Vision-based modal parameter identification for bridges using a novel holographic visual sensor

Zhou, Zhixiang; Shao, Shuai; Deng, Gaofeng; Yan-mei, Gao; Wang, Shaorui; Chu, Xi

doi:10.1016/j.measurement.2021.109551

Cited by 7 publications

(5 citation statements)

References 34 publications

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“…In addition, some of the studies that implemented experimental tests in indoor or outdoor scenarios also ventured out into the field, verifying the behavior of the proposed solutions in real-world environments and on full-scale targets, that is, under more challenging conditions. This is the case for [32,34,38,40,41,45,55,60,66,69,71,75,81,82].…”

Section: Experimental and Real-world Scenariosmentioning

confidence: 91%

“…The analysis revealed that several sensors had been used for validation purposes, including laser displacement sensors [32,34,41,42,49,70,74,76,82], accelerometers [32][33][34]50,51,53,55,59,61,[64][65][66][67][68]76], IMU [60], linear variable differential transformers [34,36,41,50,54,61,66,67,73], laser Doppler vibrometers [38,46], stationary cameras [42,75,83], actuators [71], laser trackers [81], infrared sensors [48], and marker-based systems [44,73]. Marker-based systems are routinely used in other fields (such as medicine), where they are the traditional reference systems for motion capture and analysis.…”

Section: Validation With Gold-standardmentioning

confidence: 99%

“…This choice applies to the following studies: [34,[40][41][42]44,45,[48][49][50][51][53][54][55][58][59][60][61][62][63][64]66,67,[69][70][71][72][73][74][75][76][77][78][80][81][82]. In a few cases, the experimental scenarios were outdoors where, on the other hand, it was necessary to consider, at least in part, the environmental conditions and the problems previously indicated, testing the solution on scale models in most cases as well.…”

Section: Experimental and Real-world Scenariosmentioning

confidence: 99%

See 2 more Smart Citations

Computer Vision and Image Processing in Structural Health Monitoring: Overview of Recent Applications

Ferraris

Amprimo

Pettiti

2023

Signals

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Structural deterioration is a primary long-term concern resulting from material wear and tear, events, solicitations, and disasters that can progressively compromise the integrity of a cement-based structure until it suddenly collapses, becoming a potential and latent danger to the public. For many years, manual visual inspection has been the only viable structural health monitoring (SHM) solution. Technological advances have led to the development of sensors and devices suitable for the early detection of changes in structures and materials using automated or semi-automated approaches. Recently, solutions based on computer vision, imaging, and video signal analysis have gained momentum in SHM due to increased processing and storage performance, the ability to easily monitor inaccessible areas (e.g., through drones and robots), and recent progress in artificial intelligence fueling automated recognition and classification processes. This paper summarizes the most recent studies (2018–2022) that have proposed solutions for the SHM of infrastructures based on optical devices, computer vision, and image processing approaches. The preliminary analysis revealed an initial subdivision into two macro-categories: studies that implemented vision systems and studies that accessed image datasets. Each study was then analyzed in more detail to present a qualitative description related to the target structures, type of monitoring, instrumentation and data source, methodological approach, and main results, thus providing a more comprehensive overview of the recent applications in SHM and facilitating comparisons between the studies.

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Section: Experimental and Real-world Scenariosmentioning

confidence: 91%

Section: Validation With Gold-standardmentioning

confidence: 99%

Section: Experimental and Real-world Scenariosmentioning

confidence: 99%

See 1 more Smart Citation

Computer Vision and Image Processing in Structural Health Monitoring: Overview of Recent Applications

Ferraris

Amprimo

Pettiti

2023

Signals

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“…We frst validated the applicability of the proposed displacement monitoring approach by the experimental studies on the scale model of a self-anchored suspension bridge, as shown in Figure 15. Te suspension bridge model, shown in Figure 16, had a main span of 5.3335 + 13.5330 + 5.3335 � 24.2 m, and its width was 1.2 m. Unlike previous displacement monitoring tests on this structure [38][39][40][41], in which the cameras were perpendicular to the target region, we used a network surveillance camera, which was tilted at diferent angles in this study. To explore the proposed SF estimation method, we selected a total of three targets (LED lamps) located at the three-eighths of the midspan as the tracing targets, as shown in Figure 17.…”

Section: Displacement Monitoring Of a Self-anchored Suspension Bridge...mentioning

confidence: 99%

An Improved Structural Displacement Monitoring Approach by Acceleration-Aided Tilt Camera Measurement

Tang

Zhang

et al. 2023

Structural Control and Health Monitoring

Self Cite

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Computer vision is becoming one of the most popular remote-sensing techniques and has been used widely in displacement monitoring and damage identification of in-service bridges. Nevertheless, several obstacles, including limited sampling rate, insufficient resolution for remote measurement, and error induced by camera tilting, restrict the application of vision-based approaches in structural health monitoring (SHM). The combination of a traditional SHM system and a modern remote-sensing technique can significantly improve the accuracy and reliability of the monitoring system. To make full use of data collected in the traditional SHM system and computer vision technique and overcome their shortcomings, we presented an improved bridge displacement estimation approach for SHM purposes by fusing camera-based and acceleration measurements. First, we estimated the scaling factor, which transfers pixel displacement to real displacement under tilt photogrammetry, by the acceleration reconstructed and camera-based displacements in the same frequency band without the actual size of the structure or the measurement parameters. Then, we extracted the low-frequency displacement from the vision-based measurement, and we fused the high-frequency displacement that was reconstructed from the acceleration measurement to achieve high-accuracy displacement estimation. The efficiency of this method was validated through dynamic load tests on a suspension model bridge in the laboratory and field tests on a highway and subway cable-stayed bridge.

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“…SHM applications provide vital information about the actual structural response of infrastructures, the condition of the structures and their performance. As indicated by many scholars (such as [ 6 , 7 ]), SHM can be used to calibrate the simulated model of the real structures (digital-twin), which imitates the performance of the structures [ 8 ]. Digital twins can then be used to evaluate the decision-making alternatives during the maintenance phase of structures under study [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Wireless Low-Cost Inclinometer Made from Combining the Measurements of Multiple MEMS Gyroscopes and Accelerometers

Komarizadehasl

Komary

Alahmad

et al. 2022

Sensors

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Structural damage detection using inclinometers is getting wide attention from researchers. However, the high price of inclinometers limits this system to unique structures with a relatively high structural health monitoring (SHM) budget. This paper presents a novel low-cost inclinometer, the low-cost adaptable reliable angle-meter (LARA), which combines five gyroscopes and five accelerometers to measure inclination. LARA incorporates Internet of Things (IoT)-based microcontroller technology enabling wireless data streaming and free commercial software for data acquisition. This paper investigates the accuracy, resolution, Allan variance and standard deviation of LARA produced with a different number of combined circuits, including an accelerometer and a gyroscope. To validate the accuracy and resolution of the developed device, its results are compared with those obtained by numerical slope calculations and a commercial inclinometer (HI-INC) in laboratory conditions. The results of a load test experiment on a simple beam model show the high accuracy of LARA (0.003 degrees). The affordability and high accuracy of LARA make it applicable for structural damage detection on bridges using inclinometers.

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Vision-based modal parameter identification for bridges using a novel holographic visual sensor

Cited by 7 publications

References 34 publications

Computer Vision and Image Processing in Structural Health Monitoring: Overview of Recent Applications

Computer Vision and Image Processing in Structural Health Monitoring: Overview of Recent Applications

An Improved Structural Displacement Monitoring Approach by Acceleration-Aided Tilt Camera Measurement

A Novel Wireless Low-Cost Inclinometer Made from Combining the Measurements of Multiple MEMS Gyroscopes and Accelerometers

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