Abstract

The orthotropic steel-box girder (OSG) is widely used in the construction of a large-scale bridges. Since cumulative damages caused by the heavy vehicles and initial flaws of welding, the bridges with OSGs frequently suffer from fatigue cracks, which are commonly distributed around U-ribs. Hence, the management of fatigue cracks is mandatory in practical engineering. Although some techniques have been adopted for the detection of cracks, the workflow is often labor-intensive, time-consuming, and of low-temporal resolution. Considering the optical visibility of a crack and the limitation of the shape of an over-welding-hole around the U-rib, a machine vision-based monitoring methodology for the fatigue cracks in U-rib-to-deck weld seams is proposed in this paper. To be specific, a specific Internet of Things (IoT) based image acquisition device is first developed and introduced to obtain precisely part-view images of a fatigue crack. As followed, a novel image rectification and stitching method based on a specified coded calibration board is innovated and described for generating a measurable panoramic fatigue crack image. Furthermore, a deep learning-based crack detection-segmentation integrated algorithm is developed to detect and segment the crack areas. Afterwards, a feature extraction procedure based on image processing is explored to obtain the morphological features of a crack, involving its area, length and width. Finally, a field experiment was carried out on a real steel suspension bridge. By comparing the measurements both from manual measuring and vision-based monitoring, the results indicate that the proposed methodology is very promising to monitor the fatigue cracks in U-rib-to-deck weld seams, and the root-mean-square errors in length and width measuring could be 3.0195 mm and 0.003 mm, respectively. This work is not only of practical value to the management and maintenance of the OSG bridges in engineering, but also critical for the researches on fatigue cracks propagation.

Highlights

  • In past decades, the orthotropic steel-box girder (OSG) has been widely applied in long-span cable-supported bridges, benefiting from the advantages of low weight, high torsional stiffness, and convenience of manufacturing and construction

  • For the generation of a measurable panoramic fatigue crack image, we proposed a novel image rectification and stitching method based on coded calibration board, which is validated by the experiments and the results indicate it is stable in the real scene and can be used for the steel surface image stitching in a low feature points case

  • OVERVIEW OF OUR METHODOLOGY In this paper, we propose a machine vision-based methodology, which can be regarded as an overall solution, containing imaging strategy, data processing algorithms and usable descriptions for the evaluation and monitoring of fatigue cracks in U-rib-to-deck weld seams, which prefer to suffer from fatigue cracks much more frequently than other details

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Summary

INTRODUCTION

The orthotropic steel-box girder (OSG) has been widely applied in long-span cable-supported bridges, benefiting from the advantages of low weight, high torsional stiffness, and convenience of manufacturing and construction. OVERVIEW OF OUR METHODOLOGY In this paper, we propose a machine vision-based methodology, which can be regarded as an overall solution, containing imaging strategy, data processing algorithms and usable descriptions for the evaluation and monitoring of fatigue cracks in U-rib-to-deck weld seams, which prefer to suffer from fatigue cracks much more frequently than other details As indicated, this framework consists of two major parts. Once the fatigue crack image acquisition device is installed on the ribs, the images of every scanning will be captured at the fixed locations, since the shooting positions and poses of the camera are precisely and strictly limited by the stepping motor in the mechanical driving module. The stitching seam is completely eliminated, and there is a smooth transition of color in the overlapping area

CRACK RECOGNITION AND MORPHOLOGICAL FEATURE EXTRACTION
SKELETON EXTRACTION
MORPHOLOGICAL FEATURES OF A CRACK
APPLICATION
DISCUSSION OF DEVIATIONS
CONCLUSION
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