scispace - formally typeset
Search or ask a question
Author

Mohammed Al Bataineh

Bio: Mohammed Al Bataineh is an academic researcher from Western Michigan University. The author has contributed to research in topics: Bridge (nautical) & Bridge maintenance. The author has an hindex of 1, co-authored 1 publications receiving 39 citations.

Papers
More filters
Journal ArticleDOI
TL;DR: In this paper, a data model that was developed to support automated imaging inspection of concrete bridges is presented. But these systems still neglect the automation aspects of bridge monitoring and inspection. But they do not address the data organization and decision-making aspect of bridge inspection and maintenance.

44 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: U-Net based concrete crack detection method proposed in the present study is compared with the DCNN-based method, and U-Net is found to be more elegant than DCNN with more robustness, more effectiveness and more accurate detection.

364 citations

Journal ArticleDOI
TL;DR: In this paper, an integrated model consisting of crack quantification, change detection, neural networks, and 3D visualization models to visualize the defects in such a way that it mimics the on-site visual inspections is presented.

268 citations

Journal ArticleDOI
TL;DR: The development of a next-generation integrated bridge inspection system, called SeeBridge, demonstrates that the hurdles in the way of automated acquisition of detailed and semantically rich models of existing infrastructure are computational in nature, not instrumental, and are surmountable with existing technologies.

90 citations

Journal ArticleDOI
TL;DR: In this paper, the authors developed an integrated model based on image processing techniques and machine learning to automate consistent spalling detection and numerical representation of distress in subway networks, which consists of a hybrid algorithm, interactive 3D presentation, and supported by regression analysis to predict spalling depth.

89 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed the application of the grey level co-occurrence matrix (GLCM) texture analysis approach and an artificial neural network (ANN) classifier to obtain surface damage information, such as the total amount of superficial cracking and the total length and range of crack widths.
Abstract: Imaging-based inspection methods are increasingly being employed for crack detection in concrete structures, because they provide quantitative information compared to inspections based solely on conventional visual approaches. However, efficient image analysis methods are needed. This study proposes the application of the grey level co-occurrence matrix (GLCM) texture analysis approach and an artificial neural network (ANN) classifier to obtain surface damage information, such as the total amount of superficial cracking, as well as the total length, and range of crack widths. These methods were applied to thermographic, visual colour and greyscale images of concrete blocks from CANMET that were exposed outdoors for ten years, as well as slabs from GRAI that were kept indoors, all specimens exhibiting various levels of alkali-aggregate reaction (AAR) damage. Results of the classifications show that the greyscale imagery performed fairly well, with an overall classification accuracy range of 72.3–76.5% for the CANMET blocks, and 68.7–75.3% for the GRAI slabs. Classifications using the colour imagery were slightly better than the greyscale imagery, with accuracies ranging from 71.4% to 75.2% for CANMET blocks and 70.9–72.0% for the GRAI slabs. The thermographic imagery, however, produced the highest overall classification accuracies, which range from 73.1% to 76.3% for the CANMET blocks and 74.2–76.9% for the GRAI slabs. The results show that all three types of imagery are relatively effective in characterizing and quantifying crack damage; however, the infrared thermography produced more accurate results compared to the visual colour, and greyscale images.

49 citations