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Title:Size and Shape Determination of Riprap and Large-sized Aggregates Using Field Imaging
Author(s):Huang, Haohang; Luo, Jiayi; Tutumluer, Erol; Hart, John M.; Qamhia, Issam
Subject(s):Riprap
Large-sized Aggregates
Field Imaging
Computer Vision
Deep Learning
Object Detection
Image Segmentation
Size and Morphological/Shape Analyses
Stockpile Aggregate Imaging
Abstract:Riprap rock and large-sized aggregates are extensively used in transportation, geotechnical, and hydraulic engineering applications. Traditional methods for assessing riprap categories based on particle weight may involve subjective visual inspection and time-consuming manual measurements. Aggregate imaging and segmentation techniques can efficiently characterize riprap particles for their size and morphological/shape properties to estimate particle weights. Particle size and morphological/shape characterization ensure the reliable and sustainable use of all aggregate skeleton materials at quarry production lines and construction sites. Aggregate imaging systems developed to date for size and shape characterization, however, have primarily focused on measurement of separated or non-overlapping aggregate particles. This research study presents an innovative approach for automated segmentation and morphological analyses of stockpile aggregate images based on deep-learning techniques. As a project outcome, a portable, deployable, and affordable field-imaging system is envisioned to estimate volumes of individual riprap rocks for field evaluation. A state-of-the-art object detection and segmentation framework is used to train an image-segmentation kernel from manually labeled 2D riprap images in order to facilitate automatic and user-independent segmentation of stockpile aggregate images. The segmentation results show good agreement with ground-truth validation, which entailed comparing the manual labeling to the automatically segmented images. A significant improvement to the efficiency of size and morphological analyses conducted on densely stacked and overlapping particle images is achieved. The algorithms are integrated into a software application with a user-friendly Graphical User Interface (GUI) for ease of operation. Based on the findings of this study, this stockpile aggregate image analysis program promises to become an efficient and innovative application for field-scale and in-place evaluations of aggregate materials. The innovative imaging-based system is envisioned to provide convenient, reliable, and sustainable solutions for the on-site quality assurance/quality control (QA/QC) tasks related to riprap rock and large-sized aggregate material characterization and classification.
Issue Date:2020-01
Publisher:Illinois Center for Transportation/Illinois Department of Transportation
Citation Info:Huang, Haohang, Jiayi Luo, Erol Tutumluer, John M. Hart, and Issam Qamhia. 2020. Size and Shape Determination of Riprap and Large-sized Aggregates Using Field Imaging. A report of the findings of ICT-R27-182. Illinois Center for Transportation Series No. 20-003. Research Report No. FHWA-ICT-20-002. Illinois Center for Transportation, Rantoul, IL. https://doi.org/10.36501/0197-9191/20-003
Series/Report:FHWA-ICT-20-002
Genre:Technical Report
Type:Text
Language:English
URI:http://hdl.handle.net/2142/106037
ISSN:0197-9191
DOI:10.36501/0197-9191/20-003
Sponsor:IDOT-R27-182
Rights Information:No restrictions. This document is available through the National Technical Information Service, Springfield, VA 22161.
Date Available in IDEALS:2020-01-16


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