Using artificial neural network and non‐destructive test for crack detection in concrete surrounding the embedded steel reinforcement

Abstract Engineering structures may face various damages such as crack, delamination, and fatigue in several circumstances. Localizing such damages becomes essential to understand the health of the structures since they may not be able to operate anymore. Among the damage detection techniques, non-destructive methods are considerably more preferred than destructive methods since damage can be located without affecting the structural integrity. However, these methods have several drawbacks in terms of detecting abilities, time consumption, cost, and hardware or software requirements. Employing artificial intelligence techniques could overcome such issues and could provide a powerful damage detection model if the technique is utilized correctly. In this study, the crack localization in flat and folded plate structures has been conducted by employing a Back-propagated Artificial Neural Network (BPANN). For this purpose, cracks with 18 different dimensions have been modeled in flat and four different folded structures by utilizing the Finite Element Method. The dataset required to perform the crack localization procedure includes the first ten natural frequencies of all structures as input variables. As output variables, the dataset contains a total of 500 crack locations for five structures. It is concluded that the BPANN can localize all cracks with an average accuracy of 95.12%.

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Nondestructive Estimation of the Chlorophyll b of Apple Fruit by Color and Spectral Features Using Different Methods of Hybrid Artificial Neural Network

Agronomy ◽

10.3390/agronomy9110735 ◽

2019 ◽

Vol 9 (11) ◽

pp. 735 ◽

Cited By ~ 2

Author(s):

Yousef Abbaspour-Gilandeh ◽

Sajad Sabzi ◽

Mario Hernández-Hernández ◽

Jose Luis Hernández-Hernández ◽

Farzad Azadshahraki

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Coefficient Of Determination ◽

Chlorophyll B ◽

Cultural Algorithm ◽

Physicochemical Features ◽

Artificial Neural ◽

Non Destructive ◽

Nondestructive Estimation ◽

Hybrid Artificial Neural Network

Nondestructive estimation of the various physicochemical features of food such as fruits and vegetables will create a dramatic development in the food industry. The reason for this development is that the estimation is non-destructive, online, and most importantly fast. Regarding the advantages, various researchers have focused on how to undertake non-destructive estimation of the physicochemical features of various nutrients. Three main goals were pursued in this article. These are: 1. Nondestructive estimation of the chlorophyll b content of red delicious apple using color features and hybrid artificial neural network-cultural algorithm (ANN-CA), 2. Nondestructive estimation of chlorophyll b content of red delicious apple using spectral data (around a range of 680 nm) and hybrid Artificial Neural Network-biogeography-based algorithm (ANN-BBO), 3. Nondestructive estimation of the chlorophyll b content of red delicious apple using different groups of selective spectra by the hybrid artificial neural network-differential evolution algorithm (ANN-DA). In each of these methods, 1000 replications were performed to evaluate the reliability of various hybrids of the artificial neural network. Finally, the results indicated that the average determination coefficient in 1000 replications for the hybrid artificial neural network, the cultural algorithm, and the hybrid artificial neural network, the biogeography-based optimization algorithm, was 0.882 and 0.932, respectively. Also, the results showed that the highest value of the coefficient of determination among the different groups of effective features is related to the group of features with 10 spectra. The coefficient of determination in this case was 0.93.

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Data traffic filtration of automated non-destructive testing based on an artificial neural network

ITM Web of Conferences ◽

10.1051/itmconf/20181804003 ◽

2018 ◽

Vol 18 ◽

pp. 04003

Author(s):

Alisa Kosach ◽

Evgeny Kovshov

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Data Traffic ◽

Non Destructive Testing ◽

Multidimensional Signal Processing ◽

Destructive Testing ◽

Multidimensional Signal ◽

Artificial Neural ◽

Processing And Storage ◽

Non Destructive

The principle of constructing a universal software and hardware platform for the collection, processing and storage of data obtained as a result of non-destructive testing of products is considered. The platform also includes a neural network module for filtering data traffic, which has a wide potential for application: from image and multidimensional signal processing to data classification. The system of remote leakage control, built on the basis of a universal digital platform with artificial neural network, is described.

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Non-Destructive Estimation of Total Chlorophyll Content of Apple Fruit Based on Color Feature, Spectral Data and the Most Effective Wavelengths Using Hybrid Artificial Neural Network—Imperialist Competitive Algorithm

Plants ◽

10.3390/plants9111547 ◽

2020 ◽

Vol 9 (11) ◽

pp. 1547

Author(s):

Razieh Pourdarbani ◽

Sajad Sabzi ◽

Mario Hernández-Hernández ◽

José Luis Hernández-Hernández ◽

Iván Gallardo-Bernal ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Chlorophyll Content ◽

Spectral Data ◽

Total Chlorophyll ◽

Total Chlorophyll Content ◽

Color Data ◽

Artificial Neural ◽

Non Destructive ◽

Hybrid Artificial Neural Network

Non-destructive assessment of the physicochemical properties of food products, especially fruits, makes it possible to examine the internal quality without any damage. This is applicable at different stages of fruit growth, harvesting stage, and storage as well as at the market stage. In this regard, the present study aimed to estimate the total chlorophyll content using three types of data: color data, spectral data, and spectral data related to the most effective wavelengths. The most important steps of the proposed algorithms include extracting spectral and color data from each sample of Fuji cultivar apple, selecting the most effective wavelengths at the range of 660–720 nm using hybrid artificial neural network–particle swarm optimization (ANN-PSO), non-destructive assessment of the chemical property of total chlorophyll content based on color data, and spectral data using hybrid artificial neural network-Imperialist competitive algorithm (ANN-ICA). In order to assess the reliability of the hybrid ANN-ICA, 1000 iterations were performed after selecting the optimal structure of the artificial neural network. According to the results, in the best training mode and using spectral data and the most effective wavelength, total chlorophyll content was predicted with the R2 and RMSE of 0.991 and 0.0035, 0.997 and 0.001, 0.997 and 0.0006, respectively.

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