Performance-Based Seismic Design, Structural Health Monitoring, and Deformation Prediction for Building Structures

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Structures".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 347

Special Issue Editors


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Guest Editor
College of Civil Engineering, Tongji University, Shanghai 200092, China
Interests: structural dynamics; seismic design; seismic resilience; concrete structures

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Guest Editor
College of Civil Engineering, Hellenic Open University, 26335 Patras, Greece
Interests: structural dynamics; seismic design; seismic resilience; steel structures

E-Mail Website
Guest Editor
College of Civil Engineering, Tongji University, Shanghai 200092, China
Interests: structural dynamics; seismic design; steel structures

Special Issue Information

Dear Colleagues,

Modern building structures must meet strict requirements for seismic safety, durability, and sustainability. As building codes evolve and materials age, engineers face the challenge of designing structures that can withstand increasingly complex loading conditions. Performance-based seismic design helps ensure that buildings achieve specific performance goals, even during strong seismic events. At the same time, structural health monitoring (SHM) systems and reliable methods for predicting deformation play a critical role not only in early damage detection and proactive maintenance, but also in guiding performance-based design informing strengthening strategies and ensuring long-term structural reliability.

This Special Issue, ‘Performance-Based Seismic Design, Structural Health Monitoring, and Deformation Prediction for Building Structures’, focuses on advancing the seismic resilience and reliability of buildings. We invite contributions that explore experimental studies, analytical approaches, computational techniques, and case studies demonstrating practical applications. By integrating innovative design strategies, advanced monitoring tools, and accurate deformation prediction methods, this Special Issue aims to contribute to safer and more sustainable building structures.

Topics of interest include, but are not limited to, the following:

  • Performance-based seismic design for buildings of different materials;
  • Advanced structural health monitoring (SHM) methods and tools for buildings;
  • Predicting deformation in building structures under seismic loading;
  • AI and machine learning for seismic analysis, SHM, and deformation prediction;
  • Retrofitting and strengthening methods for improved seismic performance.

Dr. Edmond Muho
Dr. George S. Kamaris
Dr. Nicos A. Kalapodis
Guest Editors

Manuscript Submission Information

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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. Buildings 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 2600 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

  • performance-based seismic design
  • structural health monitoring (SHM)
  • deformation prediction
  • AI and machine learning in structural engineering
  • sustainability and life-cycle assessment

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Published Papers (1 paper)

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Research

21 pages, 4734 KiB  
Article
A Bayesian Method for Simultaneous Identification of Structural Mass and Stiffness Using Static–Dynamic Measurements
by Zhiyong Li, Zhifeng Wu and Hui Chen
Buildings 2025, 15(8), 1259; https://doi.org/10.3390/buildings15081259 - 11 Apr 2025
Viewed by 191
Abstract
This paper presents a Bayesian-based finite element model updating method that integrates static displacement measurements and dynamic modal data to simultaneously identify structural mass and stiffness parameters. By leveraging Bayesian inference, a posterior probability density function (PDF) is constructed by integrating static displacement [...] Read more.
This paper presents a Bayesian-based finite element model updating method that integrates static displacement measurements and dynamic modal data to simultaneously identify structural mass and stiffness parameters. By leveraging Bayesian inference, a posterior probability density function (PDF) is constructed by integrating static displacement and modal parameters, thereby effectively decoupling the identification of structural mass and stiffness. The Delayed Rejection Adaptive Metropolis (DRAM) Markov Chain Monte Carlo (MCMC) sampling algorithm is utilized to derive the posterior distributions of the updated parameters. To mitigate the computational burden associated with repetitive finite element (FE) analyses during large-scale MCMC sampling, a Kriging surrogate model is employed to efficiently approximate the time-consuming FE simulations. Numerical examples involving a cantilever beam and an actual concrete three-span single-box girder bridge illustrate that the proposed method accurately identifies simultaneous variations in mass and stiffness at multiple structural locations, effectively addressing parameter coupling and misidentification issues encountered when using either static or dynamic data alone. Moreover, the Kriging surrogate significantly improves computational efficiency. Experimental validation on an aluminum alloy cantilever beam further corroborates the effectiveness and practical applicability of the proposed method. Full article
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