used (Cha, Chen, & Buyukozturk, 2017; Chen et al., 2015; Feng & Feng, 2016; Luo, Feng, & Wu, 2018; Min, Gelo, & Jo, 2016; Xu, Brownjohn, & Kong, 2018; Yang et al., 2017; Yoon, Elanwar, Choi, Golparvar‐Fard, & Spencer, 2016; Zhao et al., 2019). These researchers all employed stationary cameras.…”

Section: Introductionmentioning

confidence: 99%

Homography‐based structural displacement measurement for large structures using unmanned aerial vehicles

Weng

Shan

et al. 2020

Computer aided Civil Eng

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Structural displacement is an important quantity to assess the health of civil infrastructure. Vision‐based approaches using unmanned aerial vehicles (UAV) mounted with high‐resolution cameras have been proposed for this purpose. However, because the camera itself is moving with the UAV, any video obtained will contain both the motion of the structure and the motion of the camera. Planar homography can be used to eliminate the errors induced by the camera movement without the need for camera parameters. However, its direct application to large structures still has limitations, because capturing the undeformed regions, along with the measurement points on the structure, within a single image with sufficient resolution is seldom feasible. In this study, a new framework is presented to address these issues and facilitate the extraction of the structural displacement from videos taken by a UAV‐mounted camera. First, a two‐layer feedforward neural network (FNN) is adopted to obtain the image coordinates of the selected features of the structure on its stationary position, which are further used as homography features. Next, the structural displacement is estimated with the homography transformation matrix determined from the obtained homography features. Finally, the proposed approach is validated on both a six‐story shear‐building model in the laboratory and an elevator tower located in Zhongshan City, China. These results demonstrate the efficacy of the proposed approach.

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Real-time image processing for non-contact monitoring of dynamic displacements using smartphone technologies

Cited by 10 publications

References 4 publications

Reference-free dynamic displacements of railroad bridges using low-cost sensors

Reference-free dynamic displacements of railroad bridges using low-cost sensors

Shaking Table Tests and Validation of Multi-Modal Sensing and Damage Detection Using Smartphones

Homography‐based structural displacement measurement for large structures using unmanned aerial vehicles

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