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Advancements of 4th Industrial Revolution in Seismic Assessment, Repair and...
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Advancements of 4th Industrial Revolution in Seismic Assessment, Repair and Design of Structures

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

Deadline for manuscript submissions: 10 March 2025 | Viewed by 869

Special Issue Editors

Prof. Dr. Lazaros Iliadis

E-Mail Website
Guest Editor
Lab of Mathematics and Informatics (ISCE), Department of Civil Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Interests: computational intelligence; artificial neural networks; fuzzy logic; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Ioannis E. Kavvadias

E-Mail Website
Guest Editor
Department of Civil Engineering, Sector of Structural Engineering Science, Institute of Structural of Statics and Dynamics, Democritus University of Thrace, 67100 Xanthi, Greece
Interests: structural analysis; dynamics of structure; numerical methods for structural engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Antonis Papaleonidas

E-Mail Website
Guest Editor
Lab of Mathematics and Informatics, Department of Civil Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Interests: machine learning; deep learning; AI; agents; civil engineering

Special Issue Information

Dear Colleagues,

The Analysis and Design of Structures (ADS) is considered as a critical step and a wide research area of Civil Engineering. It is a fact that Artificial Intelligence (AI) lies at the core of the 4th Industrial Revolution. It offers major revolutionary transformations and solutions that can be employed as powerful tools in diverse and key research areas of ADS. Seismic risk assessment and the enhancement of structural resilience are major research challenges that require the aid of intelligent and robust algorithmic approaches. More flexibility and accuracy are offered by fusing intelligence and transforming typically traditional methods and practices. Moreover, the vast amounts of data can be exploited and analyzed using numerous AI algorithms, towards the development of real-life intelligent models offering civil engineers the chance to successfully overcome traditional analysis and design obstacles. This Special Issue is an open call for original research papers on the employment of 4th Industrial Revolution approaches in the domains of seismic assessment, repair and structural design in civil engineering. Authors are invited to submit original research papers on the aforementioned domains. This Special Issue aims to embrace the timely research conducted on the employment of Artificial Intelligence in the critical domains of Civil Engineering. It also aims to guide towards the development of more optimal models.

Prof. Dr. Lazaros Iliadis
Dr. Ioannis E. Kavvadias
Dr. Antonis Papaleonidas
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
  • 4th industrial revolution
  • analysis and design of structures
  • seismic assessment

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Published Papers (1 paper)

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Research

14 pages, 1616 KiB  
Article
Derivation of Analytical Equations for the Fundamental Period of Framed Structures Using Machine Learning and SHAP Values
by Ioannis Karampinis, Konstantinos Morfidis and Lazaros Iliadis
Appl. Sci. 2024, 14(19), 9072; https://doi.org/10.3390/app14199072 - 8 Oct 2024
Viewed by 544
Abstract
The fundamental period is one of the most important parameters for the design of new structures as well as for estimating the capacity of existing ones. Thus, to estimate it, various design codes and researchers have adopted several approximate analytical equations based on [...] Read more.
The fundamental period is one of the most important parameters for the design of new structures as well as for estimating the capacity of existing ones. Thus, to estimate it, various design codes and researchers have adopted several approximate analytical equations based on a number of key structural parameters. To this end, the present study introduces a novel methodology for deriving the analytical equations for the fundamental period of reinforced concrete structures. The methodology is based on machine learning explainability techniques, specifically the so-called SHapley Additive exPlanations values. These values are commonly employed as an explainability tool. However, in the proposed novel approach they are employed as a basis to fit analytical curves, which allows the resulting equations to be constructed sequentially and in an informed manner while controlling the balance between accuracy and complexity. An extended dataset consisting of 4026 data points is employed, on which a Gradient Boosting Machine model is fitted. The model achieves excellent accuracy, with a coefficient of determination R2≈0.99, while the equations derived from the proposed formulation achieve an R2≈0.95 and Mean Absolute Error ≈0.12. This demonstrates the potential applicability of the proposed methodology in a wide array of similar engineering challenges. Full article
(This article belongs to the Special Issue Advancements of 4th Industrial Revolution in Seismic Assessment, Repair and Design of Structures)
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