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Advanced Technologies in Construction and Infrastructure: Theory, Methods and Applications—2nd Edition

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 2796

Special Issue Editors

Prof. Dr. Joo-Sung Lee

E-Mail Website
Guest Editor
Department of Architecture, Kangwon National University, 346 Jungang-ro, Samchuk 25913, Kangwon-do, Republic of Korea
Interests: construction management; construction ICT for automation; BIM; delivery system; lean & pre-construction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sungkon Moon

E-Mail Website
Guest Editor
Department of Civil Systems Engineering, Ajou University, Suwon 16499, Republic of Korea
Interests: construction engineering; automation and control engineering; logistics and supply chain management; technology innovation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The scope of this Special Issue covers innovative technology applications in construction and infrastructure. It also presents theories, methods and cases of new technologies. New construction technologies can allow for relevant academics and those working in industry to conduct more innovative, faster and more sustainable projects. The final goal of this Special Issue is to advance sustainable development in the industry by achieving successful application of new technologies. Construction technology is a collective terminology that includes many different types of innovations. Cutting-edge ideas and methods will be the main focus, and the practical and theoretical topics associated with construction technology will also be a core area of this Special Issue.

The topics of interest include, but are not limited to, the following:

  • Innovation in buildings, civil and infrastructure engineering;
  • Virtual technologies, including BIM, AR, VR and the Metaverse;
  • Artificial intelligence (AI) and construction robotics;
  • Offsite construction and design for manufacture and assembly;
  • Renewable energy and power plant construction;
  • Smart database and cloud communication in supply chains;
  • Blockchain and information security;
  • Unmanned aerial vehicles;
  • Additive technology and 3DP construction;
  • Data-driven analysis in construction.

Prof. Dr. Joo-Sung Lee
Prof. Dr. Sungkon Moon
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

  • innovation in buildings, civil and infrastructure engineering
  • virtual technologies, including BIM, AR, VR and the Metaverse
  • artificial intelligence (AI) and construction robotics
  • offsite construction and design for manufacture and assembly
  • renewable energy and power plant construction
  • smart database and cloud communication in supply chains
  • blockchain and information security
  • unmanned aerial vehicles
  • additive technology and 3DP construction
  • data-driven analysis in construction

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

Related Special Issue

Published Papers (3 papers)

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Research

26 pages, 13085 KiB  
Article
Image Augmentation Approaches for Building Dimension Estimation in Street View Images Using Object Detection and Instance Segmentation Based on Deep Learning
by Dongjin Hwang, Jae-Jun Kim, Sungkon Moon and Seunghyeon Wang
Appl. Sci. 2025, 15(5), 2525; https://doi.org/10.3390/app15052525 - 26 Feb 2025
Cited by 3 | Viewed by 516
Abstract
There are numerous applications for building dimension data, including building performance simulation and urban heat island investigations. In this context, object detection and instance segmentation methods—based on deep learning—are often used with Street View Images (SVIs) to estimate building dimensions. However, these methods [...] Read more.
There are numerous applications for building dimension data, including building performance simulation and urban heat island investigations. In this context, object detection and instance segmentation methods—based on deep learning—are often used with Street View Images (SVIs) to estimate building dimensions. However, these methods typically depend on large and diverse datasets. Image augmentation can artificially boost dataset diversity, yet its role in building dimension estimation from SVIs remains under-studied. This research presents a methodology that applies eight distinct augmentation techniques—brightness, contrast, perspective, rotation, scale, shearing, translation augmentation, and a combined “sum of all” approach—to train models in two tasks: object detection with Faster Region-Based Convolutional Neural Networks (Faster R-CNNs) and instance segmentation with You Only Look Once (YOLO)v10. Comparing the performance with and without augmentation revealed that contrast augmentation consistently provided the greatest improvement in both bounding-box detection and instance segmentation. Using all augmentations at once rarely outperformed the single most effective method, and sometimes degraded the accuracy; shearing augmentation ranked as the second-best approach. Notably, the validation and test findings were closely aligned. These results, alongside the potential applications and the method’s current limitations, underscore the importance of carefully selected augmentations for reliable building dimension estimation. Full article
(This article belongs to the Special Issue Advanced Technologies in Construction and Infrastructure: Theory, Methods and Applications—2nd Edition)
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27 pages, 5808 KiB  
Article
Integrated Digital-Twin-Based Decision Support System for Relocatable Module Allocation Plan: Case Study of Relocatable Modular School System
by Truong Dang Hoang Nhat Nguyen, Yonghan Ahn and Byeol Kim
Appl. Sci. 2025, 15(4), 2211; https://doi.org/10.3390/app15042211 - 19 Feb 2025
Viewed by 602
Abstract
Relocatable modular buildings (RMBs) offer significant advantages, including flexibility, mobility, and scalability, making them ideal for temporary or rapidly changing scenarios. However, as the scale and quantity of RMB modules increase, their allocation across projects poses complex logistical challenges. Inefficiencies in traditional manual [...] Read more.
Relocatable modular buildings (RMBs) offer significant advantages, including flexibility, mobility, and scalability, making them ideal for temporary or rapidly changing scenarios. However, as the scale and quantity of RMB modules increase, their allocation across projects poses complex logistical challenges. Inefficiencies in traditional manual allocation methods, such as suboptimal module selection, increased transportation costs, and project delays, underscore the need for innovative solutions. This study develops a Digital Twin (DT)-based decision support system to optimize the allocation and management of RMB modules. The proposed framework integrates Building Information Modeling (BIM), Internet of Things (IoT), and Geographic Information Systems (GISs), enabling the real-time synchronization of physical assets with their digital counterparts. The DT framework incorporates real-time data acquisition, dynamic module condition assessments, and an algorithm-driven allocation process to streamline resource utilization and logistics planning. The system is validated through a case study of South Korea’s first relocatable modular school system project, demonstrating its capability to optimize module allocation, reduce costs, and enhance lifecycle management. This study advances RMB management by offering a practical, data-driven approach, empowering facility managers to leverage real-time data for preventive maintenance, asset optimization, and sustainable resource utilization. Full article
(This article belongs to the Special Issue Advanced Technologies in Construction and Infrastructure: Theory, Methods and Applications—2nd Edition)
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19 pages, 5079 KiB  
Article
Simplified Scan-vs-BIM Frameworks for Automated Structural Inspection of Steel Structures
by Bohee Kim, Inho Jo, Namhyuk Ham and Jae-jun Kim
Appl. Sci. 2024, 14(23), 11383; https://doi.org/10.3390/app142311383 - 6 Dec 2024
Viewed by 1208
Abstract
This paper presents a deep learning-based Scan-vs-BIM methodology for evaluating structural integrity through the extraction of features from As-Built scan and As-Planned Building Information Modeling (BIM) comparison data. Traditional Scan-vs-BIM frameworks often rely on Scan-to-BIM processes to generate point cloud-based mesh models for [...] Read more.
This paper presents a deep learning-based Scan-vs-BIM methodology for evaluating structural integrity through the extraction of features from As-Built scan and As-Planned Building Information Modeling (BIM) comparison data. Traditional Scan-vs-BIM frameworks often rely on Scan-to-BIM processes to generate point cloud-based mesh models for comparison, which significantly impairs computational efficiency. In contrast, the proposed streamlined Scan-vs-BIM framework incorporates a deep neural network (DNN) model consisting of two neural networks: one for structural integrity assessment and another for error type analysis. The model evaluates the structural integrity of individual components in a sequential manner, repeating the process across all elements to comprehensively assess the entire structure. Rather than converting point cloud data into mesh models for comparison, this approach directly measures the spatial discrepancies between the As-Built point cloud and As-Planned BIM, analyzing the distribution tendencies of these distance values. Experimental validation on actual steel structures demonstrated that the proposed method effectively predicts structural integrity, providing significant improvements in both accuracy and computational performance. Full article
(This article belongs to the Special Issue Advanced Technologies in Construction and Infrastructure: Theory, Methods and Applications—2nd Edition)
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