A portable monitoring approach using cameras and computer vision for bridge load rating in smart cities

Dong, Chuan‐Zhi; Baş, Selçuk; Çatbaş, F. Necati

doi:10.1007/s13349-020-00431-2

Cited by 32 publications

(12 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several studies have been carried out by integrating an array of sensors (strain gauge, accelerometer, weigh-in-motion plate) and traffic monitoring video cameras [ 51 ]. Video-based methods have also been applied in place of the sensors to facilitate noncontact measurement of the bridge vibration under live load, measuring the deflection of a portion of the structural members, e.g., girders [ 52 ] and cable stays [ 53 ]. Although such systems are generally comparable to finite simulation [ 54 ] and able to identify simulated damages [ 55 ], they are still constrained due to the finite number of measurement points, the requirement of synchronization between cameras [ 53 ], and the lack of generated data from the traffic counting camera—especially in a real-time setting [ 56 ].…”

Section: Tandem Marker Motion Capture With Side-view Monitoringmentioning

confidence: 99%

“…The suboptimal common placement of both displacement measurement (perpendicular to the bridge’s axis) and traffic counting cameras (high vantage point) resulted in a set of issues: limited measurement field and the requirement of separate calibration/alignment for each displacement measurement camera [ 52 ]; further, the difficulty to estimate the passing vehicle’s details (size, type, speed) and susceptibility to errors are related to frontal occlusion for the traffic counting camera [ 42 , 57 , 58 ].…”

Section: Tandem Marker Motion Capture With Side-view Monitoringmentioning

confidence: 99%

See 1 more Smart Citation

Single-Camera-Based Bridge Structural Displacement Measurement with Traffic Counting

Aliansyah

Shimasaki

Senoo

et al. 2021

Sensors

View full text Add to dashboard Cite

Vision-based structural displacement methods allow convenient monitoring of civil structures such as bridges, though they are often limited due to the small number of measurement points, constrained spatial resolution, and inability to identify the acting forces of the measured displacement. To increase the number of measurement points in vision-based bridge displacement measurement, this study introduces a front-view tandem marker motion capture system with side-view traffic counting to identify the force-inducing passing vehicles on the bridge’s deck. The proposed system was able to measure structural displacement at submillimeter resolution on eight measurement points at once at a distance of 40.8–64.2 m from a front-view camera. The traffic counting system with a side-view camera recorded the passing vehicles from two opposing lanes. We conducted a 35-min experiment for a 25 m-span steel road bridge with hundreds of cars passing over it and confirmed dynamic displacement distributions with amplitudes of several millimeters when large vehicles passed.

show abstract

Section: Tandem Marker Motion Capture With Side-view Monitoringmentioning

confidence: 99%

Section: Tandem Marker Motion Capture With Side-view Monitoringmentioning

confidence: 99%

Single-Camera-Based Bridge Structural Displacement Measurement with Traffic Counting

Aliansyah

Shimasaki

Senoo

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In current research and engineering practices, visual inspection is the primary approach for the inspection of fatigue cracks. Bridge owners and bridge management departments put large amounts of effort and resources into the visual inspection task, guideline, and personnel [ 5 ]. A qualified inspector needs to go through the qualification, training, and certification of inspectors [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Pixel-Level Fatigue Crack Segmentation in Large-Scale Images of Steel Structures Using an Encoder–Decoder Network

Dong

Jin

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Fatigue cracks are critical types of damage in steel structures due to repeated loads and distortion effects. Fatigue crack growth may lead to further structural failure and even induce collapse. Efficient and timely fatigue crack detection and segmentation can support condition assessment, asset maintenance, and management of existing structures and prevent the early permit post and improve life cycles. In current research and engineering practices, visual inspection is the most widely implemented approach for fatigue crack inspection. However, the inspection accuracy of this method highly relies on the subjective judgment of the inspectors. Furthermore, it needs large amounts of cost, time, and labor force. Non-destructive testing methods can provide accurate detection results, but the cost is very high. To overcome the limitations of current fatigue crack detection methods, this study presents a pixel-level fatigue crack segmentation framework for large-scale images with complicated backgrounds taken from steel structures by using an encoder-decoder network, which is modified from the U-net structure. To effectively train and test the images with large resolutions such as 4928 × 3264 pixels or larger, the large images were cropped into small images for training and testing. The final segmentation results of the original images are obtained by assembling the segment results in the small images. Additionally, image post-processing including opening and closing operations were implemented to reduce the noises in the segmentation maps. The proposed method achieved an acceptable accuracy of automatic fatigue crack segmentation in terms of average intersection over union (mIOU). A comparative study with an FCN model that implements ResNet34 as backbone indicates that the proposed method using U-net could give better fatigue crack segmentation performance with fewer training epochs and simpler model structure. Furthermore, this study also provides helpful considerations and recommendations for researchers and practitioners in civil infrastructure engineering to apply image-based fatigue crack detection.

show abstract

“…Many studies have developed image processing algorithms for measuring the motion of a single target on the structure and capturing structure's static or dynamic response [9][10][11][12][13][14][15]. Accurate multi-point displacement measurements of different parts of large structures have been obtained using time-synchronized camera systems [16,17]. Although such systems enhance spatial resolution with accurate measurements, their applications are also more expensive than using a single camera.…”

Section: Introductionmentioning

confidence: 99%

“…Vertical deflections of girders were observed to reduce by almost 20% after repair. Also, Dong et al [17] used portable cameras and computer vision technology to perform bridge load rating.…”

Section: Introductionmentioning

confidence: 99%

A multiple camera position approach for accurate displacement measurement using computer vision

Kromanis

Kripakaran

2021

J Civil Struct Health Monit

View full text Add to dashboard Cite

Engineers can today capture high-resolution video recordings of bridge movements during routine visual inspections using modern smartphones and compile a historical archive over time. However, the recordings are likely to be from cameras of different makes, placed at varying positions. Previous studies have not explored whether such recordings can support monitoring of bridge condition. This is the focus of this study. It evaluates the feasibility of an imaging approach for condition assessment that is independent of the camera positions used for individual recordings. The proposed approach relies on the premise that spatial relationships between multiple structural features remain the same even when images of the structure are taken from different angles or camera positions. It employs coordinate transformation techniques, which use the identified features, to compute structural displacements from images. The proposed approach is applied to a laboratory beam, subject to static loading under various damage scenarios and recorded using multiple cameras in a range of positions. Results show that the response computed from the recordings are accurate, with 5% discrepancy in computed displacements relative to the mean. The approach is also demonstrated on a full-scale pedestrian suspension bridge. Vertical bridge movements, induced by forced excitations, are collected with two smartphones and an action camera. Analysis of the images shows that the measurement discrepancy in computed displacements is 6%.

show abstract

A portable monitoring approach using cameras and computer vision for bridge load rating in smart cities

Cited by 32 publications

References 28 publications

Single-Camera-Based Bridge Structural Displacement Measurement with Traffic Counting

Single-Camera-Based Bridge Structural Displacement Measurement with Traffic Counting

Pixel-Level Fatigue Crack Segmentation in Large-Scale Images of Steel Structures Using an Encoder–Decoder Network

A multiple camera position approach for accurate displacement measurement using computer vision

Contact Info

Product

Resources

About