Geometrical model based scan planning approach for the classification of rebar diameters

The inspection of rebar diameter prior to concrete pouring is a crucial step for inspectors and fabricators to check the compliance to the designed bearing capacity and structural integrity of reinforced concrete structures during the stages of manufacturing and construction. Current practices for dimensional rebar inspection are, however, conducted manually using measurement tapes, which is labor-intensive and error prone. The authors' group have previously proposed laser scanning-based methods to perform rebar diameter and spacing estimation using laser scanning technologies. However, those prior studies have limitations in not only accurately classifying small-size rebar diameters but also determining optimal locations of laser scanner to ensure accurate rebar dimensional inspection. This study presents a scan planning method to select the optimal scan position of laser scanner for enhancing the rebar inspection accuracy. To this end, a model which defines the geometrical relationship between the rebar layout and the laser scanner is first developed to simulate the coordinates of scan points falling onto the rebar surface. Scan planning is then executed to determine the optimal laser scanner location where the best performance on rebar diameter prediction is achieved. For validation, a series of experimental tests including a comparison between the simulated and real scan data were performed on a rebar specimen. The results show that 1) a more than 90% similarity between the simulated and real scan data is achieved with respect to scan density and scan coverage on the rebar surface; and 2) a rebar classification accuracy of 89.3% is achieved, proving the validity of the developed mathematical model and scan planning approach.

Automated summary

The study proposes a scan planning method to select the optimal scan position of laser scanner for enhancing the rebar inspection accuracy. Experimental tests show a more than 90% similarity between the simulated and real scan data, and a rebar classification accuracy of 89.3%, validating the developed mathematical model and scan planning approach.