Special Issue "Artificial Neural Networks Applied in Civil Engineering"

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

Deadline for manuscript submissions: 30 April 2021.

Special Issue Editor

Dr. Nikos D. Lagaros
Website SciProfiles
Guest Editor
Institute of Structural Analysis & Antiseismic Research, Department of Structural Engineering, School of Civil Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str., Zografou Campus, Athens 157 80, Greece
Interests: structural design optimization; shape and topology optimization; structural health monitoring; artificial intelligence; machine learning; nano modelling; additive manufacturing

Special Issue Information

Dear Colleagues,

In recent years, artificial neural networks (ANN) and artificial intelligence (AI) in general have drawn significant attention with respect to their applications in several scientific fields, varying from big data handling to medical diagnosis. The use of ANN techniques is already present in everyday applications everyone uses, such as personalized ads, virtual assistants, autonomous driving, etc. The breakthrough of ANNs can be traced back to the year 2005 and forward with the proposal of novel learning architectures such as deep convolutional neural networks (CNN) and deep belief networks (DBN), while significant progress has been achieved so far, and new methodologies are being proposed, such as generative adversarial neural networks (GAN). At present, ANN techniques are widely used in several forms of engineering applications.

It is our great pleasure to invite you to contribute to this Special Issue by presenting your results on applications and advances of ANN to civil engineering problems. Papers can focus on applications related to structural engineering, transportation engineering, geotechnical engineering, hydraulic engineering, environmental engineering, coastal and ocean engineering, structural health monitoring, as well as construction management. Articles submitted to this Special Issue could also deal with the most significant recent developments on the topics of ANN and its application in civil engineering. The papers can present modeling, optimization, control, measurements, analysis, and applications.

Dr. Nikos Lagaros
Guest Editor

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 papers will be 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 1800 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.

Keywords

  • Deep learning
  • IoT and real-time monitoring
  • Optimization
  • Learning systems
  • Mathematical and computational analysis

Published Papers (1 paper)

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Research

Open AccessArticle
Applying Statistical Analysis and Machine Learning for Modeling the UCS from P-Wave Velocity, Density and Porosity on Dry Travertine
Appl. Sci. 2020, 10(13), 4565; https://doi.org/10.3390/app10134565 - 30 Jun 2020
Abstract
In the rock mechanics and rock engineering field, the strength parameter considered to characterize the rock is the uniaxial compressive strength (UCS). It is usually determined in the laboratory through a few statistically representative numbers of specimens, with a recommended minimum of five. [...] Read more.
In the rock mechanics and rock engineering field, the strength parameter considered to characterize the rock is the uniaxial compressive strength (UCS). It is usually determined in the laboratory through a few statistically representative numbers of specimens, with a recommended minimum of five. The UCS can also be estimated from rock index properties, such as the effective porosity, density, and P-wave velocity. In the case of a porous rock such as travertine, the random distribution of voids inside the test specimen (not detectable in the density-porosity test, but in the compressive strength test) causes large variations on the UCS value, which were found in the range of 62 MPa for this rock. This fact complicates a sufficiently accurate determination of experimental results, also affecting the estimations based on regression analyses. Aiming to solve this problem, statistical analysis, and machine learning models (artificial neural network) was developed to generate a reliable predictive model, through which the best results for a multiple regression model between uniaxial compressive strength (UCS), P-wave velocity and porosity were obtained. Full article
(This article belongs to the Special Issue Artificial Neural Networks Applied in Civil Engineering)
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