Abstract
SHS investigation development is considered from the geographical and historical viewpoint. 3 stages are described. Within Stage 1 the work was carried out in the Department of the Institute of Chemical Physics in Chernogolovka where the scientific discovery had been made. At Stage 2 the interest to SHS arose in different cities and towns of the former USSR. Within Stage 3 SHS entered the international scene. Now SHS processes and products are being studied in more than 50 countries.
Abstract
Crack detection has vital importance for monitoring and inspection of
buildings. It has great significance for structural safety. This is a challenging task for
computer vision and machine learning, as cracks only have low-level features for
detection. Convolutional Neural Networks (CNN) is a very promising framework for
crack detection from images with high accuracy and precision. This paper is based on a
deep-learning methodology to detect and recognize structural defects. The dataset is
split into training and testing data which is used to train the model. Then this trained
model is used to recognize and classify cracks in images. The dataset consists of
concrete crack images. The data set used has two categories, images with cracks and
without cracks. A Convolutional Neural Network model using Pytorch will be fit to
predict the images if the images have any cracks or not. This paper compares the
accuracy of various models.
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Authors:Bentham Science Books