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Structural Dynamics in Civil Engineering
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Structural Dynamics in Civil Engineering

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

Deadline for manuscript submissions: 10 December 2025 | Viewed by 2904

Special Issue Editors

Dr. Maria Francesca Sabba

E-Mail Website
Guest Editor
Department of Architecture, Construction and Design, Polytechnic University of Bari, Via Orabona, 4, 70126 Bari, Italy
Interests: masonry; monitoring; seismic; modal analysis; dynamic analysis
Prof. Dr. Dora Foti

E-Mail Website
Guest Editor
Department of Architecture, Construction and Design, Polytechnic of Bari, Via Orabona, 4, 70125 Bari, Italy
Interests: dynamic identification; seismic prevention; structural mitigation; bridge; structural vulnerability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

in the past three decades, the topic of Structural dynamics in civil engineering has witnessed significant evolution, highlighting the ongoing significance of analyzing the dynamic behavior of structures and/or their components against seismic activity, winds, vibrations, and other external forces. Today, a convergence of established and innovative techniques, including Artificial Intelligence (AI), has emerged as a transformative approach to comprehend and optimize structural dynamics. These methods blend traditional methodologies (like finite element analysis, modal analysis, and dynamic response spectrum analysis, as well as the correlation of numerical analysis with experimental tests) with modern techniques (such as Machine Learning (ML), neural networks and statistical analysis). This allows to tackle complex challenges with unprecedented precision and efficiency, facilitating the development of more accurate modeling and simulation in capturing nonlinear and time-varying structural behaviors. In particular, some new techniques permit the evaluation of different scenarios and project performances in different conditions. In this Special Issue papers addressing the monitoring, identification, design, and retrofit of new or existing structures, as well as the failure detection and reinforcement strategies, are encouraged. Through the application of consolidated and new techniques, the aim is to investigate the complex interactions between structures and their dynamic environments to optimize performance and mitigate the potential risks effectively.

Dr. Maria Francesca Sabba
Prof. Dr. Dora Foti
Guest Editors

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Keywords

  • modal analysis
  • spectrum analysis
  • dynamic response
  • structural health monitoring (SHM)
  • finite element analysis (FEA)
  • limit analysis
  • nonlinear behavior
  • failure mechanisms
  • machine learning (ML)

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Published Papers (4 papers)

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Research

19 pages, 5563 KiB  
Article
Parametric Analysis of Static–Dynamic Characteristics of Adjacent Tunnels in Super-Large Twin Tunnels by DEM
by Lin Wu, Zhuoyuan Cao, Xiaoya Bian, Jiayan Wang and Hong Guo
Appl. Sci. 2025, 15(13), 7124; https://doi.org/10.3390/app15137124 - 25 Jun 2025
Abstract
The dynamic characteristics of super-large-diameter twin tunnels under train vibration loads have become a critical issue affecting not only the engineering safety of their own tunnels but also adjacent tunnels. A numerical model of super-large-diameter (D = 15.2 m) twin tunnels was [...] Read more.
The dynamic characteristics of super-large-diameter twin tunnels under train vibration loads have become a critical issue affecting not only the engineering safety of their own tunnels but also adjacent tunnels. A numerical model of super-large-diameter (D = 15.2 m) twin tunnels was established by the discrete element method (DEM) to analyze the static and dynamic responses of adjacent tunnel structures and surroundings under train-induced vibrations. Three parameters were considered: internal walls, absolute and relative spacing, and water pressure. The results indicate that internal walls in super-large twin tunnels can significantly reduce the static and dynamic responses in both the structures and surroundings of the adjacent tunnel. The vehicular lane board (wall2) plays a determinative role, followed by the smoke exhaust board (wall1), while the left and right partition walls (wall3 and wall4) exhibit the least effectiveness. The static–dynamic responses of the liners and surroundings of adjacent tunnels in super-large twin tunnels are significantly greater than those in smaller twin tunnels when the absolute spacing is identical. Moreover, the significant differences in displacement and velocity between the liners and surroundings can lead to cracks, leakage, or even instability. Appropriate water pressure (149 kPa) can effectively mitigate dynamic responses in adjacent tunnel structures and surroundings. The dynamic characteristics of super-large-diameter twin tunnels differ markedly from those of small-diameter twin tunnels, with internal walls, twin tunnel spacing, and water pressure all influencing their static and dynamic behaviors. This study provides theoretical guidance for the design and operation of super-large-diameter twin tunnels. Full article
(This article belongs to the Special Issue Structural Dynamics in Civil Engineering)
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25 pages, 13049 KiB  
Article
Physics-Informed Neural Networks-Based Wide-Range Parameter Displacement Inference for Euler–Bernoulli Beams on Foundations Under a Moving Load Using Sparse Local Measurements
by Bin Zhen, Chenyun Xu and Lijun Ouyang
Appl. Sci. 2025, 15(11), 6213; https://doi.org/10.3390/app15116213 - 31 May 2025
Viewed by 287
Abstract
This study develops a novel physics-informed neural network (PINN) framework for predicting steady-state dynamic responses of infinite Euler–Bernoulli (E–B) beams on foundations under moving loads. By combining localized PINN modeling with transfer learning techniques, our approach achieves high-fidelity predictions across broad parameter ranges [...] Read more.
This study develops a novel physics-informed neural network (PINN) framework for predicting steady-state dynamic responses of infinite Euler–Bernoulli (E–B) beams on foundations under moving loads. By combining localized PINN modeling with transfer learning techniques, our approach achieves high-fidelity predictions across broad parameter ranges while significantly reducing data requirements. Numerical results show that the method maintains accuracy with less than half the training data of conventional PINN models (15 target domains) and remains effective with just four domains for approximate solutions. Key findings demonstrate that optimal spatial distribution—rather than quantity—of target domains ensures robustness against noise and parameter variations. The framework advances data-efficient surrogate modeling, enabling reliable predictions in data-scarce scenarios with applications to complex engineering systems where experimental data are limited. Full article
(This article belongs to the Special Issue Structural Dynamics in Civil Engineering)
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27 pages, 4577 KiB  
Article
Machine Learning-Based Seismic Response Prediction for Nuclear Power Plant Structures Considering Aging Deterioration
by Hyunsu Kim, Soyeon Lee, Junsu Jang and Sihyeon An
Appl. Sci. 2025, 15(11), 6211; https://doi.org/10.3390/app15116211 - 31 May 2025
Viewed by 283
Abstract
Given that aging deterioration significantly influences the structural behavior of reinforced concrete (RC) nuclear power plant (NPP) structures, it is crucial to incorporate changes in the material properties of NPPs for accurate prediction of seismic responses. In this study, machine learning (ML) models [...] Read more.
Given that aging deterioration significantly influences the structural behavior of reinforced concrete (RC) nuclear power plant (NPP) structures, it is crucial to incorporate changes in the material properties of NPPs for accurate prediction of seismic responses. In this study, machine learning (ML) models for predicting the seismic response of RC NPP structures were developed by considering aging deterioration. The OPR1000 was selected as a representative structure, and its finite element model was generated. A total of 500 artificial ground motions were created for time history analyses, and the analytical results were utilized to establish a database for training and testing ML models. Six ML algorithms, commonly employed in the structural engineering domain, were used to construct the seismic response prediction model. Thirteen intensity measures of artificial earthquakes and four material properties were employed as input parameters for the training database. The floor response spectrum of the example structure was chosen as the output for the database. Four evaluation metrics were implemented as quantitative measures to assess the prediction performance of the ML models. This study used multiple input variables to represent the characteristics of the seismic loads and changes in material properties, thereby increasing the minimum required database size for ML model development. This increase may extend the time and effort required to construct the database. Consequently, this study also explored the possibility of reducing the minimum required database size and the prediction performance through input dimension reduction of the ML model. Numerical results demonstrated that the developed ML model could effectively predict the seismic responses of RC NPP structures, taking into account aging deterioration. Full article
(This article belongs to the Special Issue Structural Dynamics in Civil Engineering)
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17 pages, 4236 KiB  
Article
Assessing the Influence of RMS and VDV on Analysis of Human Perception of Vibrations in Buildings Caused by Selected Sources of Traffic
by Alicja Kowalska-Koczwara, Fabio Rizzo, Maria Francesca Sabbà and Chiara Bedon
Appl. Sci. 2024, 14(9), 3688; https://doi.org/10.3390/app14093688 - 26 Apr 2024
Cited by 1 | Viewed by 1379
Abstract
This research paper delves into the nuanced effects of traffic-induced vibrations on human comfort and perception within residential and commercial buildings. By prioritizing the influence of vehicle types—including metro trains, trams, and cars—over their speed or mass, the study presents a novel perspective [...] Read more.
This research paper delves into the nuanced effects of traffic-induced vibrations on human comfort and perception within residential and commercial buildings. By prioritizing the influence of vehicle types—including metro trains, trams, and cars—over their speed or mass, the study presents a novel perspective on addressing vibrational comfort. Conducted in the urban context of Warsaw’s metro line construction, this investigation employs a rigorous methodology, utilizing both the Root Mean Square (RMS) and Vibration Dose Value (VDV) analytical methods to quantify vibrational impacts. The findings illuminate the distinct contributions of various transportation modes to the perceived vibrations, offering significant insights into the complex relationship between the Human Perception of Vibration Index (HPVI) and VDV values. It underscores the necessity of integrating a multifaceted consideration of vehicle type, operational dynamics, and urban infrastructure in the strategic planning and design of buildings. Such a holistic approach is essential for mitigating the adverse effects of transportation-induced vibrations, thereby enhancing the quality of life and well-being of urban inhabitants. Full article
(This article belongs to the Special Issue Structural Dynamics in Civil Engineering)
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