Knowledge Management in the Building and Construction Industry

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 344

Special Issue Editors


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Guest Editor
Department of Architecture, School of Architectural Engineering, Politecnico di Milano, 20133 Milan, Italy
Interests: BIM; knowledge management; digital twins; ICT for construction; digital platforms; standardization; cost management
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, 20133 Milan, Italy
Interests: BIM; knowledge management; digital twins; ICT for construction; digital platforms; standardization; cost management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The activities related to the design, construction, operation and maintenance of buildings and infrastructure generate an enormous variety and quantity of knowledge. Currently, the majority of this knowledge is not capitalized in the building and construction industry due to a number of factors, such as the fragmentation of the industrial chain, the one-of-a-kind products used in the industry, the complexity of the construction site, the difficulties in collecting and reusing data from operational and maintenance activities, etc.

This Special Issue titled “Knowledge Management in the Building and Construction Industry” aims at collecting research results that address the advancement in knowledge management practices in the building and construction industry.

Papers on new theoretical and technological advancements, as well as practical applications and case studies within the area of knowledge management, are invited.

The building and construction industry is increasingly interested in technological developments such as BIM, GIS, machine learning, artificial intelligence, etc. Research works that combine the use of such technologies with the area of knowledge management are especially welcome.

Dr. Mirarchi Claudio
Prof. Dr. Alberto Pavan
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. Buildings 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 2600 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

  • knowledge management
  • machine learning
  • artiticial intelligence
  • building information modeling
  • geographic information system
  • decision making
  • decision support systems

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Published Papers (1 paper)

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Research

22 pages, 14848 KiB  
Article
Digital Twin Framework for Bridge Slab Deterioration: From 2D Inspection Data to Predictive 3D Maintenance Modeling
by Hyunhye Song, Kiyeol Kim, Jihun Shin, Gitae Roh and Changsu Shim
Buildings 2025, 15(12), 1979; https://doi.org/10.3390/buildings15121979 (registering DOI) - 8 Jun 2025
Abstract
Bridge slabs are critical structural components that directly sustain vehicle loads and generally have the shortest service life among bridge elements, leading to increased maintenance needs and costs. In many countries, damage and repair histories have been systematically recorded for over four decades. [...] Read more.
Bridge slabs are critical structural components that directly sustain vehicle loads and generally have the shortest service life among bridge elements, leading to increased maintenance needs and costs. In many countries, damage and repair histories have been systematically recorded for over four decades. In this study, a digital twin framework for predicting the future performance of bridge slabs by integrating long-term inspection data was proposed. Historical 2D damage drawings were digitized using the YOLOv7 deep-learning model to extract the spatial coordinates of the damaged locations. Based on this data, eight representative damage states were defined to support the prediction of the service life. The damage and repair history was embedded into the 3D bridge models using a unique coding system to enable temporal and spatial tracking. As the corrosion of the reinforcement cannot be directly observed by visual inspection, its development and progression is estimated using empirical models. The digital twin concept is validated using historical inspection records to demonstrate its applicability to existing bridge slabs. The integration of cumulative deterioration data significantly improves the accuracy of the performance predictions and facilitates data-driven maintenance and rehabilitation strategies. Full article
(This article belongs to the Special Issue Knowledge Management in the Building and Construction Industry)
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