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Abstract:Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.Keywords: deep learning; machine learning; structural health monitoring; crack detection; damage detection; data science; computer vision
Abstract:At present, a number of computer vision-based crack detection techniques have been developed to efficiently inspect and manage a large number of structures. However, these techniques have not replaced visual inspection, as they have been developed under near-ideal conditions and not in an on-site environment. This article proposes an automated detection technique for crack morphology on concrete surface under an on-site environment based on convolutional neural networks (CNNs). A well-known CNN, AlexNet is trained for crack detection with images scraped from the Internet. The training set is divided into five classes involving cracks, intact surfaces, two types of similar patterns of cracks, and plants. A comparative study evaluates the successfulness of the detailed surface categorization. A probability map is developed using a softmax layer value to add robustness to sliding window detection and a parametric study was carried out to determine its threshold. The applicability of the proposed method is evaluated on images taken from the field and real-time video frames taken using an unmanned aerial vehicle. The evaluation results confirm the high adoptability of the proposed method for crack inspection in an on-site environment.Keywords: crack; deep learning; convolutional neural networks; AlexNet; unmanned aerial vehicle
The home security system using Neural Network which is suggested here is harder to be hacked. The Neural Network is used to train (learning) the identification parameters like UserID and password. Such a network acts as a brain in securing of passwords without constraints. One of the most well known types of neural network is the Multilayer Perceptrons Neural Network (MLPs).Such a perceptron network makes use of Backpropagation Algorithm which is a supervised artificial neural network (ANN) [2].Here, Resilient Backpropagation Technique is used to accelerate the training epochs [3]. Using this system it is safe
This is a mandatory step as the inputs that we are dealing with are alphanumeric (characters, numbers, symbols etc.) and the system used is a neural network which deals with binary numbers. Hence, the inputs must be converted into their binary equivalent which could be their own ASCII codes. The only requirement is to feed (or train) the network with the data that we encode through the encoder.
System will be initialized on the first instance, wherein the user will provide an appropriate UserID and password. Training of the data is done after the users have registered the User ID and password, and before the users are able to login the system. The UserID will be accepted by the input layer of the neural network as the input and the password will hence be considered as the target. Such an input/output pair will be used to train the neural network using the RBPN for thousands of epochs for better learning. Once the training is done the system is ready to work, when the User enters the UserID and password, the encoded UserID is taken as an input to the BPN, which performs the algorithm and produces an output. Now, this output is compared with the encoded password which was entered by the user. If they match, the Login is successful and the user is genuine else the access is denied.
The use of neural networks provides the benefit to eliminate the disadvantages of maintaining the conventional verification table which not only requires memory to store but also reduces the speed of the operation. Hence, by using ANN the conventional verification table can be made redundant and the speed of operation increases. 2b1af7f3a8