Machine learning and image processing approaches for estimating concrete surface roughness using basic cameras

Abstract

Casting concrete at different ages for new construction and repairing or retrofitting concrete structures requires a sufficient bond between concrete casts. The bond strength between different casts is attributed to surface roughness. Surface roughness can be achieved in many ways, such as water‐jetting or sandblasting. To evaluate the degree of surface roughness, qualitative and quantitative methods are introduced by many researchers; however, several drawbacks are associated with most of these methods, including cost, availability, human errors, and inability to assess old structures from prior inspection records. Two novel industrial implementation methods are introduced in this paper to estimate, quantitatively, the concrete surface roughness from images with sufficient resolution. In the first application method, a digital image processing method is proposed to distinguish the coarse aggregate from cement paste, and a new index is presented as a function of aggregate proportional area to the surface area. In the second application method, data augmentation and transfer learning techniques in computer vision and machine learning are utilized to classify new images based on predefined images during the learning process. Both application methods were related to a well‐established method of 3D laser scanning from sandblasted concrete surfaces. Finally, a brand new set of images of sandblasted surfaces was used to test and validate both methods. The results show that both methods successfully estimate the concrete surface roughness with an accuracy of more than 93%.

Image Analysis of Surface Porosity Mortar Containing Processed Spent Bleaching Earth

“…The use of a digital camera in concrete image analysis has been attempted, but previous studies have required the use of a shooting cabin [ 15 ] or at least a setup in which the distance between the concrete and camera was kept constant at 300 mm [ 14 ]. In general, shotting at a specified distance is done to calibrate the area size of specimens based on the resolution or calibre of the image capturing device as well as the shooting distance [ 13 ]. However, this study provides a different method in which shooting distance can vary as long as the quality of the image is satisfactory.…”

“…A hand handled microscope has also been successfully utilised for concrete crack analysis [ 12 ]. Valikhani et al [ 13 ] evaluated the surface roughness and compressive strength of concrete through images of concrete cross-section. The experiment captured images with only a smartphone camera with 12 megapixels but achieved satisfactory results with the aid of machine learning techniques.…”

Improving Build Quality in Laser Powder Bed Fusion Using High Dynamic Range Imaging and Model-Based Reinforcement Learning

“…Recently, methods that incorporate the process of feature extraction from images into the ML pipeline, have been developed to predict surface roughness characteristics in industrial [36] and infrastructure engineering [37]. These models are based on recent developments in Deep Learning that led to a new class of neural networks known as convolutional neural networks [38], which allow to analyze high dimensional sensor and further process-related data more efficiently.…”

“…To quantitatively estimate the concrete surface roughness from high-resolution images in infrastructure engineering, an approach based on convolutional neural networks was recently developed and implemented [37].…”

Fire Resistance and Elevated Temperature in Reinforced Concrete Members: Research Needs for India

“…Robotics, new sensors, and algorithms for interpretation of data for non-destructive tests have been reported by Ahmed et al [88] for bridges and similar work could be relevant for examining the fire-ravaged structures as well. The developments might involve the latest scanning technologies [89,90] coupled with advanced techniques [artificial neural networks, deep learning, data fusion, etc.) for interpretation of the scan results [91][92][93][94][95][96][97][98].…”