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Recent Advances in Sensing and Data Centric Methods for Structural Health Monitoring and Resilience: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 2883

Special Issue Editors

Prof. Dr. Mohammad N Noori

E-Mail Website
Guest Editor
1. Department of Mechanical Engineering, California Polytechnic State University, San Luis Obispo, CA 93405, USA
2. School of Civil Engineering, University of Leeds, Leeds LS2 9JT, UK
Interests: AI-based methods for structural health monitoring and dynamic response; random vibrations; hysteretic systems; seismic isolation; reliability and resilience
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Domaneschi

E-Mail Website
Guest Editor
Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Turin, TO, Italy
Interests: disaster resilience; earthquake engineering; numerical simulations; special structures; structural control; structural monitoring; structural analysis, control and monitoring; structural and community resilience; structural degradation and damage detection; seismic risk; emergency and evacuation
Special Issues, Collections and Topics in MDPI journals
Dr. Naiwei Lu

E-Mail Website
Guest Editor
School of Civil Engineering, Changsha University of Science and Technology, Changsha 410114, China
Interests: reliability and safety in bridge engineering; bridge health monitoring; structural damage identification; machine learning methods in civil engineering; probabilistic digital twins
Special Issues, Collections and Topics in MDPI journals
Dr. Tianyu Wang

E-Mail Website
Guest Editor
School of Urban Construction and Safety Engineering, Shanghai Institute of Technology, 150 Honggu Road, Changning District, Shanghai 201418, China
Interests: building information modeling (bim); design automation; structural nonlinear analysis; structural health monitoring

Special Issue Information

Dear Colleagues,

Over the last decade, due to major advancements in sensing technology, diagnostics, data analytics (including various AI-based methodologies), optimization, and system identification, significant developments have taken place in structural health monitoring, intelligent systems, and enhancing the resilience of engineering systems. This Special Issue aims to underscore the importance of the latest developments in those areas and their collective impact on further progress in the fields of structural health monitoring and the resilience of infrastructure, mechanical and aerospace systems, as well as other engineering systems. Potential research topics include, but are not limited to, the following:

  • New and novel innovations in structural health monitoring;
  • IoT and smart infrastructure;
  • The SHM of historic and ageing structures;
  • AI-based methodologies, such as deep learning neural networks, big data, and digital twins;
  • System identification;
  • Surrogate models;
  • Optimization techniques;
  • Probabilistic methods, such as uncertainty quantification and variability assessment, especially combined with AI methods;
  • Various machine learning tools;
  • The dynamic response prediction of highly nonlinear systems;
  • Feature extraction schemes;
  • The resilience of civil infrastructure in a life cycle;
  • The resiliency and recoverability of structures and isolation–structure systems;
  • The impact of SHM on urban infrastructure resilience;
  • The utilization of data analytics schemes in structural control and seismic isolation systems;
  • Data-driven methods used for structural damage identification;
  • The reliability and safety of engineering structures;
  • Distributed sensors and big data in SHM application;
  • Damage identification (detection, localization, quantification, and remaining life) to improve structural resilience;
  • Image processing methods used for SHM;
  • Output-only methods used for SHM.

Prof. Dr. Mohammad N Noori
Dr. Marco Domaneschi
Dr. Naiwei Lu
Dr. Tianyu Wang
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. Sensors 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

  • sensing
  • data centric methods
  • structural health monitoring

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Related Special Issue

Published Papers (2 papers)

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Research

17 pages, 6836 KiB  
Article
A Time–Frequency-Based Data-Driven Approach for Structural Damage Identification and Its Application to a Cable-Stayed Bridge Specimen
by Naiwei Lu, Yiru Liu, Jian Cui, Xiangyuan Xiao, Yuan Luo and Mohammad Noori
Sensors 2024, 24(24), 8007; https://doi.org/10.3390/s24248007 - 15 Dec 2024
Cited by 1 | Viewed by 1123
Abstract
Structural damage identification based on structural health monitoring (SHM) data and machine learning (ML) is currently a rapidly developing research area in structural engineering. Traditional machine learning techniques rely heavily on feature extraction, where weak feature extraction can lead to suboptimal features and [...] Read more.
Structural damage identification based on structural health monitoring (SHM) data and machine learning (ML) is currently a rapidly developing research area in structural engineering. Traditional machine learning techniques rely heavily on feature extraction, where weak feature extraction can lead to suboptimal features and poor classification performance. In contrast, ML-based methods, particularly deep learning approaches like convolutional neural networks (CNNs), automatically extract relevant features from raw data, improving the accuracy and adaptability of the damage identification process. This study developed a time–frequency-based data-driven approach aiming to improve the effectiveness of traditional data-driven structural damage identification approaches for large complex structures. Firstly, the structural acceleration signals in the time domain were converted into two-dimensional images via the Gram angle difference field (GADF). Subsequently, the characteristic feature in the image data was studied by convolutional neural networks (CNNs) to predict the structural damage conditions. An experimental study on a scale model of a cable-stayed bridge was conducted to identify the damage of stay cables under the moving vehicle load on the main girders. The CNN was employed to extract the characteristic features from the time-varying monitoring data of vehicle–bridge interactions. The CNN parameters were optimized to conduct the structural damage classification task. The performance of the proposed method was evaluated by comparing it with various traditional pre-trained networks. The effect of environmental noise on the prediction accuracy was also investigated. The numerical results show that the ResNet model has the best performance in terms of damage identification accuracy and convergence speed, achieving higher accuracy and faster convergence compared to the other four traditional networks. The method can accurately identify damage on bridges using insufficient sensors on the bridge deck, which has valuable potential for application to real-world bridges with monitoring data. As the Signal-to-Noise Ratio (SNR) decreases from 20 dB to 2.5 dB, the prediction accuracy of ResNet decreases from 86.63% to 62.5%, which demonstrates the robustness and reliability in identifying structural damage. Full article
(This article belongs to the Special Issue Recent Advances in Sensing and Data Centric Methods for Structural Health Monitoring and Resilience: 2nd Edition)
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23 pages, 6181 KiB  
Article
A Novel Method of Bridge Deflection Prediction Using Probabilistic Deep Learning and Measured Data
by Xinhui Xiao, Zepeng Wang, Haiping Zhang, Yuan Luo, Fanghuai Chen, Yang Deng, Naiwei Lu and Ying Chen
Sensors 2024, 24(21), 6863; https://doi.org/10.3390/s24216863 - 25 Oct 2024
Viewed by 1095
Abstract
The deflection control of the main girder in suspension bridges, as flexible structures, is critically important during their operation. To predict the vertical deflection of existing suspension bridge girders under the combined effects of stochastic traffic loads and environmental temperature, this paper proposes [...] Read more.
The deflection control of the main girder in suspension bridges, as flexible structures, is critically important during their operation. To predict the vertical deflection of existing suspension bridge girders under the combined effects of stochastic traffic loads and environmental temperature, this paper proposes an integrated deflection interval prediction method based on a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), a probability density estimation layer, and bridge monitoring data. A time-series training dataset consisting of environmental temperature, vehicle load, and deflection monitoring data was built based on bridge health monitoring data. The CNN-LSTM combined layer is used to capture both local features and long-term dependencies in the time series. A Gaussian distribution (GD) is adopted as the probability density function, and its parameters are estimated using the maximum likelihood method, which outputs the optimal deflection prediction and probability intervals. Furthermore, this paper proposes a method for identifying abnormal deflections of the main girder in existing suspension bridges and establishes warning thresholds. This study indicates that, for short time scales, the CNN-LSTM-GD model achieves a 47.22% improvement in Root Mean Squared Error (RMSE) and a 12.37% increase in the coefficient of determination (R2) compared to the LSTM model. When compared to the CNN-LSTM model, it shows an improvement of 28.30% in RMSE and 6.55% in R2. For long time scales, the CNN-LSTM-GD model shows a 54.40% improvement in RMSE and a 10.22% increase in R2 compared to the LSTM model. Compared to the CNN-LSTM model, it improves RMSE by 38.43% and R2 by 5.31%. This model is instrumental in more accurately identifying abnormal deflections and determining deflection thresholds, making it applicable to bridge deflection early-warning systems. Full article
(This article belongs to the Special Issue Recent Advances in Sensing and Data Centric Methods for Structural Health Monitoring and Resilience: 2nd Edition)
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