Advances in Smart Construction

A special issue of CivilEng (ISSN 2673-4109).

Deadline for manuscript submissions: 30 June 2025 | Viewed by 156

Special Issue Editors


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Guest Editor
Department of Civil, Construction and Environmental Engineering, North Dakota State University, Fargo, ND 58102, USA
Interests: advanced materials; nanocomposite development; surface engineering; machine learning enriched damage assessment; structural health monitoring; corrosion mitigation
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Guest Editor
Department of Civil, Construction, and Environmental Engineering, North Dakota State University, Fargo, ND 58102, USA
Interests: fiber optic sensing; structural heath monitoring; non-destructive; structural anomaly detection; intelligent corrosion monitoring and resistance; nanocomposite materials; material performance and analysis; thermal spray coatings; genetic algorithm (GA)

Special Issue Information

Dear Colleagues,

The Special Issue entitled “Advances in Smart Construction” aims to explore the latest innovations and developments in the field of smart construction, focusing on the integration of advanced technologies and methodologies to enhance the efficiency, sustainability, and safety of infrastructure constructions. This Issue seeks to gather a collection of high-quality research articles, reviews, and case studies. The scope of this Special Issue encompasses a broad range of topics including, but not limited to, the following:

  • Development and implementation of innovative smart building materials and technologies.
  • Advancements in digital twin technology for construction/infrastructure applications.
  • Utilization of artificial intelligence (AI) and machine learning to optimize construction processes and infrastructure development.
  • Integration of green technologies to enhance environmental sustainability throughout infrastructure development.
  • Deployment of advanced sensors to optimize building operations or monitor structural health.

Contributions may also cover innovative construction techniques for predictive maintenance, as well as the challenges and solutions in adopting these technologies at scale. This Special Issue aims to serve as a platform for researchers, engineers, and industry professionals to disseminate their insights and discoveries in smart construction. We welcome submissions that introduce new frameworks, models, or applications with the potential to significantly influence the construction industry.

Dr. Ying Huang
Dr. Xingyu Wang
Dr. Luyang Xu
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. CivilEng is an international peer-reviewed open access quarterly 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 1200 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

  • smart building materials
  • digital twin
  • artificial intelligence
  • machine learning
  • green technologies
  • sensors

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Published Papers

This special issue is now open for submission.
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