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Applied Sciences
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Application of Computer Vision and Deep Learning in Construction Engineering
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Application of Computer Vision and Deep Learning in Construction Engineering

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

Deadline for manuscript submissions: 20 January 2026 | Viewed by 2204

Special Issue Editors

Dr. Seunghyeon Wang

E-Mail Website
Guest Editor
Department of Architectural Engineering, Hanyang University, Seoul 04763, Republic of Korea
Interests: computer vision; artificial intelligence; deep learning; object detection; semantic segmenation; instance segmentation
Prof. Sungkon Moon

E-Mail Website
Guest Editor
Department of Civil Systems Engineering, College of 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,

Construction engineering is increasingly benefiting from advancements in computer vision and deep learning technologies, transforming various aspects, such as safety management, quality control, productivity monitoring, and asset management. This Special Issue focuses on innovative research and practical applications leveraging computer vision and deep learning techniques within the domain of construction engineering. We encourage submissions highlighting novel methods, theoretical advancements, experimental studies, and comprehensive reviews addressing challenges and opportunities for integrating these technologies into construction practices. Areas of particular interest include, but are not limited to, automated detection and classification of construction defects, site safety monitoring through image and video analytics, 3D reconstruction and progress monitoring using UAV-based photogrammetry, digital twin creation and management, real-time personnel and equipment tracking, and advanced predictive analytics for project management. Submissions should emphasize both technical developments and practical implications to enhance efficiency, safety, and quality outcomes in construction engineering.

Dr. Seunghyeon Wang
Prof. 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 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-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

  • computer vision
  • deep learning
  • construction engineering
  • safety monitoring
  • defect detection
  • photogrammetry
  • digital twin
  • predictive analytics
  • UAV
  • image processing

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Published Papers (2 papers)

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17 pages, 3856 KB  
Article
Wavelet Fusion with Sobel-Based Weighting for Enhanced Clarity in Underwater Hydraulic Infrastructure Inspection
by Minghui Zhang, Jingkui Zhang, Jugang Luo, Jiakun Hu, Xiaoping Zhang and Juncai Xu
Appl. Sci. 2025, 15(14), 8037; https://doi.org/10.3390/app15148037 - 18 Jul 2025
Viewed by 752
Abstract
Underwater inspection images of hydraulic structures often suffer from haze, severe color distortion, low contrast, and blurred textures, impairing the accuracy of automated crack, spalling, and corrosion detection. However, many existing enhancement methods fail to preserve structural details and suppress noise in turbid [...] Read more.
Underwater inspection images of hydraulic structures often suffer from haze, severe color distortion, low contrast, and blurred textures, impairing the accuracy of automated crack, spalling, and corrosion detection. However, many existing enhancement methods fail to preserve structural details and suppress noise in turbid environments. To address these limitations, we propose a compact image enhancement framework called Wavelet Fusion with Sobel-based Weighting (WWSF). This method first corrects global color and luminance distributions using multiscale Retinex and gamma mapping, followed by local contrast enhancement via CLAHE in the L channel of the CIELAB color space. Two preliminarily corrected images are decomposed using discrete wavelet transform (DWT); low-frequency bands are fused based on maximum energy, while high-frequency bands are adaptively weighted by Sobel edge energy to highlight structural features and suppress background noise. The enhanced image is reconstructed via inverse DWT. Experiments on real-world sluice gate datasets demonstrate that WWSF outperforms six state-of-the-art methods, achieving the highest scores on UIQM and AG while remaining competitive on entropy (EN). Moreover, the method retains strong robustness under high turbidity conditions (T ≥ 35 NTU), producing sharper edges, more faithful color representation, and improved texture clarity. These results indicate that WWSF is an effective preprocessing tool for downstream tasks such as segmentation, defect classification, and condition assessment of hydraulic infrastructure in complex underwater environments. Full article
(This article belongs to the Special Issue Application of Computer Vision and Deep Learning in Construction Engineering)
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Review

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33 pages, 5070 KB  
Review
Railway Track Structural Health Monitoring: Identifying Emerging Trends and Research Agendas Using Bibliometric and Topic Modeling
by Tien Phat Dinh, Quang Hoai Le, Thao Nguyen Thach, Byeol Kim and Yonghan Ahn
Appl. Sci. 2025, 15(23), 12462; https://doi.org/10.3390/app152312462 - 24 Nov 2025
Viewed by 1108
Abstract
While railways are critical for transportation, their expansive networks spanning thousands of kilometers pose significant challenges for conventional structural health inspection and maintenance. Recent advancements in sensors and artificial intelligence technologies have led to a substantial growth in the body of research proposing [...] Read more.
While railways are critical for transportation, their expansive networks spanning thousands of kilometers pose significant challenges for conventional structural health inspection and maintenance. Recent advancements in sensors and artificial intelligence technologies have led to a substantial growth in the body of research proposing innovative approaches for Railway Track Structural Health Monitoring (RTSHM) to enhance safety and operational efficiency. This work aims to synthesize the current RTSHM research landscape to explore mainstream and emerging directions and identify advancements, challenges, and opportunities in this field. Through the hybrid systematic review using bibliometrics analysis and topic modeling, core research themes emerged, from developing sensor and data acquisition techniques as the foundation, to be combined with AI-based methods for fault detection and prediction. These predictions are leveraged for predictive maintenance through degradation modeling, supplemented with information from dynamic response assessment and performance optimization, and the ultimate goal is integration of RTSHM for operational safety assessments and risk-based decision-making. While technologically advanced, current research predominantly focuses on detecting discrete defects, thereby neglecting the holistic management of the track system. This fragmentation contributes to a complex and often siloed landscape for infrastructure management, emphasizing that RTSHM remains in a critical developmental stage. Consequently, the development of smart railway, integrated with intelligent data collection devices, deep learning technologies, and innovative operational platforms, represents a challenging yet promising direction for future research. These advancements are anticipated to foster safer, more efficient, and sustainable railway systems worldwide. Full article
(This article belongs to the Special Issue Application of Computer Vision and Deep Learning in Construction Engineering)
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