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Applied Sciences
Special Issues
Monitoring and Maintenance of Buildings and Infrastructure Facilities
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Monitoring and Maintenance of Buildings and Infrastructure Facilities

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

Deadline for manuscript submissions: closed (1 September 2022) | Viewed by 1565

Special Issue Editor

Prof. Dr. Seong-Hoon Jeong

E-Mail Website
Guest Editor
Department of Architectural Engineering, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, Korea
Interests: structural health monitoring of civil structures; earthquake engineering; structural dynamics; reinforced concrete structures

Special Issue Information

Dear Colleagues,

This Special Issue aims to discuss recent advances in the application of monitoring technologies for buildings and infrastructure facilities. The usage of the latest technologies of sensors and networks has led to breakthrough advances in facility monitoring systems. It is envisaged that international researchers will share ideas and research outcomes for structural engineering and constructional and building engineering. As an applied engineering technology, research in the field of facility monitoring and management is inherently multi-disciplinary. The common technologies that provide tools for structural health monitoring, construction automation and building environment control have a disciplinary foundation in sensor theories, signal processing and data analytics. Debates and proposals for the cross-disciplinary research of system monitoring technologies are highly appreciated.

Prof. Dr. Seong-Hoon Jeong
Guest Editor

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. 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

  • buildings
  • bridges
  • industrial facilities
  • infrastructures
  • building facilities
  • building environment
  • construction robotics
  • sensors
  • structural monitoring
  • hybrid simulation
  • environmental monitoring and measurements
  • testbed measurements
  • facility management
  • structural responses
  • condition assessment of structures
  • system identification
  • ambient vibration
  • wind engineering
  • earthquake engineering
  • structural safety
  • experimental tests for response analysis
  • big data analytics
  • machine learning
  • vibration control
  • hazard mitigation

Published Papers (1 paper)

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Research

17 pages, 16157 KiB  
Article
A Deep-Learning-Based Bridge Damaged Object Automatic Detection Model Using a Bridge Member Model Combination Framework
by Sung-Sam Hong, Cheol-Hoon Hwang, Su-Wan Chung and Byung-Kon Kim
Appl. Sci. 2022, 12(24), 12868; https://0-doi-org.brum.beds.ac.uk/10.3390/app122412868 - 14 Dec 2022
Cited by 2 | Viewed by 1200
Abstract
More bridges today require maintenance with age, owing to increasing structural loads from traffic and natural disasters. Routine inspection for damages, including in the aftermath of special events, is conducted by experts. To address the limitations of human inspection, deep-learning-based analysis of bridge [...] Read more.
More bridges today require maintenance with age, owing to increasing structural loads from traffic and natural disasters. Routine inspection for damages, including in the aftermath of special events, is conducted by experts. To address the limitations of human inspection, deep-learning-based analysis of bridge damage is being actively conducted. However, such models exhibit deteriorated performance in classifying multiple classes. Most existing algorithms do not use in situ images. Hence, the results of the model training do not accurately reflect the actual damage. This study utilizes an extant method and proposes a new model of combination training by bridge member. By integrating the two approaches, we propose a bridge damaged-object-detection deep-combination framework (BDODC-F). To ensure variety in the type of damaged objects and enhanced model performance, a deep-learning-based super-resolution module is employed. For performance improvement and optimization, a deep-learning combination model based on individual training by bridge member is proposed. The BDODC-F improved the mean average precision by 191.6% and 112.21% in the combination model. We expect the framework to aid engineers in the automated detection and identification of bridge damage. Full article
(This article belongs to the Special Issue Monitoring and Maintenance of Buildings and Infrastructure Facilities)
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