ABSTRACT
Buildings and infrastructure in congested metropolitan areas are continuously deteriorating. Various structural flaws such as surface cracks, spalling, delamination, and other defects are found, and keep on progressing. Traditionally, the assessment and inspection is conducted by humans; however, due to human physiology, the assessment limits the accuracy of image evaluation, making it more subjective rather than objective. Thus, in this study, a multivariant defect recognition technique was developed to efficiently assess the various structural health issues of concrete. The image dataset used was comprised of 3650 different types of concrete defects, including surface cracks, delamination, spalling, and non-crack concretes. The proposed scheme of this paper is the development of an automated image-based concrete condition recognition technique to categorize, not only non-defective concrete into defective concrete, but also multivariant defects such as surface cracks, delamination, and spalling. The developed convolution-based model multivariant defect recognition neural network can recognize different types of defects on concretes. The trained model observed a 98.8% defect detection accuracy. In addition, the proposed system can promote the development of various defect detection and recognition methods, which can accelerate the evaluation of the conditions of existing structures.
Subject(s)
Neural Networks, Computer , Recognition, Psychology , HumansABSTRACT
With the growing demand for structural health monitoring system applications, data imaging is an ideal method for performing regular routine maintenance inspections. Image analysis can provide invaluable information about the health conditions of a structure's existing infrastructure by recording and analyzing exterior damages. Therefore, it is desirable to have an automated approach that reports defects on images reliably and robustly. This paper presents a multivariate analysis approach for images, specifically for assessing substantial damage (such as cracks). The image analysis provides graph representations that are related to the image, such as the histogram. In addition, image-processing techniques such as grayscale are also implemented, which enhance the object's information present in the image. In addition, this study uses image segmentation and a neural network, for transforming an image to analyze it more easily and as a classifier, respectively. Initially, each concrete structure image is preprocessed to highlight the crack. A neural network is used to calculate and categorize the visual characteristics of each region, and it shows an accuracy for classification of 98%. Experimental results show that thermal image extraction yields better histogram and cumulative distribution function features. The system can promote the development of various thermal image applications, such as nonphysical visual recognition and fault detection analysis.
Subject(s)
Image Processing, Computer-Assisted , Thermography , Multivariate Analysis , Neural Networks, ComputerABSTRACT
An adenoviral vector (Ad) expressing a Wnt decoy receptor (sLRP6E1E2) is known to induce an anti-fibrotic effect by inhibiting Wnt signaling. We evaluated its effects in vivo using pig models and attempted to introduce an alginate gel-matrix system to prolong the effect of the Ad. Transduction efficiency as to the biological activity of Ad in different forms was evaluated. Then, 50 days after the formation of full-thickness skin defects on the backs of Yorkshire pigs, scars were treated with each form of Ad. Therapeutic efficacy and various factors influencing scar formation and collagen rearrangement were analyzed. Inflammatory cell infiltration within the scar tissues was also evaluated. Decoy Wnt receptor (sLRP6E1E2)-expressing adenovirus treatment improved scar quality in a pig model. Loading this construct in alginate gel allows sustained virus release into local tissues and prolongs Ad activity, thus maintaining its therapeutic effect longer in vivo.
