Application of Artificial Intelligence to Model the Behavior of Infrastructure

A special issue of Infrastructures (ISSN 2412-3811). This special issue belongs to the section "Smart Infrastructures".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 3214

Special Issue Editors

Research Associate, Lyles School of Civil Engineering, Purdue Univeristy, 550 W Stadium Ave., West Lafayette, IN 47906, USA
Interests: artificial intelligence; supplementary cementitious materials; concrete durability; multifunctional materials; additive manufacturing; sustainability; microstructure; advanced characterization techniques
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Assistant Professor, Department of Civil Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
Interests: artificial intelligence; metaheuristic optimization algorithms; multicriteria decision making; new construction materials; sustainability; high-performance concrete; waste materials

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Guest Editor
Engineering Faculty, Near East University, North Cyprus, Mersin 10, Turkey
Interests: structural optimization; computational mechanics; multidisciplinary optimization

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Guest Editor
Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
Interests: structural health monitoring; smart civil infrastructure systems; deployment of advanced sensors; energy harvesting; civil engineering system informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Infrastructures including structures, pavements, bridge, dams, etc. are usually exposed to various environmental and loading conditions during their service life. Understanding the behavior of these elements is of utmost importance for their properly design and effective conduct of maintenance practices. Conventional methods to model the behavior of infrastructures rely on time-consuming and expensive techniques, which may also require highly trained personnel. Artificial intelligence (AI)-based techniques have been widely applied in many engineering fields due to their capabilities in computation and knowledge processing. AI-based techniques, including machine learning (ML) and deep learning (DL), are effective empirical approaches for both classification (supervised and unsupervised) and/or regression of nonlinear systems. Moreover, there are a wide range of metaheuristic optimization algorithms that can be used in the field of infrastructures. These novel techniques can be used for optimizing the design, maintenance, rehabilitation, and strengthening strategies of infrastructures.

This Special Issue focuses on the latest research findings in the area of the applications of AI to model the behavior of infrastructures. Various original and novel research topics will be considered, including but not limited to: 

  • Leveraging AI to automatically process the data obtained through destructive experiments and non-destructive evaluation;
  • Application of AI for health monitoring of infrastructures;
  • The use of AI-based techniques for maintenance and rehabilitation purposes;
  • Applications of AI to monitor the performance of infrastructures;
  • Hybrid models to develop solutions for multiobjective problems of infrastructures;
  • Serving AI-based methods for the inspection of infrastructures;
  • Employing metaheuristic optimization algorithms for optimizing the design, maintenance, rehabilitation, and strengthening strategies of infrastructures;
  • The use of BIM and AI-based methods for the design, maintenance, rehabilitation, and strengthening of infrastructures.

Dr. Ali Behnood
Dr. Emadaldin Mohammadi Golafshani
Dr. Siamak Talatahari
Dr. Amir H. Alavi
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. Infrastructures is an international peer-reviewed open access monthly 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

  • Infrastructures
  • Artificial intelligence
  • Machine learning
  • Deep learning
  • Hybrid models
  • Artificial neural network
  • Symbolic regression
  • Evolutionary methods
  • Metaheuristic optimization algorithm
  • Multiobjective optimization
  • Classification algorithms
  • BIM

Published Papers (1 paper)

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Research

22 pages, 8842 KiB  
Article
Deep Reinforcement Learning Model to Mitigate Congestion in Real-Time Traffic Light Networks
by Fábio de Souza Pereira Borges, Adelayda Pallavicini Fonseca and Reinaldo Crispiniano Garcia
Infrastructures 2021, 6(10), 138; https://0-doi-org.brum.beds.ac.uk/10.3390/infrastructures6100138 - 26 Sep 2021
Cited by 2 | Viewed by 2309
Abstract
Urban traffic congestion has a significant detrimental impact on the environment, public health and the economy, with at a high cost to society worldwide. Moreover, it is not possible to continually modify urban road infrastructure in order to mitigate increasing traffic demand. Therefore, [...] Read more.
Urban traffic congestion has a significant detrimental impact on the environment, public health and the economy, with at a high cost to society worldwide. Moreover, it is not possible to continually modify urban road infrastructure in order to mitigate increasing traffic demand. Therefore, it is important to develop traffic control models that can handle high-volume traffic data and synchronize traffic lights in an urban network in real time, without interfering with other initiatives. Within this context, this study proposes a model, based on deep reinforcement learning, for synchronizing the traffic signals of an urban traffic network composed of two intersections. The calibration of this model, including training of its neural network, was performed using real traffic data collected at the approach to each intersection. The results achieved through simulations were very promising, yielding significant improvements in indicators measured in relation to the pre-existing conditions in the network. The model was able to deal with a broad spectrum of traffic flows and, in peak demand periods, reduced delays and queue lengths by more than 28% and 42%, respectively. Full article
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