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A Special Issue of Buildings (ISSN 2075-5309) belonging to the section "Building Structures".

Deadline for manuscript submissions: closed (30 April 2026) | Viewed by 24554

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Editors

Prof. Dr. Zuguang Ying

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Guest Editor

Department of Mechanics, School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China
Interests: stochastic optimal control; random system identification; optimal parameter estimation; bayesian inference; stochastic system dynamics; machine learning

Dr. Youwu Wang

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Guest Editor

Dr. Sumei Wang

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Guest Editor

Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center, Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Interests: vehicle-bridge coupling interaction; structural health monitoring and control of maglev system; suspension control

Prof. Dr. Yi-Qing Ni

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Guest Editor

Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center, Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Interests: structural health monitoring; structural dynamics and control; smart materials and structures; sensors and actuators; bayesian inference and machine learning; high-speed rail and maglev safety
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Special Issue Information

Dear Colleagues,

Structural health monitoring and vibration control are two important research subjects that are expanding in the engineering and theoretical fields. With the development of estimation methods, diagnosis technology, control strategies, optimization algorithms, multi-physics sensing, and actuating technology, etc., vibration-based structural health monitoring and structural vibration control, especially for uncertain systems under random excitations due to the application of artificial intelligence technology, have been experiencing tremendous progress. Buildings are an important class of engineered structures, which generally have uncertain parameters and are subjected to random excitations due to their structural complexity and environmental uncertainty. Accordingly, research focusing on the technology and theory of health monitoring and vibration control related to built structures will lead to their advancement. This Special Issue focuses on structural health monitoring and vibration control for various extensive engineering and theoretical problems using various estimation, control, optimization, and intelligence technologies, including estimation and identification methods, control strategies and methods, sensing and actuating technology, application analysis and experiments, data processing, machine learning and inference, neural network representation, etc. This issue will bring together and share recent relevant research, aiming to enable extensive development in this area. The potential topics for this issue include, but are not limited to, the following:

Structural health monitoring;
Estimation method and applications;
Vibration control;
Control method and applications;
Sensor and actuator technology and applications;
Data processing technology and applications;
Machine learning and inference and applications;
Neural network algorithm and applications;
Smart structural dynamics.

Prof. Dr. Zuguang Ying
Dr. Youwu Wang
Dr. Sumei Wang
Prof. Dr. Yi-Qing Ni
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

structural health monitoring
parameter estimation
vibration control
data processing
machine learning
neural network
smart structural dynamics
stochastic vibration

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Published Papers (8 papers)

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Research

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37 pages, 4042 KB

Open AccessArticle

VIWNO: Vehicle–Bridge Interaction Wavelet Neural Operator for Controlled Bridge Simulation and Laboratory Damage Identification

by Zixu Hu, Haitao Li, Wei He and Yongweng Wu

Buildings 2026, 16(16), 3235; https://doi.org/10.3390/buildings16163235 - 14 Aug 2026

Viewed by 344

Abstract

Controlled bridge simulation and laboratory damage identification require models that can simulate structural responses and infer localized stiffness loss from limited measurements. Existing Fourier Neural Operator (FNO)-based vehicle–bridge interaction (VBI) models provide efficient surrogates for these mappings, but the global Fourier representation can smooth localized damage transitions and introduce boundary-related errors for finite-span bridge responses. This study adapts the Wavelet Neural Operator (WNO) to the VBI setting and develops the Vehicle–Bridge Interaction Wavelet Neural Operator (VIWNO), an application-oriented framework for wavelet-domain operator learning between structural response fields and damage fields. VIWNO is pre-trained on a numerical VBI finite-element dataset (VBI-FE) and fine-tuned using only healthy-state measurements from a scaled VBI experimental dataset (VBI-EXP), before being evaluated on unseen laboratory damage scenarios. Under the controlled VBI-FE setting, where bridge, vehicle, speed, and measured road-profile parameters are fixed and the main variation is the damage field, VIWNO reduces forward response errors by 20–30% and inverse damage-estimation errors by 26–32% relative to the FNO-based Vehicle–Bridge Interaction Neural Operator (VINO) baseline. Additional morphology and operating-condition stress tests show that the error increases under sharper damage fields and perturbed VBI conditions, but VIWNO remains more accurate than VINO and the added convolutional or frequency-domain baselines in the tested cases. On VBI-EXP, projection-only healthy-state fine-tuning reduces intact false-damage levels and yields sharper damage estimates than VINO under both displacement and acceleration inputs. Stability checks over five initializations and repeated vehicle passages show limited variation in the reported inverse metrics. These results support the feasibility of wavelet-domain neural operators for calibrated VBI simulation and scaled laboratory damage identification, while field-scale bridge health monitoring still requires validation under broader traffic, environmental, support, and damage-morphology variability. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Vibration Control)

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33 pages, 11804 KB

Open AccessArticle

Parameter-Free Jaya Algorithm for Steel Frame Optimization Under Natural Frequency Constraints

by Gülay Yalçin Bayar and Sadik Ozgur Degertekin

Buildings 2026, 16(15), 2940; https://doi.org/10.3390/buildings16152940 - 23 Jul 2026

Viewed by 253

Abstract

The optimal design of steel frame structures subjected to natural frequency constraints is a challenging problem due to its highly nonlinear and computationally demanding nature. In this study, Parameter-Free Jaya Algorithm (PFJA) is proposed for the minimum-weight design of planar and spatial steel frame systems under prescribed natural frequency constraints. The standard Jaya Algorithm (JA), which is based on moving toward the best solution and away from the worst solution, is enhanced by eliminating algorithm-specific control parameters and enabling automatic self-adaptation during the optimization process. The performance of the proposed PFJA is evaluated using three planar and two spatial steel frame benchmark problems previously studied in the literature. All structural analyses and optimization procedures are implemented in MATLAB R2020b, and the optimal designs are independently verified using SAP2000 v20, yielding identical natural frequency values. The results demonstrate that PFJA outperforms the standard JA, modified Jaya algorithm (MJA), and several recently developed metaheuristic algorithms in terms of minimum structural weight, convergence speed, and statistical robustness. These findings confirm the superiority and reliability of the proposed PFJA for frequency-constrained structural optimization problems. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Vibration Control)

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27 pages, 7716 KB

Open AccessArticle

Field Dynamic Testing and Adaptive Dynamic Characteristic Identification of Steel Tower Structures in High-Speed Railway Stations Under Limited Sensor Configurations

by Wei Liu, Boqi Liu, Hailong Feng, Bo Wang, Na Yang and Yuan Gao

Buildings 2025, 15(20), 3754; https://doi.org/10.3390/buildings15203754 - 17 Oct 2025

Viewed by 834

Abstract

In the context of complex operational environments and limited sensor configurations, modal identification of large-scale tower structures often faces challenges related to adaptive model order determination and modal aliasing. This study develops an algorithmic framework for automatic mode identification based on the corrected Akaike information criterion (AICC) and adaptive density-based clustering. First, unlike traditional singular entropy increment (SEI) methods where the determined model order is affected by cumulative thresholds, the AICC-based approach ensures that the adaptively determined model order remains stable. Furthermore, automatic model order selection using the AICC is integrated with adaptive density-based clustering, where the modal assurance criterion extended to a complex mode space (MACXP) is employed to define a modal distance metric. The proposed framework enhances automatic modal clustering and mode-shape discrimination under limited sensor conditions. Finally, a field application was carried out on A-shaped steel towers of integrated bridge–station structures in a high-speed railway station to identify and validate their dynamic characteristics. The results demonstrate that (i) AICC-based model order selection effectively overcomes the threshold dependence of SEI, ensuring improved stability and reliability; (ii) combining AICC-based order determination with density-based clustering enables stable and automated modal identification; and (iii) compared with the conventional MAC, MACXP exhibits superior mode shape discrimination capability under sparse measurement conditions and clearly reveals differences in the modal characteristics of complex structures. This study provides an effective approach for model order determination, mode discrimination, and automated modal identification of large-scale engineering structures under limited sensor deployments. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Vibration Control)

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18 pages, 6212 KB

Open AccessArticle

A Method to Detect Concealed Damage in Concrete Tunnels Using a Radar Feature Vector and Bayesian Analysis of Ground-Penetrating Radar Data

by Junfang Wang, Heng Chen, Jianfu Lin and Xiangxiong Li

Buildings 2024, 14(11), 3662; https://doi.org/10.3390/buildings14113662 - 18 Nov 2024

Cited by 5 | Viewed by 1932

Abstract

Many machine learning (ML)-based detection methods for interpreting ground-penetrating radar (GPR) data of concrete tunnels require extensive labeled damage-state data for model training, limiting their practical use in concealed damage detection of in-service tunnels. This study presents a probabilistic, data-driven method for GPR-based damage detection, which exempts the requirement in the training process of supervised ML models. The approach involves extracting a radar feature vector (RFV), building a Bayesian baseline model with healthy data, and quantifying damage severity with the Bayes factor. The RFV is a complex vector obtained by radargram data fusion. Bayesian regression is applied to build a model for the relationship between real and imaginary parts of the RFV. The Bayes factor is employed for defect identification and severity assessment, by quantifying the difference between the RFV built with new observations and the baseline RFV predicted by the baseline model with new input. The probability of damage is calculated to reflect the influence of uncertainties on the detection result. The effectiveness of the proposed method is validated through simulated data with random noise and physical model tests. This method facilitates GPR-based hidden damage detection of in-service tunnels when lacking labeled damage-state data in the model training process. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Vibration Control)

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20 pages, 8425 KB

Open AccessArticle

An NSCT-Based Multifrequency GPR Data-Fusion Method for Concealed Damage Detection

by Junfang Wang, Xiangxiong Li, Huike Zeng, Jianfu Lin, Shiming Xue, Jing Wang and Yanfeng Zhou

Buildings 2024, 14(9), 2657; https://doi.org/10.3390/buildings14092657 - 27 Aug 2024

Cited by 6 | Viewed by 2541

Abstract

Ground-penetrating radar (GPR) is widely employed as a non-destructive tool for subsurface detection of transport infrastructures. Typically, data collected by high-frequency antennas offer high resolution but limited penetration depth, whereas data from low-frequency antennas provide deeper penetration but lower resolution. To simultaneously achieve high resolution and deep penetration via a composite radargram, a Non-Subsampled Contourlet Transform (NSCT) algorithm-based multifrequency GPR data-fusion method is proposed by integrating NSCT with appropriate fusion rules, respectively, for high-frequency and low-frequency coefficients of decomposed radargrams and by incorporating quantitative assessment metrics. Despite the advantages of NSCT in image processing, its applications to GPR data fusion for concealed damage identification of transport infrastructures are rarely reported. Numerical simulation, tunnel model test, and on-site road test are conducted for performance validation. The comparison between the evaluation metrics before and after fusion demonstrates the effectiveness of the proposed fusion method. Both shallow and deep hollow targets hidden in the simulated concrete structure, real tunnel model, and road are identified through one radargram obtained by fusing different radargrams. The significance of this study is producing a high-quality composite radargram to enable multi-depth concealed damage detection and exempting human interference in the interpretation of multiple radargrams. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Vibration Control)

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23 pages, 4283 KB

Open AccessArticle

Stochastic Optimal Bounded Parametric Control of Periodic Viscoelastomer Sandwich Plate with Supported Mass Based on Dynamical Programming Principle

by Zhi-Gang Ruan, Zu-Guang Ying, Zhao-Zhong Ying, Hua Lei, Wen Wang and Lei Xia

Buildings 2024, 14(8), 2309; https://doi.org/10.3390/buildings14082309 - 25 Jul 2024

Cited by 1 | Viewed by 1573

Abstract

The sandwich plate (SP) with supported mass can model structural systems such as platform or floor with installed vibration-sensitive apparatus under random loading. The stochastic optimal control (in time domain) of periodic (in space) viscoelastomer (VE) SP with supported mass subjected to random excitation is an important research subject, which can fully use VE controllability, but it is a challenging problem on optimal bounded parametric control (OBPC). In this paper, a stochastic OBPC for periodic VESP with supported mass subjected to random base loading is proposed according to the stochastic dynamical programming (SDP) principle. Response-reduction capability using the proposed OBPC is studied to demonstrate further control effectiveness of periodic SP via SDP. Controllable VE core modulus of SP is distributed periodically in space. Differential equations for coupling vibration of periodic SP with supported mass are derived and transformed into multi-dimensional system equations with parameters as nonlinear functions of bounded control. The OBPC problem is established by the system equations and performance index with bound constraint. Then, an SDP equation is derived according to the SDP principle. The OBPC law is obtained from the SDP equation under bound constraint. Optimally controlled responses are calculated and compared with passively controlled responses to evaluate control effectiveness. Numerical results on responses and statistics of SP via the proposed OBPC show further remarkable control effectiveness. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Vibration Control)

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15 pages, 3566 KB

Open AccessArticle

Stochastic Response of Composite Post Insulators under Seismic Excitation

by Haibo Wang, Yongfeng Cheng, Zhicheng Lu, Ronghua Huan, Qiangfeng Lü and Zhenlin Liu

Buildings 2024, 14(6), 1539; https://doi.org/10.3390/buildings14061539 - 25 May 2024

Cited by 2 | Viewed by 1567

Abstract

Composite post insulators are crucial facilities in substations and are prone to significant damage during seismic disasters. However, existing research lacks seismic motion models suitable for power facilities and rarely involves stochastic models. Furthermore, considering the non-stationary characteristics of seismic motion, predicting the response of nonlinear systems under non-stationary excitation becomes exceedingly challenging. In view of this, the stochastic response of composite post insulators under the non-stationary stochastic seismic excitation appropriate for power facilities has been studied. First, a stochastic ground motion model, conforming to the Code for Seismic Design of Electrical Insulators in China, is established, incorporating amplitude and frequency non-stationarity. Next, the nonlinear dynamic system, accounting for multi-section composite post insulators and the nonlinearity of flange connections, is established under stochastic ground motion conditions. Based on this stochastic nonlinear dynamic model, the dynamic behavior of the system was analyzed using the stochastic dynamics method (the wavelet-Galerkin method), and the influence of nonlinear stiffness on the system response was discussed. The stochastic seismic response analysis method proposed in this paper can serve as a valuable reference for the seismic design of pillar-type electrical equipment. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Vibration Control)

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Review

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36 pages, 4790 KB

Open AccessReview

A Review of Levitation Control Methods for Low- and Medium-Speed Maglev Systems

by Qi Zhu, Su-Mei Wang and Yi-Qing Ni

Buildings 2024, 14(3), 837; https://doi.org/10.3390/buildings14030837 - 20 Mar 2024

Cited by 42 | Viewed by 13767

Abstract

Maglev transportation is a highly promising form of transportation for the future, primarily due to its friction-free operation, exceptional comfort, and low risk of derailment. Unlike conventional transportation systems, maglev trains operate with no mechanical contact with the track. Maglev trains achieve levitation and guidance using electromagnetic forces controlled by a magnetic levitation control system. Therefore, the magnetic levitation control system is of utmost importance in maintaining the stable operation performance of a maglev train. However, due to the open-loop instability and strong nonlinearity of the control system, designing an active controller with self-adaptive ability poses a substantial challenge. Moreover, various uncertainties exist, including parameter variations and unknown external disturbances, under different operating conditions. Although several review papers on maglev levitation systems and control methods have been published over the last decade, there has been no comprehensive exploration of their modeling and related control technologies. Meanwhile, many review papers have become outdated and no longer reflect the current state-of-the-art research in the field. Therefore, this article aims to summarize the models and control technologies for maglev levitation systems following the preferred reporting items for systematic reviews and meta-analysis (PRISMA) criteria. The control technologies mainly include linear control methods, nonlinear control methods, and artificial intelligence methods. In addition, the article will discuss maglev control in other scenarios, such as vehicle–guideway vibration control and redundancy and fault-tolerant design. First, the widely used maglev levitation system modeling methods are reviewed, including the modeling assumptions. Second, the principle of the control methods and their control performance in maglev levitation systems are presented. Third, the maglev control methods in other scenarios are discussed. Finally, the key issues pertaining to the future direction of maglev levitation control are discussed. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Vibration Control)

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