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A list of the main pages and collections currently published on this site. For crawlers and indexing tools, there is also an XML version.
Pages
Publications
BallastAttN: Occlusion-Robust 3D Railway Ballast Characterization using Data Synthesis and Deep Learning
Published in Transportation Research Record, 2026
An occlusion-robust 3D deep learning pipeline for railway ballast segmentation, particle completion, and morphological characterization.
Recommended citation: Ding, K., Fan, Q., & Tutumluer, E. (2026). "BallastAttN: Occlusion-Robust 3D Railway Ballast Characterization using Data Synthesis and Deep Learning." Transportation Research Record.
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Image-Based Moisture Content Prediction in Railway Ballast Using Deep Learning
Published in Geo-Congress 2026, 2026
Deep learning approach for predicting ballast moisture content from images.
Recommended citation: Ding, K., & Tutumluer, E. (2026). "Image-Based Moisture Content Prediction in Railway Ballast Using Deep Learning." Geo-Congress 2026, 396-406.
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Condition Evaluation of Mud Spots in Railroad Ballast Using Deep Learning
Published in Journal of Infrastructure Systems, 2025
Deep learning for condition evaluation of mud spots in railroad ballast.
Recommended citation: Ding, K., Qamhia, I. I., Hart, J. M., & Tutumluer, E. (2025). "Condition Evaluation of Mud Spots in Railroad Ballast Using Deep Learning." Journal of Infrastructure Systems, 31(4), 04025028.
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Investigating Effect of Ballast Degradation on Track Dynamic Behavior using Coupled Train-Track Discrete Element Method Model
Published in Transportation Research Record, 2025
Study of ballast degradation and track dynamics using a coupled train-track discrete element method model.
Recommended citation: Liu, Z., Ding, K., Tutumluer, E., & Hashash, Y. M. (2025). "Investigating Effect of Ballast Degradation on Track Dynamic Behavior using Coupled Train-Track Discrete Element Method Model." Transportation Research Record, 03611981251349423.
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Evaluation of Field Ballast Degradation at Ft. Campbell Army Installation Using Computer Vision and Comparison with Ground Penetrating Radar
Published in International Conference on Transportation and Development 2025, 2025
Field ballast degradation evaluation using computer vision and comparison with ground penetrating radar.
Recommended citation: Kim, Y., Ding, K., Koh, Y., Kong, T., Tutumluer, E., Beasley, J., ... & Harrell, M. J. (2025). "Evaluation of Field Ballast Degradation at Ft. Campbell Army Installation Using Computer Vision and Comparison with Ground Penetrating Radar." International Conference on Transportation and Development 2025, 842-852.
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Deep Learning Image Segmentation to Assess Field Ballast Angularity Index
Published in Geotechnical Frontiers 2025, 2025
Image segmentation methods for evaluating ballast angularity in the field.
Recommended citation: Ding, K., Qamhia, I. I., Hart, J. M., & Tutumluer, E. (2025). "Deep Learning Image Segmentation to Assess Field Ballast Angularity Index." Geotechnical Frontiers 2025, 73-82.
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A Comparative Study of New Ballast and Recycled Ballast Generated from Various Maintenance Activities
Published in Transportation Engineering, 2025
Comparative study of new and recycled ballast generated from maintenance activities.
Recommended citation: Tutumluer, E., Wang, H., Husain, S. F., Kong, T., Kim, Y., Ding, K., ... & Li, D. (2025). "A Comparative Study of New Ballast and Recycled Ballast Generated from Various Maintenance Activities." Transportation Engineering, 100321.
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Towards Automated Field Ballast Condition Evaluation: Field Validation of the Ballast Scanning Vehicle Capabilities
Published in Transportation Geotechnics, 2024
Field validation of the ballast scanning vehicle capabilities for automated ballast condition evaluation.
Recommended citation: Luo, J., Ding, K., Huang, H., Qamhia, I. I., Tutumluer, E., Hart, J. M., ... & Sussmann, T. R. (2024). "Towards automated field ballast condition evaluation: Field validation of the ballast scanning vehicle capabilities." Transportation Geotechnics, 48, 101311.
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Deep Learning Approach for Automated Railroad Ballast Condition Evaluation
Published in International Conference on Transportation Geotechnics, 2024
Automated railroad ballast condition evaluation using deep learning.
Recommended citation: Luo, J., Ding, K., Qamhia, I. I., Hart, J. M., & Tutumluer, E. (2024). "Deep Learning Approach for Automated Railroad Ballast Condition Evaluation." International Conference on Transportation Geotechnics, 49-57.
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I-BALLAST: Computer Vision Solutions for Ballast Degradation Analysis Using Deep-Learning and Data Fusion Methods
Published in Geodata and AI, 2024
Computer vision and data fusion methods for ballast degradation analysis.
Recommended citation: Ding, K., Luo, J., Huang, H., Hart, J. M., Qamhia, I. I., Tutumluer, E., ... & Sussmann, T. R. (2024). "I-BALLAST: Computer vision solutions for ballast degradation analysis using deep-learning and data fusion methods." Geodata and AI, 1, 100007.
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Augmented Dataset for Vision-Based Analysis of Railroad Ballast via Multi-Dimensional Data Synthesis
Published in Algorithms, 2024
Synthetic and augmented datasets for vision-based ballast analysis.
Recommended citation: Ding, K., Luo, J., Huang, H., Hart, J. M., Qamhia, I. I., & Tutumluer, E. (2024). "Augmented Dataset for Vision-Based Analysis of Railroad Ballast via Multi-Dimensional Data Synthesis." Algorithms, 17(8), 367.
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Augmented Dataset for Multidimensional Ballast Segmentation and Evaluation
Published in IOP Conference Series: Earth and Environmental Science, 2024
Conference paper on an augmented dataset for multidimensional ballast segmentation and evaluation.
Recommended citation: Ding, K., Luo, J., Huang, H., Hart, J. M., Qamhia, I. I. A., & Tutumluer, E. (2024). "Augmented dataset for multidimensional ballast segmentation and evaluation." IOP Conference Series: Earth and Environmental Science, 1332(1), 012019.
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Toward Automated Field Ballast Condition Evaluation: Development of a Ballast Scanning Vehicle
Published in Transportation Research Record, 2024
Development of a ballast scanning vehicle for automated field ballast condition evaluation.
Recommended citation: Luo, J., Ding, K., Huang, H., Hart, J. M., Qamhia, I. I., Tutumluer, E., ... & Sussmann, T. R. (2024). "Toward automated field ballast condition evaluation: Development of a ballast scanning vehicle." Transportation Research Record, 2678(3), 24-36.
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Toward Automated Field Ballast Condition Evaluation: Algorithm Development Using a Vision Transformer Framework
Published in Transportation Research Record, 2023
Vision transformer framework for automated field ballast condition evaluation.
Recommended citation: Luo, J., Huang, H., Ding, K., Qamhia, I. I., Tutumluer, E., Hart, J. M., ... & Sussmann, T. R. (2023). "Toward automated field ballast condition evaluation: algorithm development using a vision transformer framework." Transportation Research Record, 2677(10), 423-437.
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I-RIPRAP 3D Image Analysis Software: User Manual
Published in Illinois Center for Transportation, 2023
User manual for I-RIPRAP 3D image analysis software.
Recommended citation: Huang, H., Luo, J., Ding, K., Tutumluer, E., Hart, J. M., & Qamhia, I. (2023). "I-RIPRAP 3D Image Analysis Software: User Manual." Illinois Center for Transportation, FHWA-ICT-23-007.
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3D Image Analysis Using Deep Learning for Size and Shape Characterization of Stockpile Riprap Aggregates-Phase 2
Published in FHWA-ICT-22-013, 2022
Deep-learning-based 3D image analysis for stockpile riprap aggregate characterization.
Recommended citation: Huang, H., Tutumluer, E., Luo, J., Ding, K., Qamhia, I., & Hart, J. M. (2022). "3D image analysis using deep learning for size and shape characterization of stockpile riprap aggregates-Phase 2." FHWA-ICT-22-013.
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Laboratory Characterization of Foundation Geomaterials for a Smart Runway Pavement Test Section
Published in Geo-Congress 2022, 2022
Laboratory characterization of foundation geomaterials for a smart runway pavement test section.
Recommended citation: Liu, Z., Kang, M., Qamhia, I., Ding, K., Wang, H., Huang, H., ... & Tingle, J. S. (2022). "Laboratory Characterization of Foundation Geomaterials for a Smart Runway Pavement Test Section." Geo-Congress 2022, 431-441.
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Projects
I-BALLAST Mobile
Published:
iOS application for field ballast inspection with real-time on-device inference and deployment-oriented ML integration.
Mud Spot Ballast Segmentation
Published:
Computer vision project combining synthetic data augmentation, DETR-based detection, and SAM fine-tuning for muddy ballast imagery.
Simulation-Driven Dataset Generation
Published:
Desktop software and data pipeline for synthetic image and point-cloud generation using Blender Python API.
Ballast Scanning Vehicle (BSV)
Published:
Large-scale multi-sensor scanning platform for automated field condition evaluation using cameras, 3D scanning, and GPS.
I-RIPRAP 3D
Published:
Interactive 3D analysis software for aggregate materials with model-assisted measurement and visualization workflows.