Automated Estimation of Reinforced Precast Concrete Rebar Positions Using Colored Laser Scan Data

Abstract

Precast concrete elements are widely adopted and the performance of precast structures is relying on the quality of connections between adjacent elements. For reinforced precast concrete elements, rebar positions are important for the overall structural performance, however, they are usually manually inspected. This study develops a technique for automated position estimation of rebars on reinforced precast concrete elements using colored laser scan data. A novel mixed pixel filter is developed to remove mixed pixels from the raw scan data based on both distance and color difference. A one-class classifier is used for extracting rebars from all the data based on both geometric and color features of points. Furthermore, a novel rebar recognition algorithm is developed to recognize individual rebars based on two newly defined metrics. Experiments on two reinforced precast concrete bridge deck panels were conducted and showed that the proposed technique can accurately and efficiently estimate rebar positions. C 2017 Computer-Aided Civil and Infrastructure Engineering.

From Semantic Segmentation to Semantic Registration: Derivative-Free Optimization–Based Approach for Automatic Generation of Semantically Rich As-Built Building Information Models from 3D Point Clouds

“…One can capture the measurements of a facility on foot with a handheld augmented reality (AR) smartphone. For larger facilities, one can use a vehicle-borne light detection and ranging (LiDAR) scanner, a consumer unmanned aerial vehicle (UAV) or drone flown at low altitude, fixed-wing aircraft-borne LiDAR at a high altitude, or interferometric synthetic aperture radar (InSAR) at low Earth orbit (Tang et al, 2010;Zhu and Shahzad, 2014;Wang et al, 2017). If the directly measured data exists as multiple 2D images, structure from motion (SfM) or simultaneous localization and mapping (SLAM) can convert the images to 3D point clouds by estimating the camera positions and fusing with 3D scenes (Snavely et al, 2008).…”

“…The latter, which can be collected using any type of 3D scanner, have become increasingly affordable and thus popular in recent years (Tang et al, 2010;Wang et al, 2017). Considerable advances have also been made in point cloud preparation and pre-processing proficiencies (e.g., Song et al, 2014;Zhang et al, 2016) in model generation methods (e.g., Bosché et al, 2015;Díaz-Vilariño et al, 2015) and for semantic enrichment (Belsky et al, 2016;Hamledari et al, 2017;Sacks et al, 2017).…”

Towards Automatic Segmentation and Recognition of Multiple Precast Concrete Elements in Outdoor Laser Scan Data

“…Since the 3D coordinates and color information of the scanned object could be obtained by using a laser scanner with a built-in camera, the acquisition of color information provided another way to process the scanned PCE data. Wang et al [41] developed a technique of automated position estimation of the rebars in reinforced PCEs. The authors recognized rebars based on the RGB values of the laser scan data and as-designed models of the rebar.…”

“…Figure 14 shows the results of coarse matching and fine matching for the PC exterior wall panel. To further identify PCEs with small shape differences, based on the fine matching results, the degree of completeness (DOC) proposed by Wang et al [41] is adopted in this study as the evaluation. The DOC value can be calculated by Equation 5.…”

Optimization of Temporary Debris Management Site Selection and Site Service Regions for Enhancing Postdisaster Debris Removal Operations

“…Multiple remote sensing technologies such as satellite imagery, aerial imagery, Light Detection and Ranging (LiDAR) have been studied for debris volume estimation (Adeli and Karim, 1997;Bull et al, 2010;Riveiro et al, 2016;Wang et al, 2017;Park et al, 2018). These technologies have shown great potential in providing valuable information regarding the volume and distribution of disaster debris.…”