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Advances in Structural Health Monitoring and Seismic Assessment of Civil Engineering Structures

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

Deadline for manuscript submissions: closed (30 September 2025) | Viewed by 1356

Special Issue Editors

Dr. Tiago Ribeiro

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Guest Editor
Centre of Materials and Building Technologies (C-MADE), Department of Civil Engineering and Architecture, University of Beira Interior, 6201-001 Covilha, Portugal
Interests: topology optimisation; additive manufacturing; machine learning; artificial neural networks; artificial intelligence; structural engineering; civil engineering; bridge engineering; building information modelling; bayesian inference; probabilistic analyses; data-driven engineering methods; structural health monitoring; seismic design; steel structures; energy
Special Issues, Collections and Topics in MDPI journals
Dr. Shieh-Kung Huang

E-Mail Website
Guest Editor
Department of Civil Engineering, National Chung Hsing University, Taichung 402, Taiwan
Interests: smart structure; structural health monitoring; structural control; earthquake early warning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marcin Górski

E-Mail Website
Guest Editor
Department of Structural Engineering, Faculty of Civil Engineering, Silesian University of Technology, Akademicka 2A, 44-100 Gliwice, Poland
Interests: smart structures; structural engineering; structural health monitoring; smart materials; carbon; fibres
Special Issues, Collections and Topics in MDPI journals
Dr. Luís Filipe Almeida Bernardo

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Guest Editor
GeoBioTec, Department of Civil Engineering and Architecture, University of Beira Interior, Calçada Fonte do Lameiro, 6201-001 Covilhã, Portugal
Interests: composites; nanocomposites; structural analysis and design; numerical modelling; concrete structures; structural materials; polymer-matrix composites; mechanical properties; building systems; topology optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Structural health monitoring (SHM) has evolved from research and practice and recently expanded into one of the most vibrant areas of civil, structural, mechanical, naval, and aerospace engineering. It embraces research, development, and industry in a rare blend and is one of the fittest fields of knowledge for integrating technological developments, either physical or digital. This explains the recent surge in research articles, private and public-funded projects, and even entire journals devoted to the theme. Moreover, it certainly justifies this Special Issue on the “Advances in Structural Health Monitoring and Seismic Assessment of Civil Engineering Structures”. It will be open, but not limited, to the aforementioned fields of research and is intended to capture significant research and applications involving all novel aspects pertaining to SHM, the technologies already or potentially involved, and places a special emphasis on the applications associated with seismic assessment. We welcome new research, including SHM systems, theoretical works, new concepts, and validation through computational or laboratory investigations, as well as the development of products, case studies of applications, and sectorial systematic reviews.

Dr. Tiago Ribeiro
Dr. Shieh-Kung Huang
Dr. Marcin Górski
Dr. Luís Filipe Almeida Bernardo
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

  • structural health monitoring
  • predictive maintenance
  • inspection planning
  • reliability
  • sensing networks and optimization
  • signal processing
  • system identification
  • damage detection, location, and quantification
  • structural performance evaluation
  • numerical modeling and model updating
  • model analysis
  • artificial intelligence and data-driven approach
  • environmental effect

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

25 pages, 7875 KB  
Article
Intelligent Optimal Seismic Design of Buildings Based on the Inversion of Artificial Neural Networks
by Augusto Montisci, Francesca Pibi, Maria Cristina Porcu and Juan Carlos Vielma
Appl. Sci. 2025, 15(19), 10713; https://doi.org/10.3390/app151910713 - 4 Oct 2025
Viewed by 231
Abstract
The growing need for safe, cheap and sustainable earthquake-resistant buildings means that efficient methods for optimal seismic design must be found. The complexity and nonlinearity of the problem can be addressed using advanced automated techniques. This paper presents an intelligent three-step procedure for [...] Read more.
The growing need for safe, cheap and sustainable earthquake-resistant buildings means that efficient methods for optimal seismic design must be found. The complexity and nonlinearity of the problem can be addressed using advanced automated techniques. This paper presents an intelligent three-step procedure for optimally designing earthquake-resistant buildings based on the training (1st step) and successive inversion (2nd step) of Multi-Layer Perceptron Neural Networks. This involves solving the inverse problem of determining the optimal design parameters that meet pre-assigned, code-based performance targets, by means of a gradient-based optimization algorithm (3rd step). The effectiveness of the procedure was tested using an archetypal multistory, moment-resisting, concentrically braced steel frame with active tension diagonal bracing. The input dataset was obtained by varying four design parameters. The output dataset resulted from performance variables obtained through non-linear dynamic analyses carried out under three earthquakes consistent with the Chilean code spectrum, for all cases considered. Three spectrum-consistent records are sufficient for code-based seismic design, while each seismic excitation provides a wealth of information about the behavior of the structure, highlighting potential issues. For optimization purposes, only information relevant to critical sections was used as a performance indicator. Thus, the dataset for training consisted of pairs of design parameter sets and their corresponding performance indicator sets. A dedicated MLP was trained for each of the outputs over the entire dataset, which greatly reduced the total complexity of the problem without compromising the effectiveness of the solution. Due to the comparatively low number of cases considered, the leave-one-out method was adopted, which made the validation process more rigorous than usual since each case acted once as a validation set. The trained network was then inverted to find the input design search domain, where a cost-effective gradient-based algorithm determined the optimal design parameters. The feasibility of the solution was tested through numerical analyses, which proved the effectiveness of the proposed artificial intelligence-aided optimal seismic design procedure. Although the proposed methodology was tested on an archetypal building, the significance of the results highlights the effectiveness of the three-step procedure in solving complex optimization problems. This paves the way for its use in the design optimization of different kinds of earthquake-resistant buildings. Full article
(This article belongs to the Special Issue Advances in Structural Health Monitoring and Seismic Assessment of Civil Engineering Structures)
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22 pages, 4625 KB  
Article
Automated Modal Analysis Using Stochastic Subspace Identification and Field Monitoring Data
by Shieh-Kung Huang, Zong-Zhi Lai, Hoong-Pin Lee and Yen-Yu Yang
Appl. Sci. 2025, 15(14), 7794; https://doi.org/10.3390/app15147794 - 11 Jul 2025
Viewed by 726
Abstract
The accurate identification of modal parameters is essential for structural health monitoring (SHM), as it provides critical insights into the presence of damage or degradation within the structure. A promising technique, stochastic subspace identification (SSI) has numerous advantages in operational modal analysis (OMA), [...] Read more.
The accurate identification of modal parameters is essential for structural health monitoring (SHM), as it provides critical insights into the presence of damage or degradation within the structure. A promising technique, stochastic subspace identification (SSI) has numerous advantages in operational modal analysis (OMA), particularly in implementing automated OMA. Hence, an improved procedure is proposed in this study, addressing the size of the SSI matrix, the estimation of system order, and the removal of spurious modes for automated modal analysis. A general instruction for user-defined parameters is first reviewed and summarized. Subsequently, a proposed procedure is then introduced and framed into three steps. Key advances include the preliminary identification of fundamental frequency, which helps the overall automated work, adequately assigning the size of the SSI matrix, which can improve decomposition, and a decay function, which provides a good estimation of system order. To demonstrate and verify the procedure, a numerical simulation of a ten-story shear-type building structure and two field datasets, collected from reinforced concrete (RC) frames in Taiwan, are utilized. Consequently, the results suggest that the proposed three-step procedure based on SSI can facilitate automated OMA for continuous and long-term SHM, in terms of autonomously adjusting user-defined parameters. Full article
(This article belongs to the Special Issue Advances in Structural Health Monitoring and Seismic Assessment of Civil Engineering Structures)
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