Novel invisible markers for monitoring cracks on masonry structures

This paper presents a proof of concept for monitoring masonry structures using two different types of markers which are not easily noticeable by human eye but exhibit high reflection when subjected to NIR (near-infrared) wavelength of light. The first type is a retroreflective marker covered by a special tape that is opaque in visible light but translucent in NIR, while the second marker is a paint produced from infrared reflective pigments. The reflection of these markers is captured by a special camera-flash com- bination and processed using image processing algorithms. A series of experiments were conducted to verify their potential to monitor crack development. It is shown that the difference between the actual crack width and the measured was satisfactorily small. Besides that, the painted markers perform better than the tape markers both in terms of accuracy and precision, while their accuracy could be in the range of 0.05 mm which verifies its potential to be used for measuring cracks in masonry walls or plastered and painted masonry surfaces. The proposed method can be particularly useful for heritage structures, and especially for acute problems like foundation settlement. Another advantage of the method is that it has been designed to be used by non-technical people, so that citizen involvement is also possible in col- lecting data from the field

Damage monitoring in fibre reinforced mortar by combined digital image correlation and acoustic emission

International audienceThe present work aims at developing a methodology for the detection and monitoring of damage and fractures in building materials in the prospects of energetic renovation. Digital image correlation (DIC) and acoustic emission (AE) monitoring were simultaneously performed during tensile loading tests of fibre reinforced mortar samples. The full-field displacement mappings obtained by DIC revealed all ranges of cracks, from microscopic to macroscopic, and an image processing procedure was conducted as to quantify their evolution in the course of the degradation of the samples. The comparison of these measurements with the acoustic activity of the material showed a fair match in terms of quantification and localisation of damage. It is shown that after such a calibration procedure, AE monitoring can be autonomously used for the characterisation of damage and fractures at larger scales

Automated pavement imaging program (APIP) for pavement cracks classification and quantification

This paper describes the development of an Automated Pavement Imaging Program (APIP) for evaluating pavement distress condition. The digital image processing program enables longitudinal, transverse, and alligator cracks to be classified. Subsequently, the program automatically predicts types of cracks and estimates the crack intensity which can be used to rate pavement distress severity. Results obtained by this technique are compared with the conventional manual method to check accuracy. The algorithm developed in this study is capable of identifying types of cracks and the severity level at about 90% accuracy, which is similar to the accuracy obtained by the manual method

Automatic detection of concrete cracks from images using Adam-SqueezeNet deep learning model

Cracks on concrete surface are typically clear warning signs of a potential threat to the integrity and serviceability of structure. The techniques based on image processing can effectively detect the cracks from images. These techniques, however, are generally susceptible to user-driven heuristic thresholds and extraneous distractors. Inspired by recent success of artificial intelligence, a deep learning based automated crack detection system called CrackSN was developed. An image dataset of concrete surface is collected by smartphone and carefully prepared in order to develop and train the CrackSN system. This proposed deep learning model, built on the Adam-SqueezeNet architecture, automatically learns the discriminative feature directly from the labeled and augmented patches. Hyperparameters of SqueezeNet are tuned with Adam optimization additive through the training and validation procedures. The fine-tuned CrackSN model outperforms state-of-the-art models in recent literature by correctly classifying 97.3% of the cracked patches in the image dataset. The success of CrackSN model demonstrated with light network design and outstanding performance provides a key step toward automated damage inspection and health evaluation for infrastructure. &nbsp