Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.7
3.6
Journals
Infrastructures
Special Issues
Advanced Technologies for Bridge Health Monitoring
share announcement

Advanced Technologies for Bridge Health Monitoring

A special issue of Infrastructures (ISSN 2412-3811). This special issue belongs to the section "Infrastructures Inspection and Maintenance".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1198

Editors

Dr. Adriana Trias Blanco

E-Mail Website
Guest Editor
Civil and Environmental Engineering Department, Rowan University, Glassboro, NJ, USA
Interests: remote sensing; non-destructive evaluation; structural health monitoring; condition assessment; data fusion
Dr. Diogo Ribeiro

E-Mail Website
Guest Editor
1. iBuilt, School of Engineering, Polytechnic of Porto, Porto, Portugal
2. COSNTRUCT, Faculty of Engineering, University of Porto, Porto, Portugal
Interests: railway infrastructures; condition assessment; remote inspection; digital twins; AI; structural health monitoring; digital construction; dynamic testing; drive-by strategies
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: computer vision; digital twins; autonomous inspections; infrastructure resilience; construction robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The state of civil infrastructure is fundamental to socio-economic development worldwide. Aging bridges, railway systems, transportation networks, and other critical assets face increasing demands due to climate change, urbanization, extreme events, and growing service loads. At the same time, rapid advancements in sensing technologies, artificial intelligence (AI), robotics, and digital modeling are transforming the way infrastructure is inspected, monitored, analyzed, and managed. Over the past decade, the role of infrastructure engineers has evolved significantly. Traditional inspection and condition assessment approaches are increasingly complemented by data-driven methodologies, autonomous inspection systems, remote sensing technologies, and Digital Twin frameworks. Emerging tools such as computer vision, deep learning, point cloud processing, and multi-sensor data fusion are enabling scalable, automated, and more objective infrastructure evaluation strategies. These advances are paving the way for predictive maintenance, risk-informed decision-making, and life-cycle optimization of critical systems.

This Special Issue aims to disseminate high-quality original research that advances the state of the art in remote inspection, structural health monitoring (SHM), non-destructive evaluation (NDE), digital construction, and AI-enabled infrastructure management. We encourage interdisciplinary studies that bridge sensing technologies, data analytics, mechanics-based modeling, robotics, and resilience assessment, fostering cross-sector innovation and scalable solutions.

Dr. Adriana Trias Blanco
Dr. Diogo Ribeiro
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Infrastructures is an international peer-reviewed open access monthly 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 1800 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

  • bridge inspection
  • structural health monitoring
  • nondestructive evaluation
  • artificial intelligence and machine learning
  • digital twins

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 (2 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

20 pages, 1355 KB  
Article
Spatial Patterns of Bridge Deterioration and Municipal Maintenance Potential for Municipality-Managed Bridges in the Chubu Region of Japan
by Saki Namimatsu
Infrastructures 2026, 11(7), 239; https://doi.org/10.3390/infrastructures11070239 - 15 Jul 2026
Viewed by 259
Abstract
Aging bridge infrastructure poses a growing challenge for Japanese municipalities under population decline, fiscal constraints, and shortages of technical personnel. This study analyzes municipality-managed bridges in the Chubu region of Japan by integrating bridge inspection records with demographic, fiscal, technical staffing, and regional-condition [...] Read more.
Aging bridge infrastructure poses a growing challenge for Japanese municipalities under population decline, fiscal constraints, and shortages of technical personnel. This study analyzes municipality-managed bridges in the Chubu region of Japan by integrating bridge inspection records with demographic, fiscal, technical staffing, and regional-condition indicators. Bridge deterioration severity for 237 municipalities was evaluated, and its spatial structure was examined using Global Moran’s I and local indicators of spatial association (LISA), which respectively indicate whether similar deterioration levels are regionally clustered across the study area and where local clusters or spatial outliers occur. The results showed significant positive spatial autocorrelation, indicating that deterioration is spatially clustered and locally heterogeneous. Municipal maintenance potential was then represented through principal component analysis and classified by cluster analysis, identifying seven municipal types characterized by different combinations of depopulation, wide-area management burden, land-use conditions, and fiscal constraints. By integrating deterioration severity, LISA composition, and maintenance-potential typologies, the study distinguished municipalities where low deterioration is spatially stable, municipalities where highly deteriorated areas are locally concentrated, and municipalities where deterioration is high but spatially dispersed. These findings provide a basis for targeted support and strategic bridge management. Full article
(This article belongs to the Special Issue Advanced Technologies for Bridge Health Monitoring)
►▼ Show Figures

Figure 1

28 pages, 8801 KB  
Article
Smartphone and Smartwatch Crowdsensing for Bridge Modal Identification with Convergence Behavior and Bootstrap Uncertainty Analysis
by Furkan Luleci and Sadig Nuraliyev
Infrastructures 2026, 11(6), 204; https://doi.org/10.3390/infrastructures11060204 - 16 Jun 2026
Viewed by 578
Abstract
This study investigates the feasibility, accuracy, and data-sufficiency requirements of smartphone- and smartwatch-based crowdsensing for pedestrian bridge modal identification under real-world conditions. Full-scale experiments were conducted on a bridge across two crowdsensing scenarios with varying dynamic excitation intensities by six pedestrians performing walking, [...] Read more.
This study investigates the feasibility, accuracy, and data-sufficiency requirements of smartphone- and smartwatch-based crowdsensing for pedestrian bridge modal identification under real-world conditions. Full-scale experiments were conducted on a bridge across two crowdsensing scenarios with varying dynamic excitation intensities by six pedestrians performing walking, running, and bicycling activities while carrying smartphones and wearing smartwatches. Triaxial acceleration data were collected over 300 s and processed using a framework comprising preprocessing, modal estimation, growing-window convergence analysis, and block-bootstrap uncertainty quantification. Using the full dataset, both devices reliably identified the four consistently detectable bridge modes with average errors of approximately 3% across the scenarios relative to the benchmark. In the convergence analysis, smartwatches consistently produced narrower confidence intervals and more stable early-window estimates, which may be related to their more constrained wearing condition and reduced incidental motion compared to pocket-carried smartphones. Higher pedestrian excitation with additional pedestrians running accelerated the convergence, reducing the required data duration and number of pedestrian passes, albeit with increased uncertainty. The study established data-sufficiency thresholds, showing that reliable modal estimates require in the range of 5–17 walking or running passes, while bicycling passes range from 14 to 28, depending on bridge excitation level and device type. Results demonstrate that commodity smartphones and smartwatches are viable, scalable, and cost-effective platforms for crowdsensed bridge modal identification, provided that uncertainty ranges are properly accounted for and sufficient passes across different pedestrian activities are collected to achieve the desired accuracy. Full article
(This article belongs to the Special Issue Advanced Technologies for Bridge Health Monitoring)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop