• Energy Research

Traditional spalling repair on concrete pavement roads is labor-intensive. It involves traffic blockages and the manual calculation of repair areas, leading to time-consuming processes with potential discrepancies. This study used a line scan camera to photograph road surface conditions to analyze spalling without causing traffic blockage in an indoor setting. By using deep learning algorithms, specifically a region-based convolutional neural network (R-CNN) in the form of the Mask R-CNN algorithm, the system detects spalling and calculates its area. The program processes data based on the Federal Highway Administration (FHWA) spalling repair standards. Accuracy was assessed using root mean square error (RMSE) and Pearson correlation coefficient (PCC) via comparisons with actual field calculations. The RMSE values were 0.0137 and 0.0167 for the minimum and maximum repair areas, respectively, showing high accuracy. The PCC values were 0.987 and 0.992, indicating a strong correlation between the actual and calculated repair areas, confirming the high calculation accuracy of the method.

Embedded sensors are widely employed for the structural health monitoring of structures constructed with concrete or mortar. Despite embedded sensors being actively used, there has been no study on whether or not the sensor probe placement within structures made of concrete or mortar influences their structural stability. The strength of small structures in particular could be affected by sensor probes embedded within them. To address the lack of research in this area, this study analyzed the effect of embedding positions of sensor probes on the compressive strength development of mortar. After the production of mortar specimens with the depth of the embedded sensor being controlled by the developed mold, compressive strength tests were conducted, and then test results were verified through finite element analysis. For testing, copper–nickel-plated sensor probes were embedded within the mortar because these sensor probes are popular commercial probes. The test results show that the compressive strength was 7.1 MPa when the sensor probe was embedded at a depth of 5 mm. In contrast, the compressive strength was 28.2 MPa at a depth of 30 mm. Since the compressive strength without the embedded sensor probe was 29.8 MPa, considering the results of this study, it is highly recommended that copper–nickel-plated sensor probes be embedded at least 30 mm from the surface of mortar structures.