A Review of Computer Vision-Based Structural Deformation Monitoring in Field Environments

Easa, Said M.; Chen, Weimin; Jin, Tao; Chen, Bin; Zhang, He; Zhang, Wen

doi:10.3390/s22103789

Cited by 25 publications

(6 citation statements)

References 149 publications

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“…Assessment of the health state of infrastructures throughout their lifecycle is essential to verify their structural safety and serviceability [9] and minimize their future reparation costs [10]. Structural Health Monitoring (SHM) applications can provide essential information about the current structural response, performance, and condition of infrastructures [11].…”

Section: Of 22mentioning

confidence: 99%

Operational and Analytical Modal Analysis of a Bridge Using Low-Cost Wireless Arduino-Based Accelerometers

Komarizadehasl

Huguenet²,

Lozano

et al. 2022

Sensors

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Arduino-based accelerometers are receiving wide attention from researchers to make long-term Structural Health Monitoring (SHM) feasible for structures with a low SHM budget. The current low-cost solutions found in the literature share some of the following drawbacks: (1) high noise density, (2) lack of wireless synchronization, (3) lack of automatic data acquisition and data management, and (4) lack of dedicated field tests aiming to compare mode shapes from Operational Modal Analysis (OMA) with those of a digital model. To solve these problems, a recently built short-span footbridge in Barcelona is instrumented using four Low-cost Adaptable Reliable Accelerometers (LARA). In this study, the automatization of the data acquisition and management of these low-cost solutions is studied for the first time in the literature. In addition, a digital model of the bridge under study is generated in SAP2000 using the available drawings and reported characteristics of its materials. The OMA of the bridge is calculated using Frequency Domain Decomposition (FDD) and Covariance Stochastic Subspace Identification (SSI-cov) methods. Using the Modal Assurance Criterion (MAC), the mode shapes of OMA are compared with those of the digital model. Finally, the acquired eigenfrequencies of the bridge obtained with a high-precision commercial sensor (HI-INC) showed a good agreement with those obtained with LARA.

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Section: Of 22mentioning

confidence: 99%

Operational and Analytical Modal Analysis of a Bridge Using Low-Cost Wireless Arduino-Based Accelerometers

Komarizadehasl

Huguenet²,

Lozano

et al. 2022

Sensors

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“…However, these contact-type sensors need to be installed at specific locations on structures, which is often difficult or even impossible in some cases. Recently, the non-contact sensing system based on computer vision (CV) has been developed and applied to vibration measurement [6][7][8]. The CV-based photogrammetry techniques for structural vibration measurement mainly include the template-matching method [9], digital image correlation (DIC) [10], and the optical flow method [11].…”

Section: Introductionmentioning

confidence: 99%

Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking

Kong

Wang

et al. 2023

Applied Sciences

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Most research on computer vision (CV)-based vibration measurement is limited to the determination of discrete or coarse mode shapes of the structure. The continuous edge of the structure in images has rich optical features, and thus, by identifying and tracking the movement of the structure’s edge, it is possible to determine high-resolution full-field mode shapes of the structure without a preset target. The present study proposes a CV-based method of full-field mode shape identification using the subpixel edge detection and tracking techniques. Firstly, the Canny operator is applied on each frame of the structure vibration video to extract the pixel-level edges, and the improved Zernike orthogonal moment (ZOM) subpixel edge detection technique is adopted to relocate the precise structure edges. Then, all the detected edge points are tracked to obtain the full-field dense displacement time history that is subsequently used to determine the structure frequencies and compute full-field mode shapes by combining the covariance driven stochastic subspace identification (SSI-COV) with the hierarchical cluster analysis. Finally, the proposed method is verified on the aluminum cantilever beam in the laboratory and the Humen Bridge in the field. The results show that the proposed method is able to detect more precise structure edges and identify the full-field displacement and mode shapes of structures without the need for installing artificial targets on the structure in advance, which provides valuable information for the structural condition assessment, especially for structures with small-amplitude vibrations.

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“…The four most common types of accelerometers are micro-electro-mechanical systems (MEMS) [20], differential capacitive [21], Piezoresistive [22], and piezoelectric [23] accelerometers are used, and piezoelectric accelerometers are the most traditionally used in SHM applications [24]. Infrastructure condition assessment throughout the life cycle of a bridge is essential in verifying serviceability and structural safety [25], thereby minimizing repair costs [26]. The Structure Health Monitoring (SHM) system can provide essential information about the current structural performance, status, and response of the infrastructure [27].…”

Section: Introductionmentioning

confidence: 99%

Expansion Joints Risk Prediction System Based on IoT Displacement Device

Park¹,

Ham

Ahn

2023

Electronics

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Damage to bridge expansion joints arises from a variety of causes such as increasingly deteriorated bridges, abnormal temperatures, and increased traffic. To detect anomalies in the expansion joints, this study proposes an Artificial Intelligence (AI)-model-based diagnosis method of analyzing the vibration of the bridge bearing that supports the upper structure of a bridge. The proposed system establishes big data with the measured displacement of a bridge bearing and makes an AI-based prediction about the risk of bridge expansion joints. Replacing a bridge bearing makes it possible to manage the bridge displacement before and after construction and helps improve safety inspections and diagnosis methods. It is necessary to prepare a bridge with anomalies for the AI model training. For this reason, a bridge with a bridge bearing was simulated. In addition, a vehicle suitable for the bridge was simulated. The displacement data in normal and abnormal situations were collected, cleaned, and applied to the AI analysis model. The system was found to have over 90% accuracy of prediction about expansion joint faulting and damage.

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A Review of Computer Vision-Based Structural Deformation Monitoring in Field Environments

Cited by 25 publications

References 149 publications

Operational and Analytical Modal Analysis of a Bridge Using Low-Cost Wireless Arduino-Based Accelerometers

Operational and Analytical Modal Analysis of a Bridge Using Low-Cost Wireless Arduino-Based Accelerometers

Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking

Expansion Joints Risk Prediction System Based on IoT Displacement Device

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