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Applied Sciences
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Artificial Intelligence in Civil and Environmental Engineering
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Artificial Intelligence in Civil and Environmental Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 718

Special Issue Editors

Prof. Dr. Juan R. Rabuñal

E-Mail Website
Guest Editor
Center for Technological Innovation in Building and Civil Engineering (CITEEC) & Department of Computer Science and Information Technologies, University of A Coruña, Campus Elviña, 15071 A Coruña, Spain
Interests: artificial intelligence techniques applied to civil engineering: artificial neural networks; evolutionary computation; artificial vision; ports and coasts; hydrology; construction; environmental science
Dr. Marcos Gestal

E-Mail Website
Guest Editor
Department of Computer Science and Information Technologies, University of A Coruña, Campus Elviña, 15071 A Coruña, Spain
Interests: evolutionary computation; artificial neural networks; artificial intelligence; feature selection; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Achieving algorithms that mimic intelligent behavior has been a research goal since the mid-1950s for, among others, John McCarthy, Warren McCullough and Walter Pitts. As a result of this research, techniques such as Artificial Neural Networks, Genetic Algorithms and Genetic Programming have emerged.

After overcoming a small bump in recent years, they have been revived under the name of Deep Neural Networks, or Deep Learning; These networks have a similar (and simpler) basis to brain functioning and take advantage of the emergence of new learning algorithms and greater computational capabilities.

In the last decade, advances in the field of Artificial Intelligence have had and are having a strong influence on the different fields of civil and environmental engineering. New methods of applying AI algorithms are emerging that allow civil engineers to use computer science in different ways to solve real problems in the fields within civil engineering and environmental sciences: hydrology, concrete and construction, ports and coasts, Waste Management, the Atmospheric Environment, Climate Change, Environmental Health, Renewable Energy, etc.

This Special Issue aims to accommodate, on the one hand, the latest theoretical advances in this field, such as new learning paradigms, new algorithms and new architectures in Artificial Intelligence, and, on the other hand, more recent works in the scientific field in which the authors have used an Artificial Intelligence algorithm to solve a problem in the fields of civil and environmental engineering.

We invite researchers and investigators to contribute their original research or review articles to this Special Issue.

Prof. Dr. Juan R. Rabuñal
Dr. Marcos Gestal
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.

Keywords

  • artificial intelligence, machine learning, deep learning
  • applications of artificial intelligence in civil and environmental engineering
    • hydrology
    • construction: concrete, steel, new materials
    • ports and coasts
    • transport
    • waste management
    • atmospheric environment
    • climate change
    • environmental health
    • renewable energy

Published Papers (1 paper)

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Research

22 pages, 1259 KiB  
Article
Deep Learning-Based Wave Overtopping Prediction
by Alberto Alvarellos, Andrés Figuero, Santiago Rodríguez-Yáñez, José Sande, Enrique Peña, Paulo Rosa-Santos and Juan Rabuñal
Appl. Sci. 2024, 14(6), 2611; https://doi.org/10.3390/app14062611 - 20 Mar 2024
Viewed by 535
Abstract
This paper analyses the application of deep learning techniques for predicting wave overtopping events in port environments using sea state and weather forecasts as inputs. The study was conducted in the outer port of Punta Langosteira, A Coruña, Spain. A video-recording infrastructure was [...] Read more.
This paper analyses the application of deep learning techniques for predicting wave overtopping events in port environments using sea state and weather forecasts as inputs. The study was conducted in the outer port of Punta Langosteira, A Coruña, Spain. A video-recording infrastructure was installed to monitor overtopping events from 2015 to 2022, identifying 3709 overtopping events. The data collected were merged with actual and predicted data for the sea state and weather conditions during the overtopping events, creating three datasets. We used these datasets to create several machine learning models to predict whether an overtopping event would occur based on sea state and weather conditions. The final models achieved a high accuracy level during the training and testing stages: 0.81, 0.73, and 0.84 average accuracy during training and 0.67, 0.48, and 0.86 average accuracy during testing, respectively. The results of this study have significant implications for port safety and efficiency, as wave overtopping events can cause disruptions and potential damage. Using deep learning techniques for overtopping prediction can help port managers take preventative measures and optimize operations, ultimately improving safety and helping to minimize the economic impact that overtopping events have on the port’s activities. Full article
(This article belongs to the Special Issue Artificial Intelligence in Civil and Environmental Engineering)
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