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Drones
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Applications of UAVs in Civil Infrastructure
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Applications of UAVs in Civil Infrastructure

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (15 September 2024) | Viewed by 24351

Special Issue Editors

Prof. Dr. Guido Morgenthal

E-Mail Website
Guest Editor
Institute of Structural Engineering, Bauhaus-Universität Weimar, Marienstrasse 13a, 99423 Weimar, Germany
Interests: structural engineering; bridge design and assessment; structural dynamics; structural health monitoring; image-based condition assessment; drones
Dr. Valerio Baiocchi

E-Mail Website
Guest Editor
Department of Civil, Constructional and Environmental Engineering, Sapienza University of Rome, I-00184 Rome, Italy
Interests: drones; GPS/GNSS; geomatic; geodesy; photogrammetry; survey; remote sensing; GIS; post seismic survey
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Drones have proven to have significant potential in supporting the condition assessment of civil infrastructure and contribute to more efficient maintenance procedures. Unmanned Aerial Vehicles (UAVs) can function as flexible platforms for carrying high-quality digital data acquisition equipment such as image sensors of different spectral ranges, lidar scanners, and GPR as well as further surveying and non-destructive testing devices. They can be operated semi or fully autonomously and thus perform extensive data generation operations near large structures very efficiently. The processing of acquired sensor data can support digital modeling of existing structures, provide deep insight into the structure’s condition and through repeated and systematic flights pave the way to modern data-driven and predictive maintenance strategies. Furthermore, drones can be applied in the context of infrastructure planning for early site investigations or construction progress monitoring. Finally, drones have proven to be very efficient in the management of seismic events and other natural disasters both for early damage assessment and for the safe survey of damaged buildings in order to plan the recovery or restoration of damaged historical buildings.

The enormous potential of UAVs in civil infrastructure requires further scientific developments and the implementation and validation of suitable workflows for ensuring safe, goal-oriented, quality-controlled, and optimized flight operations and data analysis. This Special Issue is aimed at showcasing contributions to the application of UAVs in relation to civil infrastructure on the methodological level as well as on successful and novel applications.

Suggested topics for article contributions are:

  • UAV and sensor hardware development and optimization for applications in civil infrastructure;
  • Automatic and autonomous UAV operation and flight planning near infrastructures;
  • Application scenarios and novel strategies for UAV operation, data generation and analysis for infrastructure planning, construction and maintenance;
  • Complex use cases, validation, benchmark data and quality assessment of UAV applications;
  • Analysis methods and workflows for UAV data in the context of surveying, 3D modeling, condition assessment, visualization and asset management;
  • Methods and procedures for the safe survey of buildings and structures after seismic events and other natural disasters;
  • Proposals for criteria, regulations and standards for UAV applications in civil infrastructure.

Prof. Dr. Guido Morgenthal
Prof. Dr. Valerio Baiocchi
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. Drones 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 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

  • Unmanned Aerial Vehicles
  • civil infrastructure
  • condition assessment
  • surveying
  • image analysis
  • monitoring
  • 3D modeling
  • flight control
  • natural disaster response

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Published Papers (6 papers)

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Research

21 pages, 16344 KiB  
Article
A General Method for Pre-Flight Preparation in Data Collection for Unmanned Aerial Vehicle-Based Bridge Inspection
by Pouya Almasi, Yangjian Xiao, Roshira Premadasa, Jonathan Boyle, David Jauregui, Zhe Wan and Qianyun Zhang
Drones 2024, 8(8), 386; https://doi.org/10.3390/drones8080386 - 9 Aug 2024
Viewed by 1069
Abstract
Unmanned Aerial Vehicles (UAVs) have garnered significant attention in recent years due to their unique features. Utilizing UAVs for bridge inspection offers a promising solution to overcome challenges associated with traditional methods. While UAVs present considerable advantages, there are challenges associated with their [...] Read more.
Unmanned Aerial Vehicles (UAVs) have garnered significant attention in recent years due to their unique features. Utilizing UAVs for bridge inspection offers a promising solution to overcome challenges associated with traditional methods. While UAVs present considerable advantages, there are challenges associated with their use in bridge inspection, particularly in ensuring effective data collection. The primary objective of this study is to tackle the challenges related to data collection in bridge inspection using UAVs. A comprehensive method for pre-flight preparation in data collection is proposed. A well-structured flowchart has been created, covering crucial steps, including identifying the inspection purpose, selecting appropriate hardware, planning and optimizing flight paths, and calibrating sensors. The method has been tested in two case studies of bridge inspections in the State of New Mexico. The results show that the proposed method represents a significant advancement in utilizing UAVs for bridge inspection. These results indicate improvements in accuracy from 7.19% to 21.57% in crack detection using the proposed data collection method. By tackling the data collection challenges, the proposed method serves as a foundation for the application of UAVs for bridge inspection. Full article
(This article belongs to the Special Issue Applications of UAVs in Civil Infrastructure)
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27 pages, 9998 KiB  
Article
Automatic Road Pavement Distress Recognition Using Deep Learning Networks from Unmanned Aerial Imagery
by Farhad Samadzadegan, Farzaneh Dadrass Javan, Farnaz Ashtari Mahini, Mehrnaz Gholamshahi and Francesco Nex
Drones 2024, 8(6), 244; https://doi.org/10.3390/drones8060244 - 4 Jun 2024
Viewed by 1741
Abstract
Detecting and recognizing distress types on road pavement is crucial for selecting the most appropriate methods to repair, maintain, prevent further damage, and ensure the smooth functioning of daily activities. However, this task presents challenges, such as dealing with crowded backgrounds, the presence [...] Read more.
Detecting and recognizing distress types on road pavement is crucial for selecting the most appropriate methods to repair, maintain, prevent further damage, and ensure the smooth functioning of daily activities. However, this task presents challenges, such as dealing with crowded backgrounds, the presence of multiple distress types in images, and their small sizes. In this study, the YOLOv8 network, a cutting-edge single-stage model, is employed to recognize seven common pavement distress types, including transverse cracks, longitudinal cracks, alligator cracks, oblique cracks, potholes, repairs, and delamination, using a dataset comprising 5796 terrestrial and unmanned aerial images. The network’s optimized architecture and multiple convolutional layers facilitate the extraction of high-level semantic features, enhancing algorithm accuracy, speed, and robustness. By combining high and low semantic features, the network achieves improved accuracy in addressing challenges and distinguishing between different distress types. The implemented Convolutional Neural Network demonstrates a recognition precision of 77%, accuracy of 81%, mAP of 79%, f1-score of 74%, and recall of 75%, underscoring the model’s effectiveness in recognizing various pavement distress forms in both aerial and terrestrial images. These results highlight the model’s satisfactory performance and its potential for effectively recognizing and categorizing pavement distress for efficient infrastructure maintenance and management. Full article
(This article belongs to the Special Issue Applications of UAVs in Civil Infrastructure)
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19 pages, 9150 KiB  
Article
Next-Gen Remote Airport Maintenance: UAV-Guided Inspection and Maintenance Using Computer Vision
by Zhiyuan Yang, Sujit Nashik, Cuiting Huang, Michal Aibin and Lino Coria
Drones 2024, 8(6), 225; https://doi.org/10.3390/drones8060225 - 29 May 2024
Viewed by 2174
Abstract
This paper presents a novel system for the automated monitoring and maintenance of gravel runways in remote airports, particularly in Northern Canada, using Unmanned Aerial Vehicles (UAVs) and computer vision technologies. Due to the geographic isolation and harsh weather conditions, these airports face [...] Read more.
This paper presents a novel system for the automated monitoring and maintenance of gravel runways in remote airports, particularly in Northern Canada, using Unmanned Aerial Vehicles (UAVs) and computer vision technologies. Due to the geographic isolation and harsh weather conditions, these airports face unique challenges in runway maintenance. Our approach integrates advanced deep learning algorithms and UAV technology to provide a cost-effective, efficient, and accurate means of detecting runway defects, such as water pooling, vegetation encroachment, and surface irregularities. We developed a hybrid approach combining the vision transformer model with image filtering and thresholding algorithms, applied on high-resolution UAV imagery. This system not only identifies various types of defects but also evaluates runway smoothness, contributing significantly to the safety and reliability of air transport in these areas. Our experiments, conducted across multiple remote airports, demonstrate the effectiveness of our approach in real-world scenarios, offering significant improvements over traditional manual inspection methods. Full article
(This article belongs to the Special Issue Applications of UAVs in Civil Infrastructure)
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23 pages, 19339 KiB  
Article
Integration of UAV Digital Surface Model and HEC-HMS Hydrological Model System in iRIC Hydrological Simulation—A Case Study of Wu River
by Yen-Po Huang, Hui-Ping Tsai and Li-Chi Chiang
Drones 2024, 8(5), 178; https://doi.org/10.3390/drones8050178 - 30 Apr 2024
Viewed by 1284
Abstract
This research investigates flood susceptibility in the mid- and downstream areas of Taiwan’s Wu River, historically prone to flooding in central Taiwan. The study integrates the Hydrologic Engineering Center—Hydrologic Modeling System (HEC-HMS) for flow simulations with unmanned aerial vehicle (UAV)-derived digital surface models [...] Read more.
This research investigates flood susceptibility in the mid- and downstream areas of Taiwan’s Wu River, historically prone to flooding in central Taiwan. The study integrates the Hydrologic Engineering Center—Hydrologic Modeling System (HEC-HMS) for flow simulations with unmanned aerial vehicle (UAV)-derived digital surface models (DSMs) at varying resolutions. Flood simulations, executed through the International River Interface Cooperative (iRIC), assess flood depths using diverse DSM resolutions. Notably, HEC-HMS simulations exhibit commendable Nash–Sutcliffe efficiency (NSE) exceeding 0.88 and a peak flow percentage error (PEPF) below 5%, indicating excellent suitability. In iRIC flood simulations, optimal results emerge with a 2 m resolution UAV-DSM. Furthermore, the study incorporates rainfall data at different recurrence intervals in iRIC flood simulations, presenting an alternative flood modeling approach. This research underscores the efficacy of integrating UAV-DSM into iRIC flood simulations, enabling precise flood depth assessment and risk analysis for flood control management. Full article
(This article belongs to the Special Issue Applications of UAVs in Civil Infrastructure)
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25 pages, 5120 KiB  
Article
Microdrone-Based Indoor Mapping with Graph SLAM
by Samer Karam, Francesco Nex, Bhanu Teja Chidura and Norman Kerle
Drones 2022, 6(11), 352; https://doi.org/10.3390/drones6110352 - 14 Nov 2022
Cited by 18 | Viewed by 6169
Abstract
Unmanned aerial vehicles offer a safe and fast approach to the production of three-dimensional spatial data on the surrounding space. In this article, we present a low-cost SLAM-based drone for creating exploration maps of building interiors. The focus is on emergency response mapping [...] Read more.
Unmanned aerial vehicles offer a safe and fast approach to the production of three-dimensional spatial data on the surrounding space. In this article, we present a low-cost SLAM-based drone for creating exploration maps of building interiors. The focus is on emergency response mapping in inaccessible or potentially dangerous places. For this purpose, we used a quadcopter microdrone equipped with six laser rangefinders (1D scanners) and an optical sensor for mapping and positioning. The employed SLAM is designed to map indoor spaces with planar structures through graph optimization. It performs loop-closure detection and correction to recognize previously visited places, and to correct the accumulated drift over time. The proposed methodology was validated for several indoor environments. We investigated the performance of our drone against a multilayer LiDAR-carrying macrodrone, a vision-aided navigation helmet, and ground truth obtained with a terrestrial laser scanner. The experimental results indicate that our SLAM system is capable of creating quality exploration maps of small indoor spaces, and handling the loop-closure problem. The accumulated drift without loop closure was on average 1.1% (0.35 m) over a 31-m-long acquisition trajectory. Moreover, the comparison results demonstrated that our flying microdrone provided a comparable performance to the multilayer LiDAR-based macrodrone, given the low deviation between the point clouds built by both drones. Approximately 85 % of the cloud-to-cloud distances were less than 10 cm. Full article
(This article belongs to the Special Issue Applications of UAVs in Civil Infrastructure)
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16 pages, 2167 KiB  
Article
Flying Washer: Development of High-Pressure Washing Aerial Robot Employing Multirotor Platform with Add-On Thrusters
by Ryo Miyazaki, Hannibal Paul, Takamasa Kominami, Ricardo Rosales Martinez and Kazuhiro Shimonomura
Drones 2022, 6(10), 286; https://doi.org/10.3390/drones6100286 - 2 Oct 2022
Cited by 5 | Viewed by 4208
Abstract
In this study, we propose a multirotor aerial robot for high-pressure washing tasks at high altitudes. The aerial robot consists of a multirotor platform, an add-on planar translational driving system (ATD), a visual sensing system, and a high-pressure washing system. The ATD consists [...] Read more.
In this study, we propose a multirotor aerial robot for high-pressure washing tasks at high altitudes. The aerial robot consists of a multirotor platform, an add-on planar translational driving system (ATD), a visual sensing system, and a high-pressure washing system. The ATD consists of three ducted fans, which can generate force in all directions on the horizontal plane. The ATD also allows the multirotor to suppress the reaction force generated by the nozzle of a high-pressure washing system and inject water accurately. In this study, we propose a method to precisely inject water by installing an ATD in the multirotor and using its driving force to suppress the reaction force and move the multirotor while keeping its posture horizontal. The semi-autonomous system was designed to allow the operator to maneuver the multirotor while maintaining a constant distance from the wall by the sensor feedback with onboard LiDAR or stereo camera. In the experiment, we succeeded in performing the high-pressure washing task in a real environment and verified that the reaction force generated from the nozzle was actually suppressed during the task. Full article
(This article belongs to the Special Issue Applications of UAVs in Civil Infrastructure)
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