Search

Your search keyword '"Ding, Kelin"' showing total 8 results
Start Over Author "Ding, Kelin"
8 results on '"Ding, Kelin"'

1. Deep Learning Approach for Automated Railroad Ballast Condition Evaluation

Author

Luo, Jiayi, Ding, Kelin, Qamhia, Issam I. A., Hart, John M., Tutumluer, Erol, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Lu, Xinzheng, Series Editor, Rujikiatkamjorn, Cholachat, editor, Xue, Jianfeng, editor, and Indraratna, Buddhima, editor

Published
2025
Full Text
View/download PDF

3. Augmented Dataset for Vision-Based Analysis of Railroad Ballast via Multi-Dimensional Data Synthesis.

Author

Ding, Kelin, Luo, Jiayi, Huang, Haohang, Hart, John M., Qamhia, Issam I. A., and Tutumluer, Erol

Subjects
*COMPUTER vision, *DEEP learning, *RAILROAD management, *POINT cloud, *DATABASES, *BALLAST (Railroads)
Abstract

Ballast serves a vital structural function in supporting railroad tracks under continuous loading. The degradation of ballast can result in issues such as inadequate drainage, lateral instability, excessive settlement, and potential service disruptions, necessitating efficient evaluation methods to ensure safe and reliable railroad operations. The incorporation of computer vision techniques into ballast inspection processes has proven effective in enhancing accuracy and robustness. Given the data-driven nature of deep learning approaches, the efficacy of these models is intrinsically linked to the quality of the training datasets, thereby emphasizing the need for a comprehensive and meticulously annotated ballast aggregate dataset. This paper presents the development of a multi-dimensional ballast aggregate dataset, constructed using empirical data collected from field and laboratory environments, supplemented with synthetic data generated by a proprietary ballast particle generator. The dataset comprises both two-dimensional (2D) data, consisting of ballast images annotated with 2D masks for particle localization, and three-dimensional (3D) data, including heightmaps, point clouds, and 3D annotations for particle localization. The data collection process encompassed various environmental lighting conditions and degradation states, ensuring extensive coverage and diversity within the training dataset. A previously developed 2D ballast particle segmentation model was trained on this augmented dataset, demonstrating high accuracy in field ballast inspections. This comprehensive database will be utilized in subsequent research to advance 3D ballast particle segmentation and shape completion, thereby facilitating enhanced inspection protocols and the development of effective ballast maintenance methodologies. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

4. Toward Automated Field Ballast Condition Evaluation: Development of a Ballast Scanning Vehicle

Author

Luo, Jiayi, Ding, Kelin, Huang, Haohang, Hart, John M., Qamhia, Issam I. A., Tutumluer, Erol, Thompson, Hugh, and Sussmann, Theodore R.

Abstract

Ballast plays an essential role in the response of a railroad track to repeated loading. Ballast degradation may lead to poor drainage, lateral instability, and excessive settlement. Extreme levels of ballast degradation may cause service interruptions and safety concerns. Therefore, ballast condition evaluation is of great importance in ensuring safe and efficient operations. Current state-of-the-practice evaluation methods are heavily dependent on visual inspection, field sampling, and laboratory testing, which are subjective and labor-intensive. Meanwhile, existing inspection systems for railroads have not been customized for conducting in-depth evaluation of the ballast layer, including determination of the level of fouling and aggregate size and shape characteristics. For this reason, there is an urgent need for the development of a novel, vison-based ballast scanning platform. This paper introduces the ballast scanning vehicle (BSV), an automated platform that acquires high-quality images, videos, and 3-D height maps of ballast from plan and profile views of cut sections and trenches. The BSV is capable of performing data analysis and generating accurate and comprehensive evaluations of ballast conditions through geotechnical analyses. The essential design components, prototyping, and development stages are described. Further, preliminary data collected from testing on two in-service railroad tracks behind a shoulder ballast cleaner are presented to validate the functions of the BSV. The fully developed BSV serves as a data collection device for ballast evaluation and provides continuous and high-quality images for a deep learning-based computer vision algorithm for field ballast condition evaluation and geotechnical analyses.

Published
2024
Full Text
View/download PDF

7. I-RIPRAP 3D Image Analysis Software: User Manual

Author

Illinois. Department of Transportation, United States. Department of Transportation. Federal Highway Administration, Huang, Haohang, Luo, Jiayi, Ding, Kelin, Tutumluer, Erol, Hart, John M., Qamhia, Issam, Illinois Center for Transportation, University of Illinois at Urbana-Champaign, Illinois. Department of Transportation, United States. Department of Transportation. Federal Highway Administration, Huang, Haohang, Luo, Jiayi, Ding, Kelin, Tutumluer, Erol, Hart, John M., Qamhia, Issam, Illinois Center for Transportation, and University of Illinois at Urbana-Champaign

Abstract

This research study introduces a new approach based on deep learning techniques that combines three developed research components: field 3D reconstruction procedures, 3D stockpiles instance segmentation, and 3D shape completion. The approach is designed to reconstruct aggregate stockpiles from multiple images, segment the stockpile into individual instances, and predict the unseen sides of each instance (particle) based on the partially visible shapes. The approach was validated using ground-truth measurements and demonstrated satisfactory algorithm performance in capturing and predicting the unseen sides of aggregates.

8. 3D Image Analysis Using Deep Learning for Size and Shape Characterization of Stockpile Riprap Aggregates—Phase 2

Author

Illinois Department. of Transportation. Bureau of Research, United States. Department of Transportation. Federal Highway Administration, Huang, Haohang, Tutumluer, Erol, Luo, Jiayi, Ding, Kelin, Qamhia, Issam, Hart, John M., Illinois Center for Transportation, University of Illinois at Urbana-Champaign. Department of Civil and Environmental Engineering, Illinois Department. of Transportation. Bureau of Research, United States. Department of Transportation. Federal Highway Administration, Huang, Haohang, Tutumluer, Erol, Luo, Jiayi, Ding, Kelin, Qamhia, Issam, Hart, John M., Illinois Center for Transportation, and University of Illinois at Urbana-Champaign. Department of Civil and Environmental Engineering

Abstract

R27-214, Riprap rock and aggregates are extensively used in structural, transportation, geotechnical, and hydraulic engineering applications. Field determination of morphological properties of aggregates such as size and shape can greatly facilitate the quality assurance/quality control (QA/QC) process for proper aggregate material selection and engineering use. Many aggregate imaging approaches have been developed to characterize the size and morphology of individual aggregates by computer vision. However, 3D field characterization of aggregate particle morphology is challenging both during the quarry production process and at construction sites, particularly for aggregates in stockpile form. This research study presents a 3D reconstruction-segmentation-completion approach based on deep learning techniques by combining three developed research components: field 3D reconstruction procedures, 3D stockpile instance segmentation, and 3D shape completion. The approach was designed to reconstruct aggregate stockpiles from multi-view images, segment the stockpile into individual instances, and predict the unseen side of each instance (particle) based on the partial visible shapes. Based on the dataset constructed from individual aggregate models, a state-of-the-art 3D instance segmentation network and a 3D shape completion network were implemented and trained, respectively. The application of the integrated approach was demonstrated on re-engineered stockpiles and field stockpiles. The validation of results using ground-truth measurements showed satisfactory algorithm performance in capturing and predicting the unseen sides of aggregates. The algorithms are integrated into a software application with a user-friendly graphical user interface. Based on the findings of this study, this stockpile aggregate analysis approach is envisioned to provide efficient field evaluation of aggregate stockpiles by offering convenient and reliable solutions for on-site QA/QC tasks of riprap rock an

Catalog

Books, media, physical & digital resources
See catalog results