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Digital Advancements in Civil Engineering and Construction

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

Deadline for manuscript submissions: 20 November 2026 | Viewed by 762

Special Issue Editors


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Guest Editor
School of Civil Engineering, Southeast University, Nanjing 211189, China
Interests: digitalization and intelligence in civil engineering; BIM; digital twins; smart construction; reality capture; structural health monitoring

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Guest Editor
School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough LE11 3TU, UK
Interests: non-destructive testing in civil engineering; static and dynamic soil-structure interaction; multi-disciplinary engineering; geotechnical engineering; structural engineering; numerical methods

Special Issue Information

Dear Colleagues,

Digital technologies are transforming civil engineering and construction from labor-intensive industries to data-driven, intelligent systems and the essence of civil engineering—creating human living spaces—has evolved with rapid technological advancement over the past two decades. Building information modeling (BIM), digital twins, and digital construction not only enhance building efficiency but also establish foundations for autonomous construction. These innovations represent critical means of preparing for construction in extreme terrestrial or extraterrestrial environments in an era driven by AI and robotics. Data collected through digital technologies now trains AI models while supporting equipment development for construction in harsh environments. This Special Issue explores how digital innovation reshapes the entire construction lifecycle, with BIM, digital twins, IoT, and mature AI now enabling engineering projects to achieve unprecedented precision and sustainability. The emergence of intelligent agents and autonomous decision-making systems offers new solutions to complex engineering challenges. From intelligent scheduling to autonomous construction systems and real-time monitoring, digital innovations create smarter, safer built environments. This Special Issue will showcase, therefore, cutting-edge research that addresses industry challenges while preparing for construction in extreme conditions, and we welcome contributions spanning theoretical research, technological development, and practical applications that advance the digital transformation of civil engineering.

Dr. Daguang Han
Dr. Ashraf El-Hamalawi
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

  • digital twins
  • AI agents
  • autonomous construction systems
  • autonomous decision-making
  • construction in extreme environments
  • extraterrestrial construction technologies
  • environmentally adaptive construction
  • construction robotics
  • digital transformation
  • AI for BIM
  • 3D-printing
  • autonomous decision-making
  • artificial intelligence in construction

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Published Papers (1 paper)

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Research

23 pages, 5390 KB  
Article
A Metrologically Validated Cost-Effective Solution for Laboratory Measurement of Long-Term Deformations in Construction Materials
by Ahmad Fathi, Luís Lages Martins, João M. Pereira, Graça Vasconcelos and Miguel Azenha
Appl. Sci. 2026, 16(4), 1866; https://doi.org/10.3390/app16041866 - 13 Feb 2026
Viewed by 413
Abstract
Investigating the long-term performance of building materials, such as drying shrinkage, moisture expansion, creep, and others, usually requires long-lasting tests with a high number of specimens. Given the initial costs, required data acquisition systems, and the time allocated, conventional sensors like LVDTs become [...] Read more.
Investigating the long-term performance of building materials, such as drying shrinkage, moisture expansion, creep, and others, usually requires long-lasting tests with a high number of specimens. Given the initial costs, required data acquisition systems, and the time allocated, conventional sensors like LVDTs become costly for such long-term experimental studies. This article proposes an innovative cost-effective solution combining optical microscopy imaging, 3D printed sliding rulers, and Python-based artificial vision to overcome these limitations. The 3D printed rulers establish a local physical reference frame, while the artificial vision system uses contour detection and point tracking of optical targets to quantify displacements. Unlike continuous monitoring systems, the proposed solution utilises a discontinuous point-tracking approach, allowing a single USB microscope to monitor an unlimited number of specimens while maintaining the possibility for moisture exchange between the material surface and the environment. The system was metrologically validated against a laser interferometer, achieving an expanded instrumental uncertainty of 0.0042 mm (4.2 µm), determined through strict calibration. These results demonstrate that the proposed solution delivers accuracy comparable to conventional sensors but with significantly higher scalability and lower cost, making it highly suitable for extensive long-term experimental programmes. Full article
(This article belongs to the Special Issue Digital Advancements in Civil Engineering and Construction)
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