Artificial neural networks for nondestructive identification of the interlayer bonding between repair overlay and concrete substrate Abstract   The article presents the application of artificial neural networks (ANNs) for the nondestructive identification of the pulloff adhesion fb values between the repair overlay with variable thickness and the substrate in concrete surfacerepaired elements. For this purpose, a large database was built on the basis of the tests of model concrete elements. The numerical analyses were performed using this data and ANNs with various learning algorithms. Based on these analyses, it was shown that the ANN with the BroydenFletcherGoldfarbShanno learning algorithm, with thirtyone input parameters and twenty hidden neurons, is the most useful for identifying the interlayer pulloff adhesion in repaired concrete elements. The reliability of the presented application of ANNs was confirmed on the basis of carried out validation, using a part of the database not used in the learning and testing. The applicationu0027s reliability was also confirmed on the basis of experimental verification carried out using the results of tests performed on an additional model element made exclusively for this purpose. This is an important and original issue presented in the article. Another novelty presented in the article is the application of ANNs for a much more difficult case, which is the identification of the pulloff adhesion fb value of the repair overlay of variable thickness from the repaired element and in a very wide range of identified pulloff adhesion fb within the range of 0.5xe2x80x933.60xc2xa0MPa. Moreover, the unique value of the article is the use for the first time of spatial and function related parameters to describe the concrete surface morphology of a repaired element. The investigation presented in the article has also confirmed the high usefulness of these parameters for identifying the value of pulloff adhesion fb.

Deep Neural Network based Visual Inspection with 3D Metric Measurement of Concrete Defects using Wallclimbing Robot This paper presents a novel metric inspection robot system using a deep neural network to detect and measure surface flaws (i.e., crack and spalling) on concrete structures performed by a wallclimbing robot. The system consists of four modules: robotics data collection module to obtain RGBD images and IMU measurement, visualinertial SLAM module to generate pose coupled keyframes with depth information, InspectionNet module to classify each pixel into three classes (background, crack and spalling), and 3D registration and map fusion module to register the flaw patch into registered 3D model overlaid and highlighted with detected flaws for spatialcontextual visualization. The system enables the metric model of each surface flaw patch with pixellevel accuracy and determines its location in 3D space that is significant for structural health assessment and monitoring. The InspectionNet achieves an average accuracy of 87.64% for crack and spalling inspection. We also demonstrate our InspectionNet is robust to view angle, scale and illumination variation. Finally, we design a metric voxel volume map to highlight the flaw in 3D model and provide location and metric information.