Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.1
2.5
Journals
Applied Sciences
Special Issues
Artificial Intelligence for Structural Health Monitoring, Inspection, Maintenance, and Rehabilitation...
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Artificial Intelligence for Structural Health Monitoring, Inspection, Maintenance, and Rehabilitation of Civil Infrastructure

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

Deadline for manuscript submissions: closed (30 April 2026) | Viewed by 5656

Special Issue Editors

Dr. Yang Xu

E-Mail Website
Guest Editor
School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China
Interests: structural health monitoring; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Yasutaka Narazaki

E-Mail Website
Guest Editor
ZJU-UIUC Institute, Zhejiang University, Haining 314400, China
Interests: structural health monitoring; bridge inspection; condition assessment; digital twin; computer vision; deep learning; autonomous robots for inspection; point cloud
Special Issues, Collections and Topics in MDPI journals
Dr. Vedhus Hoskere

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Houston, Houston, TX, USA
Interests: structural health monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, artificial intelligence, especially machine learning, deep learning, computer vision, and large-scale models, has inspired novel progress and revolutional advances in structural health monitoring, inspection, maintenance, and rehabilitation. Advancements of full-field high-resolution sensing, multi-scale contextual modeling and digital twin, cascade inference in cyber space, accurate and robust recognition of multi-type damage and change, reasonable evaluation and credible assessment of structural condition, autonomous decision-making of maintenance strategies, swarm intelligence and human–machine–thing synergetic interaction, as well as various multi-modal large-scale models and high-effectiveness learning algorithms, greatly enhance the domain knowledge discovery, embedding, and intelligent applications for structural health diagnosis and performance improvement in a data–model–knowledge fusion-driven manner. This Special Issue aims to provide a platform to share current scientific original research and engineering technical applications of the related topics.

Dr. Yang Xu
Dr. Yasutaka Narazaki
Dr. Vedhus Hoskere
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-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

  • data, feature, and decision fusion of multi-source sensing data
  • universal time-series analysis for singal generation, reconstruction, and assessment
  • generalized vision-based damage recognition and change measurement
  • intelligent structural 3D modeling, rendering, mapping, and updating
  • learning-based feature extraction and pattern recognition for damage diagnosis
  • data, model, and knowledge fusion-driven intelligent computing and inference
  • multi-modal large-scale model-based navigation, inspection, and interaction agent
  • autonomous decision making for intelligent maintenance and rehabilitation
  • intelligent Internet-of-Thing and system intergration for industry applications

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 2387 KB  
Article
Real-Time Mechanical Modeling for Bridge Construction Based on Digital Twins and Parameter Inversion
by Xiaoqing Yu, Xiaoyun Wan, Jianchun Nie, Guquan Song, Anjun Yu and Jian Yu
Appl. Sci. 2026, 16(6), 2920; https://doi.org/10.3390/app16062920 - 18 Mar 2026
Viewed by 410
Abstract
Real-time mechanical analysis within digital twin (DT) systems requires high-fidelity models that synchronize with the “as-built” state of physical structures. This paper proposes a technical framework for constructing a “mechanical-core” DT by integrating computer vision (CV) sensing with automated finite element model (FEM) [...] Read more.
Real-time mechanical analysis within digital twin (DT) systems requires high-fidelity models that synchronize with the “as-built” state of physical structures. This paper proposes a technical framework for constructing a “mechanical-core” DT by integrating computer vision (CV) sensing with automated finite element model (FEM) updating. Utilizing the Midas API, we developed a platform that automates data acquisition, modeling, and parameter inversion. A momentum-based optimization algorithm is implemented to invert the instantaneous elastic modulus of bridge segments during cantilever construction. The system was validated through a case study of a continuous box-girder bridge. Quantitative results indicate that the initial theoretical model, based on design-phase assumptions, exhibited a mean relative error of approximately 21.9% in vertical displacement. Following the real-time parameter inversion, this error was significantly reduced to less than 0.2% across all monitored construction stages. The rapid convergence (typically within three iterations) and the substantial increase in predictive accuracy demonstrate that the proposed framework effectively bridges the gap between raw sensing data and structural analysis, providing a reliable basis for proactive engineering decision-making. Full article
(This article belongs to the Special Issue Artificial Intelligence for Structural Health Monitoring, Inspection, Maintenance, and Rehabilitation of Civil Infrastructure)
►▼ Show Figures

Figure 1

25 pages, 2689 KB  
Article
Construction of Bridge Maintenance Knowledge Graph Based on Deep Learning
by Yiming Zhang and Hongshuai Gao
Appl. Sci. 2026, 16(4), 1985; https://doi.org/10.3390/app16041985 - 17 Feb 2026
Viewed by 962
Abstract
Bridge maintenance decision-making is challenged by the “data-rich but knowledge-poor” nature of unstructured inspection and maintenance reports. A bridge maintenance knowledge graph (BMKG) construction framework is proposed, developed from a corpus of 275 inspection reports, to enable structured representation of engineering knowledge and [...] Read more.
Bridge maintenance decision-making is challenged by the “data-rich but knowledge-poor” nature of unstructured inspection and maintenance reports. A bridge maintenance knowledge graph (BMKG) construction framework is proposed, developed from a corpus of 275 inspection reports, to enable structured representation of engineering knowledge and decision support. A standards-aligned domain ontology provides semantic constraints for downstream information extraction and organization. Building on this ontology, a RoBERTa–BiGRU–CRF named entity recognition (NER) model is developed, achieving a precision of 90.8%, recall of 93.8%, and a micro-averaged F1-score (micro-F1) of 92.3%. Inter-annotator agreement for the NER annotations was quantified using Cohen’s kappa, yielding κ = 0.86. To avoid the cost of large-scale relation annotation, relations are constructed using interpretable, rule-based constraints. Through manual verification audit of randomly sampled relationship instances under a strict exact-match criterion (i.e., requiring exact matches for entity boundaries, entity types, and relationship types), an overall manual verification rate of 93.67% was obtained. Unlike existing KG methods that rely heavily on annotated data, the BMKG framework integrates ontological constraints with a rule-driven approach, prioritizing interpretability and reducing dependency on large-scale relation labeling. Consequently, the resulting knowledge graph supports semantic retrieval and visual exploration, enabling efficient disease-to-recommendation queries for refined bridge maintenance management. Full article
(This article belongs to the Special Issue Artificial Intelligence for Structural Health Monitoring, Inspection, Maintenance, and Rehabilitation of Civil Infrastructure)
►▼ Show Figures

Figure 1

23 pages, 8681 KB  
Article
Transformer-Based Traffic Flow Prediction Considering Spatio-Temporal Correlations of Bridge Networks
by Yadi Tian, Wanheng Li, Xiaojing Wang, Xin Yan and Yang Xu
Appl. Sci. 2025, 15(16), 8930; https://doi.org/10.3390/app15168930 - 13 Aug 2025
Cited by 4 | Viewed by 2682
Abstract
With the widespread implementation of bridge structural health monitoring (SHM) systems, monitored bridge networks have gradually formed. Understanding vehicle loads and considering spatio-temporal correlations within bridge networks is critical for structural condition assessment and maintenance decision making. This study aims to predict traffic [...] Read more.
With the widespread implementation of bridge structural health monitoring (SHM) systems, monitored bridge networks have gradually formed. Understanding vehicle loads and considering spatio-temporal correlations within bridge networks is critical for structural condition assessment and maintenance decision making. This study aims to predict traffic flows by investigating traffic flow correlations within a bridge network using multi-bridge data, thereby supporting bridge network-level SHM. A transformer-based traffic flow prediction model considering spatio-temporal correlations of bridge networks (ST-TransNet) is proposed. It integrates external factors (processed via fully connected networks) and multi-period traffic flows of input bridges (captured by self-attention encoders) to generate traffic flow predictions through a self-attention decoder. Validated using weigh-in-motion data from an 8-bridge network, the proposed ST-TransNet reduces prediction root mean square error (RMSE) to 12.76 vehicles/10 min, outperforming a series of baselines—SVR, CNN, BiLSTM, CNN&BiLSTM, ST-ResNet, transformer, and STGCN—with significant relative reductions of 40.5%, 36.9%, 36.6%, 37.3%, 35.6%, 31.1%, and 22.8%, respectively. Ablation studies confirm the contribution of each component of the external factors and multi-period traffic flows, particularly the recent traffic flow data. The proposed ST-TransNet effectively captures underlying the spatio-temporal correlations of traffic flow within bridge networks, offering valuable insights for enhancing bridge assessment and maintenance. Full article
(This article belongs to the Special Issue Artificial Intelligence for Structural Health Monitoring, Inspection, Maintenance, and Rehabilitation of Civil Infrastructure)
►▼ Show Figures

Figure 1

18 pages, 1814 KB  
Article
AI-Based Damage Risk Prediction Model Development Using Urban Heat Transport Pipeline Attribute Information
by Sungyeol Lee, Jaemo Kang, Jinyoung Kim and Myeongsik Kong
Appl. Sci. 2025, 15(14), 8003; https://doi.org/10.3390/app15148003 - 18 Jul 2025
Cited by 2 | Viewed by 845
Abstract
This study analyzed the probability of damage in heat transport pipelines buried in urban areas using pipeline attribute information and damage history data and developed an AI-based predictive model. A dataset was constructed by collecting spatial and attribute data of pipelines and defining [...] Read more.
This study analyzed the probability of damage in heat transport pipelines buried in urban areas using pipeline attribute information and damage history data and developed an AI-based predictive model. A dataset was constructed by collecting spatial and attribute data of pipelines and defining basic units according to specific standards. Damage trends were analyzed based on pipeline attributes, and correlation analysis was performed to identify influential factors. These factors were applied to three machine learning algorithms: Random Forest, eXtreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). The model with optimal performance was selected by comparing evaluation indicators including the F2-score, accuracy, and area under the curve (AUC). The LightGBM model trained on data from pipelines in use for over 20 years showed the best performance (F2-score = 0.804, AUC = 0.837). This model was used to generate a risk map visualizing the probability of pipeline damage. The map can aid in the efficient management of urban heat transport systems by enabling preemptive maintenance in high-risk areas. Incorporating external environmental data and auxiliary facility information in future models could further enhance reliability and support the development of a more effective maintenance decision-making system. Full article
(This article belongs to the Special Issue Artificial Intelligence for Structural Health Monitoring, Inspection, Maintenance, and Rehabilitation of Civil Infrastructure)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop