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Artificial Intelligence and Sustainable Civil Engineering
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Artificial Intelligence and Sustainable Civil Engineering

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (30 March 2023) | Viewed by 3508

Special Issue Editors

Dr. Ali Behnood

E-Mail Website
Guest Editor
Indiana Department of Transportation, Crawfordsville, IN, USA
Interests: automation; artificial intelligence; sustainability; transportation engineering; digital twin; infrastructure materials; transportation geotechnics; smart cities
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Moncef L. Nehdi

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Western University, London, ON N6A 5B9, Canada
Interests: resilient infrastructure systems; artificial intelligence; sustainability; eco-friendly structures; cement and concrete
Special Issues, Collections and Topics in MDPI journals
Dr. Max Ziyadi

E-Mail
Guest Editor
Lucid Motors, Newark, CA, USA
Interests: artificial intelligence; digital twins; machine learning; electrical vehicles; smart cities; big data analytics; autonomous driving; transportation sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI), due to its capabilities in knowledge processing, pattern recognition, prioritization, and optimization, is among the leading techniques to solve complex engineering problems. AI methods provide a wide variety of benefits, including more sustainable solutions with improved accuracy and reliability while saving in cost, energy, time, as well as physical and human resources. AI has the potential to enhance sustainability by detecting damage and distress, predicting extreme weather conditions and natural hazards, enhancing automated systems, monitoring infrastructure conditions, developing predictive models, and helping towards greener transportation and engineering.

This Special Issue welcomes the latest findings, methodologies, and conceptual frameworks in the area of applications of AI to move towards sustainable engineering. Various research articles and reviews that bridge multiple domains of AI and sustainable engineering will be considered, including but not limited to:

  • AI and sustainable infrastructure;
  • AI and cleaner production;
  • Automated and green systems;
  • AI and additive manufacturing;
  • Smart cities;
  • Digital twins and sustainability;
  • AI and green transportation;
  • AI and cleaner engineering;
  • AI and responsible consumption;
  • AI and cleaner materials.

We look forward to receiving your contributions.

You may choose our Joint Special Issue in AI.

Dr. Ali Behnood
Prof. Dr. Moncef L. Nehdi
Dr. Max Ziyadi
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. Sustainability 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

  • automation
  • artificial intelligence
  • sustainability
  • smart cities
  • digital twins

Published Papers (2 papers)

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Research

28 pages, 6483 KiB  
Article
White-Tailed Eagle Algorithm for Global Optimization and Low-Cost and Low-CO2 Emission Design of Retaining Structures
by Behdad Arandian, Amin Iraji, Hossein Alaei, Suraparb Keawsawasvong and Moncef L. Nehdi
Sustainability 2022, 14(17), 10673; https://doi.org/10.3390/su141710673 - 26 Aug 2022
Cited by 5 | Viewed by 1377
Abstract
This study proposes a new metaheuristic optimization algorithm, namely the white-tailed eagle algorithm (WEA), for global optimization and optimum design of retaining structures. Metaheuristic optimization methods are now broadly implemented to address problems in a variety of scientific domains. These algorithms are typically [...] Read more.
This study proposes a new metaheuristic optimization algorithm, namely the white-tailed eagle algorithm (WEA), for global optimization and optimum design of retaining structures. Metaheuristic optimization methods are now broadly implemented to address problems in a variety of scientific domains. These algorithms are typically inspired by the natural behavior of an agent, which can be humans, animals, plants, or any physical agent. However, a specific metaheuristic algorithm (MA) may not be able to find the optimal solution for every situation. As a result, researchers will aim to propose and discover new methods in order to identify the best solutions to a variety of problems. The white-tailed eagle algorithm (WEA) is a simple but effective nature-inspired algorithm inspired by the social life and hunting activity of white-tailed eagles. The WEA’s hunting is divided into two phases. In the first phase (exploration), white-tailed eagles seek prey inside the searching region. The eagle goes inside the designated space according to the position of the best eagle to find the optimum hunting position (exploitation). The proposed approach is tested using 13 unimodal and multimodal benchmark test functions, and the results are compared to those obtained by some well-established optimization methods. In addition, the new algorithm automates the optimum design of retaining structures under seismic load, considering two objectives: economic cost and CO2 emissions. The results of the experiments and comparisons reveal that the WEA is a high-performance algorithm that can effectively explore the decision space and outperform almost all comparative algorithms in the majority of the problems. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sustainable Civil Engineering)
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16 pages, 6654 KiB  
Article
Indirect Analysis of Concrete Slump Using Different Metaheuristic-Empowered Neural Processors
by Hamed Safayenikoo, Fatemeh Nejati and Moncef L. Nehdi
Sustainability 2022, 14(16), 10373; https://doi.org/10.3390/su141610373 - 20 Aug 2022
Cited by 5 | Viewed by 1194
Abstract
Estimating the mechanical parameters of concrete is significant towards achieving an efficient mixture design. This research deals with concrete slump analysis using novel integrated models. To this end, four wise metaheuristic techniques of biogeography-based optimization (BBO), salp swarm algorithm (SSA), moth-flame optimization (MFO), [...] Read more.
Estimating the mechanical parameters of concrete is significant towards achieving an efficient mixture design. This research deals with concrete slump analysis using novel integrated models. To this end, four wise metaheuristic techniques of biogeography-based optimization (BBO), salp swarm algorithm (SSA), moth-flame optimization (MFO), and wind driven optimization (WDO) are employed to optimize a popular member of the neural computing family, namely multilayer perceptron (MLP). Four predictive ensembles are constructed to analyze the relationship between concrete slump and seven concrete ingredients including cement, water, slag, fly ash, fine aggregate, superplasticizer, and coarse aggregate. After discovering the optimal complexities by sensitivity analysis, the results demonstrated that the combination of metaheuristic algorithms and neural methods can properly handle the early prediction of concrete slump. Moreover, referring to the calculated ranking scores (RSs), the BBO-MLP (RS = 21) came up as the most accurate model, followed by the MFO-MLP (RS = 17), SSA-MLP (RS = 12), and WDO-MLP (RS = 10). Lastly, the suggested models can be promising substitutes to traditional approaches in approximating the concrete slump. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sustainable Civil Engineering)
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