Concrete Crack Detection and Monitoring Using a Capacitive Dense Sensor Array

Cracks in concrete structures can be indicators of important damage and may significantly affect durability. Their timely identification can be used to ensure structural safety and guide on-time maintenance operations. Structural health monitoring solutions, such as strain gauges and fiber optics systems, have been proposed for the automatic monitoring of such cracks. However, these solutions become economically difficult to deploy when the surface under investigation is very large. This paper proposes to leverage a novel sensing skin for monitoring cracks in concrete structures. This sensing skin is constituted of a flexible electronic termed soft elastomeric capacitor, which detects a change in strain through changes in measured capacitance. The SEC is a low-cost, durable, and robust sensing technology that has previously been studied for the monitoring of fatigue cracks in steel components. In this study, the sensing skin is introduced and preliminary validation results on a small-scale reinforced concrete beam are presented. The technology is verified on a full-scale post-tensioned concrete beam. Results show that the sensing skin is capable of detecting, localizing, and quantifying cracks that formed in both the reinforced and post-tensioned concrete specimens

Crack Localization and Detection in Small-Scale Reinforced Concrete Beams With Smart Technology

Reinforced concrete structures form the backbone of modern civil engineering, yet the emergence of cracks poses a significant challenge to their long-term integrity. The integration of smart sensors and data analytics further augments precision by enabling real-time data collection and analysis, allowing for early intervention. Continuous monitoring, facilitated by remote sensing and wireless communication, ensures a dynamic understanding of crack propagation. To validate the proposed approach, an experimental campaign was conducted using reinforced concrete beams. Three point bending tests were conducted on two small-scale reinforced concrete beams. Different configurations of SEC arrays were used on the two specimens to assess the capacity and limitation of the proposed approach. Results show that the sensing skin was capable of detecting and localizing cracks that formed in both specimens

Semantic Segmentation Using Modified U-Net Architecture for Crack Detection

The visual inspection of a concrete crack is essential to maintaining its good condition during the service life of the bridge. The visual inspection has been done manually by inspectors, but unfortunately, the results are subjective. On the other hand, automated visual inspection approaches are faster and less subjective. Concrete crack is an important deficiency type that is assessed by inspectors. Recently, various Convolutional Neural Networks (CNNs) have become a prominent strategy to spot concrete cracks mechanically. The CNNs outperforms the traditional image processing approaches in accuracy for the high-level recognition task. Of them, U-Net, a CNN based semantic segmentation method, has been one of the most popular in the deep learning because of its excellent performance in open-source crack classification. Although the results of the trained U-Net look good for some dataset, the model still requires further improvement for the set of hard examples of concrete crack that contains the stain, waterspot, and small width crack. In this paper, we address the challenging problem of accurately detecting a thin concrete crack. We designed a U-Net like structure that has a contracting path and an expansive path to overcome this challenge and compared it to current models, including original U-Net and pyramid pooling module network. The proposed architecture utilizes multiple feature maps in a down-sampling path to obtain a higher pixel-level segmentation precision. The down-sampled feature is then up-sampled from the output of the pyramid pooling module [13], giving a binary crack and non-crack semantic segmentation. In the experiment, we have collected hard examples and evaluated the approach. Experimental results demonstrate that the proposed network outperforms the U-Net and a pyramid pooling module network in detecting a thin crack in a noisy environment

Automatic Crack Detection Using Convolutional Neural Network

Manual inspection of cracks on concrete surfaces requires wholesome knowledge and depends entirely on the expertise and capabilities of the inspector. This study proposes the use of a simple Convolutional Neural Network (CNN) for automatic crack detection. A comparative approach for Automated Crack Detection is presented between Feed-Forward Fully Connected Neural Networks and CNN, focusing on the primary hyperparameters affecting the accuracy of both systems. An inclination towards CNN is concluded due to its simplicity and computational efficiency. For the purpose of this study, the input data is extracted from an open-source platform. In the second step, the images are pre-processed for obtaining low-pixel density images with the aim to get better accuracy at lower computer power. The CNN proposed uses Max Pooling and appropriate optimization techniques. The model is trained to detect and segregate cracked and non-cracked concrete surfaces through input images. The proposed model predicts and labels images with cracks on concrete surfaces and images with no cracks using pixel-level information. The final accuracy achieved is 97.8% by the proposed CNN model. The proposed model is a novel approach to detecting cracks on low pixel density images of concrete surfaces for its economic and processing efficiency and thus eliminates the need for high-cost digital image capturing devices. This study signifies and confirms the impact of Artificial Intelligence in the Civil Engineering field where using simple techniques like a simple four-layered Neural Network is capable of carrying automatic inspection of cracks which can be further developed for other applications

Concrete Crack Detection and Monitoring Using a Capacitive Dense Sensor Array

Cracks in concrete structures can be indicators of important damage and may significantly affect durability. Their timely identification can be used to ensure structural safety and guide on-time maintenance operations. Structural health monitoring solutions, such as strain gauges and fiber optics systems, have been proposed for the automatic monitoring of such cracks. However, these solutions become economically difficult to deploy when the surface under investigation is very large. This paper proposes to leverage a novel sensing skin for monitoring cracks in concrete structures. This sensing skin is constituted of a flexible electronic termed soft elastomeric capacitor, which detects a change in strain through changes in measured capacitance. The SEC is a low-cost, durable, and robust sensing technology that has previously been studied for the monitoring of fatigue cracks in steel components. In this study, the sensing skin is introduced and preliminary validation results on a small-scale reinforced concrete beam are presented. The technology is verified on a full-scale post-tensioned concrete beam. Results show that the sensing skin is capable of detecting, localizing, and quantifying cracks that formed in both the reinforced and post-tensioned concrete specimens.This article is published as Yan, Jin, Austin Downey, Alessandro Cancelli, Simon Laflamme, An Chen, Jian Li, and Filippo Ubertini. "Concrete Crack Detection and Monitoring Using a Capacitive Dense Sensor Array." Sensors 19, no. 8 (2019): 1843. DOI: 10.3390/s19081843. Posted with permission.</p