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Applications of Deep Learning in Engineering Structures
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Applications of Deep Learning in Engineering Structures

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (10 February 2022) | Viewed by 16356

Special Issue Editors

Dr. Visar Farhangi

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas (UNLV), Las Vegas, NV 89557, USA
Interests: geotechnical engineering, structural engineering, civil engineering; polymers; foundation; liquefaction; pile; deep foundation; finite element
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Moses Karakouzian

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas (UNLV), Las Vegas, NV 89557, USA
Interests: geotechnical engineering; construction materials; hydraulics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The main topic of this issue has attracted wide attention among researchers, and it deals with various applications of soft computing methods such as artificial neural networks (ANNs) and artificial intelligence (AI) on predicting the engineering response of structures and material science, including but not limited to steel and concrete structures and deep foundations.

This Special Issue will cover novel studies in different disciplines of civil engineering, including structural engineering, geotechnical engineering, and material sciences.

Dr. Visar Farhangi
Prof. Dr. Moses Karakouzian
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

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28 pages, 12591 KiB  
Article
Behaviour Investigation of SMA-Equipped Bar Hysteretic Dampers Using Machine Learning Techniques
by Visar Farhangi, Hashem Jahangir, Danial Rezazadeh Eidgahee, Arash Karimipour, Seyed Alireza Nedaei Javan, Hamed Hasani, Nazanin Fasihihour and Moses Karakouzian
Appl. Sci. 2021, 11(21), 10057; https://doi.org/10.3390/app112110057 - 27 Oct 2021
Cited by 15 | Viewed by 3127
Abstract
Most isolators have numerous displacements due to their low stiffness and damping properties. Accordingly, the supplementary damping systems have vital roles in damping enhancement and lower the isolation system displacement. Nevertheless, in many cases, even by utilising additional dampers in isolation systems, the [...] Read more.
Most isolators have numerous displacements due to their low stiffness and damping properties. Accordingly, the supplementary damping systems have vital roles in damping enhancement and lower the isolation system displacement. Nevertheless, in many cases, even by utilising additional dampers in isolation systems, the occurrence of residual displacement is inevitable. To address this issue, in this study, a new smart type of bar hysteretic dampers equipped with shape memory alloy (SMA) bars with recentring features, as the supplementary damper, is introduced and investigated. In this regard, 630 numerical models of SMA-equipped bar hysteretic dampers (SMA-BHDs) were constructed based on experimental samples with different lengths, numbers, and cross sections of SMA bars. Furthermore, by utilising hysteresis curves and the corresponding ideal bilinear curves, the role of geometrical and mechanical parameters in the cyclic behaviour of SMA-BHDs was examined. Due to the deficiency of existing analytical models, proposed previously for steel bar hysteretic dampers (SBHDs), to estimate the first yield point displacement and post-yield stiffness ratio in SMA-BHDs accurately, new models were developed by the artificial neural network (ANN) and group method of data handling (GMDH) approaches. The results showed that, although the ANN models outperform GMDH ones, both ANN- and GMDH-based models can accurately estimate the linear and nonlinear behaviour of SMA-BHDs in pre- and post-yield parts with low errors and high accuracy and consistency. Full article
(This article belongs to the Special Issue Applications of Deep Learning in Engineering Structures)
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22 pages, 5886 KiB  
Article
A Framework of Structural Damage Detection for Civil Structures Using Fast Fourier Transform and Deep Convolutional Neural Networks
by Yingying He, Hongyang Chen, Die Liu and Likai Zhang
Appl. Sci. 2021, 11(19), 9345; https://doi.org/10.3390/app11199345 - 8 Oct 2021
Cited by 26 | Viewed by 3101
Abstract
In the field of structural health monitoring (SHM), vibration-based structural damage detection is an important technology to ensure the safety of civil structures. By taking advantage of deep learning, this study introduces a data-driven structural damage detection method that combines deep convolutional neural [...] Read more.
In the field of structural health monitoring (SHM), vibration-based structural damage detection is an important technology to ensure the safety of civil structures. By taking advantage of deep learning, this study introduces a data-driven structural damage detection method that combines deep convolutional neural networks (DCNN) and fast Fourier transform (FFT). In this method, the structural vibration data are fed into FFT method to acquire frequency information reflecting structural conditions. Then, DCNN is utilized to automatically extract damage features from frequency information to identify structural damage conditions. To verify the effectiveness of the proposed method, FFT-DCNN is carried out on a three-story building structure and ASCE benchmark. The experimental result shows that the proposed method achieves high accuracy, compared with classic machine-learning algorithms such as support vector machine (SVM), random forest (RF), K-Nearest Neighbor (KNN), and eXtreme Gradient boosting (xgboost). Full article
(This article belongs to the Special Issue Applications of Deep Learning in Engineering Structures)
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Review

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24 pages, 968 KiB  
Review
A Comprehensive Review of Deep Learning-Based Crack Detection Approaches
by Younes Hamishebahar, Hong Guan, Stephen So and Jun Jo
Appl. Sci. 2022, 12(3), 1374; https://doi.org/10.3390/app12031374 - 27 Jan 2022
Cited by 52 | Viewed by 8471
Abstract
The application of deep architectures inspired by the fields of artificial intelligence and computer vision has made a significant impact on the task of crack detection. As the number of studies being published in this field is growing fast, it is important to [...] Read more.
The application of deep architectures inspired by the fields of artificial intelligence and computer vision has made a significant impact on the task of crack detection. As the number of studies being published in this field is growing fast, it is important to categorize the studies at deeper levels. In this paper, a comprehensive literature review of deep learning-based crack detection studies and the contributions they have made to the field is presented. The studies are categorised according to the computer vision aspect and at deeper levels to facilitate exploring the studies that utilised similar approaches to address the crack detection problem. Moreover, the authors perform a comparison between the studies which use the same publicly available data sets, in order to find the most promising crack detection approaches. Critical future directions for research are proposed, based on these reviewed studies as well as on trends and developments in areas similar to the crack detection area. Full article
(This article belongs to the Special Issue Applications of Deep Learning in Engineering Structures)
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