Field Imaging and Volumetric Reconstruction of Riprap Rock and Large-Sized Aggregates: Algorithms and Application

Abstract

Riprap rock and large-sized aggregates have been used extensively in geotechnical and hydraulic engineering. They essentially provide erosion control, sediment control, and scour protection. The sustainable and reliable use of riprap materials demands efficient and accurate evaluation of their large particle sizes, shapes, and gradation information at both quarry production lines and construction sites. Traditional methods for assessing riprap geometric properties involve subjective visual inspection and time-consuming hand measurements. As such, achieving the comprehensive in-situ characterization of riprap materials still remains challenging for practitioners and engineers. This paper presents an innovative approach for characterizing the volumetric properties of riprap by establishing a field imaging system associated with newly developed color image segmentation and three-dimensional (3-D) reconstruction algorithms. The field imaging system described in this paper with its algorithms and field application examples is designed to be portable, deployable, and affordable for efficient image acquisition. The robustness and accuracy of the image segmentation and 3-D reconstruction algorithms are validated against ground truth measurements collected in stone quarry sites and compared with state-of-the-practice inspection methods. The imaging-based results show good agreement with the ground truth and provide improved volumetric estimation when compared with currently adopted inspection methods. Based on the findings of this study, the innovative imaging-based system is envisioned for full development to provide convenient, reliable, and sustainable solutions for the onsite Quality Assurance/Quality Control tasks relating to riprap rock and large-sized aggregates.

I-RIPRAP Computer Vision Software for Automated Size and Shape Characterization of Riprap in Stockpile Images

“…These focused studies brought about systems and test procedures developed for analyzing both single-aggregate and stockpile-aggregate images. For individual aggregates, Huang et al developed a versatile field imaging setup with associated color-based image segmentation and volumetric reconstruction algorithms to work under various field conditions of sunlight and shadowing (14). For adjacent aggregates, stockpile aggregates, or both, the challenge has been to efficiently extract (i.e., segment) individual particles from a densely stacked stockpile image.…”

“…As a reference to the typical 3D FER values to be used, all riprap particles collected in the single-aggregate study have been analyzed for their 3D FER statistics (14). Three orthogonal views of each particle were analyzed, and the shortest/longest dimensions were calculated based on the minimum/maximum measurement among the multiple views.…”

“…These image analysis systems have facilitated the in-place riprap characterization and extended the applications from laboratory to the field. Based on the previous work of the authors, a multi-scale solution becomes feasible for the field evaluation of riprap and large-sized aggregate material, as illustrated in Figure 1 (14,18).…”

Automated Segmentation and Morphological Analyses of Stockpile Aggregate Images using Deep Convolutional Neural Networks

“…Tutumluer et al ( 20 ) developed, and Moaveni et al ( 21 ) improved, the enhanced-University of Illinois aggregate image analyzer (E-UIAIA) that uses a conveyor system to capture individual particles continuously from orthogonal views. Huang et al ( 22 ) recently developed a field imaging system for large-sized aggregates following a similar design concept. These variations of UIAIA all deal with single particle imaging with no touching or overlapping involved.…”