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Applied Sciences
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Construction Automation and Robotics
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Construction Automation and Robotics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (20 May 2025) | Viewed by 2842

Special Issue Editors

Prof. Dr. Jongwon Seo

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Hanyang University, Seoul 04763, Republic of Korea
Interests: GIS; BIM; construction automation
Special Issues, Collections and Topics in MDPI journals
Dr. Jinhyun Kim

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
Interests: robotics; underwater robots; mobile robots

Special Issue Information

Dear Colleagues,

New technologies are emerging for the automation of construction and maintenance processes. The ever-increasing labor turnover rate, safety concerns, and competition in today's construction market has made the emergence of new technologies inevitable. New construction technologies include both soft and hard automation. Soft automation refers to the use of information technology, while hard automation refers to the use of automated machinery and robots. They are often combined to achieve a specific goal. The core foundations of emerging construction technologies are computing/information technology, BIM, AI, sensing, and robotics. In this regard, this Special Issue invites you to submit original research papers regarding ‘Construction Automation and Robotics’. Topics may include, but are not limited to, the following:

  • Data sensing and modeling technologies such as digital twin, Internet of Things, sensor networks, drones
  • Construction automation technologies such as intelligent construction equipment, robots, and human–machine interaction
  • Data-driven artificial intelligence technologies such as big data and machine learning
  • Visualization technologies such as virtual reality, augmented reality, and mixed reality
  • Computer vision technologies such as digital image processing, object detection and tracking, location identification, and visual pattern recognition
  • nD modeling and simulation technologies such as virtual physical systems and processor modeling
  • State-of-the-art methods such as modular, prefabrication, and 3D printing
  • BIM and construction automation/robotics application technology
  • Construction automation/robotics-based construction project management
  • Construction automation/robotics-related systems and policies

Prof. Dr. Jongwon Seo
Dr. Jinhyun Kim
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. 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

  • construction automation
  • robotics
  • BIM

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Published Papers (3 papers)

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Research

17 pages, 3154 KiB  
Article
The Influence of Real-Time Feedback on Excavator Operator Actions in Footing Excavation: Machine Guidance and Conventional Methods
by Hyunsik Kim, Jeonghwan Kim, Bangyul An, Taeseok Song, Jaehoon Oh, Minki Kim and Seungju Lee
Appl. Sci. 2025, 15(7), 3729; https://doi.org/10.3390/app15073729 - 28 Mar 2025
Viewed by 307
Abstract
Excavator operations play a critical role in the productivity of earthworks, yet traditional methods often rely heavily on operators’ intuition and experience, which can lead to inconsistent outcomes. This study investigates how machine guidance (MG) providing real-time feedback relating to excavation depth and [...] Read more.
Excavator operations play a critical role in the productivity of earthworks, yet traditional methods often rely heavily on operators’ intuition and experience, which can lead to inconsistent outcomes. This study investigates how machine guidance (MG) providing real-time feedback relating to excavation depth and slope can modify operators’ actions and improve performance compared with conventional excavation methods. A controlled experiment was conducted at an active construction site, in which four footings were excavated using the two approaches under similar conditions. The results demonstrated that MG excavation reduced the total duration of the work from 3650 s to 2652 s and decreased the number of excavation cycles from 68 to 57, underscoring the impact of timely, precise guidance on efficiency. Moreover, the average fill factor improved from 3.04 under conventional methods to 3.47 with MG, suggesting more consistent and optimal loading of the bucket. These findings confirm that real-time feedback can enhance operator confidence, reduce unnecessary movements, and foster systematic excavation strategies. This study thus provides empirical evidence that MG can significantly optimize excavation performance, highlighting the need for broader adoption of this technology in modern construction practices. Full article
(This article belongs to the Special Issue Construction Automation and Robotics)
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20 pages, 5795 KiB  
Article
Effectiveness of Image Augmentation Techniques on Non-Protective Personal Equipment Detection Using YOLOv8
by Sungman Park, Jaejun Kim, Seunghyeon Wang and Juhyung Kim
Appl. Sci. 2025, 15(5), 2631; https://doi.org/10.3390/app15052631 - 28 Feb 2025
Cited by 3 | Viewed by 829
Abstract
Non-Protective Personal Equipment (PPE) detection is crucial on construction sites. Although deep learning models are adept at identifying such information from on-site cameras, their success relies on large, diverse, and high-quality datasets. Image augmentation offers an alternative for artificially broadening dataset diversity. However, [...] Read more.
Non-Protective Personal Equipment (PPE) detection is crucial on construction sites. Although deep learning models are adept at identifying such information from on-site cameras, their success relies on large, diverse, and high-quality datasets. Image augmentation offers an alternative for artificially broadening dataset diversity. However, its impact on non-PPE detection in construction environments has not been adequately examined. This study introduces a methodology applying eight distinct augmentation techniques—brightness, contrast, perspective, rotation, scale, shearing, translation, and a combined strategy incorporating all methods. Model performance was assessed by comparing accuracy across different classes and architectures, both with and without augmentation. While most of these augmentations improved accuracy, their effectiveness was found to be task-dependent. Moreover, the most beneficial augmentation varied by non-PPE class and architecture, suggesting that augmentation strategies should be tailored to the unique features of each class and model. Although the primary focus here is on non-PPE, the evaluated techniques could also extend to related tasks on construction sites, such as detecting heavy equipment or identifying hazardous worker behavior. Full article
(This article belongs to the Special Issue Construction Automation and Robotics)
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20 pages, 19579 KiB  
Article
Integrating Task Component Design in Excavator–Truck Operation Planning: Enhancing Automation Systems through a Web-Based System
by Soohyun Park, Jeonghwan Kim, Kiyong Cho and Jongwon Seo
Appl. Sci. 2024, 14(14), 6052; https://doi.org/10.3390/app14146052 - 11 Jul 2024
Cited by 1 | Viewed by 1208
Abstract
Excavator–truck operations, characterized by their repetitive excavation and loading tasks, present a prime candidate for automation. While numerous studies have aimed to automate the earthworks, practical implementations remain scarce. This research introduces a task component design focused on excavator–truck operation planning to improve [...] Read more.
Excavator–truck operations, characterized by their repetitive excavation and loading tasks, present a prime candidate for automation. While numerous studies have aimed to automate the earthworks, practical implementations remain scarce. This research introduces a task component design focused on excavator–truck operation planning to improve the functionality of an earthwork automation system. To address this, fundamental task primitives necessary for executing excavation tasks were engineered, and a web-based system was developed to automate the generation of work plans for both point and trench excavation through algorithmic processes. Additionally, a JSON-based protocol was introduced to facilitate efficient integration with other subsystems. Field tests were conducted to validate the effectiveness of the newly developed algorithm and protocol within the broader context of earthwork automation systems. The results demonstrated the successful implementation of the task components, confirming their operational viability and seamless integration into the existing earthwork automation framework. Full article
(This article belongs to the Special Issue Construction Automation and Robotics)
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