Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.4
3.1
Journals
Buildings
Special Issues
Knowledge Management in the Building and Construction Industry
share announcement

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: 10 December 2026 | Viewed by 7085

Special Issue Editors

Dr. Mirarchi Claudio

E-Mail Website
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
Prof. Dr. Alberto Pavan

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 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. 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 3291 KB  
Article
Integrating Knowledge Management, Project Management, and Human Resource Management for Organisational Resilience in the Construction Industry
by Justin J. Cotter, Fergal O’Brien and Éamonn V. Kelly
Buildings 2026, 16(3), 511; https://doi.org/10.3390/buildings16030511 - 27 Jan 2026
Viewed by 1000
Abstract
Knowledge management (KM) is crucial for organisational success in volatile, uncertain, and ambiguous environments. However persistent operationalization issues hinder its interaction with Project Management (PM) and Human Resource Management (HRM). In construction, skill shortages, demographic shifts, rapid technological breakthroughs, and project complexity disrupt [...] Read more.
Knowledge management (KM) is crucial for organisational success in volatile, uncertain, and ambiguous environments. However persistent operationalization issues hinder its interaction with Project Management (PM) and Human Resource Management (HRM). In construction, skill shortages, demographic shifts, rapid technological breakthroughs, and project complexity disrupt organisational knowledge systems. This study examines the growth of KM in construction research and how its integration with PM and HRM might improve organisational resilience. This staged review included bibliometric analysis and narrative synthesis. A bibliometric mapping of Scopus and Web of Science peer reviewed literature (1998–2024) identified publishing trends and thematic clusters, followed by rigorous screening and narrative synthesis of the final corpus. Analysis showed a considerable growth in KM-related construction research since 2016. A repository-focused strategy is giving way to interconnected, human-centred frameworks that highlight social interaction, governance, and digital capability development. Five literature gaps remain: (1) limited operationalisation of core KM constructs like trust, socialisation, and knowledge transfer; (2) misalignment between KM, PM, and HRM domains; (3) inadequate integration of human-centred knowledge practices with emerging digital technologies; (4) a lack of cross-regional comparative research; and (5) a weak theory–practice bridge for KM implementation in construction organisations. Through gap synthesis, this work provides an organised approach for future research, along with practical advice on KM-PM and HRM integration for organisational resilience. Full article
(This article belongs to the Special Issue Knowledge Management in the Building and Construction Industry)
►▼ Show Figures

Figure 1

31 pages, 706 KB  
Article
Applying Action Research to Developing a GPT-Based Assistant for Construction Cost Code Verification in State-Funded Projects in Vietnam
by Quan T. Nguyen, Thuy-Binh Pham, Hai Phong Bui and Po-Han Chen
Buildings 2026, 16(3), 499; https://doi.org/10.3390/buildings16030499 - 26 Jan 2026
Viewed by 495
Abstract
Cost code verification in state-funded construction projects remains a labor-intensive and error-prone task, particularly given the structural heterogeneity of project estimates and the prevalence of malformed codes, inconsistent units of measurement (UoMs), and locally modified price components. This study evaluates a deterministic GPT-based [...] Read more.
Cost code verification in state-funded construction projects remains a labor-intensive and error-prone task, particularly given the structural heterogeneity of project estimates and the prevalence of malformed codes, inconsistent units of measurement (UoMs), and locally modified price components. This study evaluates a deterministic GPT-based assistant designed to automate Vietnam’s regulatory verification. The assistant was developed and iteratively refined across four Action Research cycles. Also, the system enforces strict rule sequencing and dataset grounding via Python-governed computations. Rather than relying on probabilistic or semantic reasoning, the system performs strictly deterministic checks on code validity, UoM alignment, and unit price conformity in material (MTR), labor (LBR), and machinery (MCR), given the provincial unit price books (UPBs). Deterministic equality is evaluated either on raw numerical values or on values transformed through explicitly declared, rule-governed operations, preserving auditability without introducing tolerance-based or inferential reasoning. A dedicated exact-match mechanism, which is activated only when a code is invalid, enables the recovery of typographical errors only when a project item’s full price vector well matches a normative entry. Using twenty real construction estimates (16,100 rows) and twelve controlled error-injection cases, the study demonstrates that the assistant executes verification steps with high reliability across diverse spreadsheet structures, avoiding ambiguity and maintaining full auditability. Deterministic extraction and normalization routines facilitate robust handling of displaced headers, merged cells, and non-standard labeling, while structured reporting provides line-by-line traceability aligned with professional verification workflows. Practitioner feedback confirms that the system reduces manual tracing effort, improves evaluation consistency, and supports documentation compliance with human judgment. This research contributes a framework for large language model (LLM)-orchestrated verification, demonstrating how Action Research can align AI tools with domain expectations. Furthermore, it establishes a methodology for deploying LLMs in safety-critical and regulation-driven environments. Limitations—including narrow diagnostic scope, unlisted quotation exclusion, single-province UPB compliance, and sensitivity to extreme spreadsheet irregularities—define directions for future deterministic extensions. Overall, the findings illustrate how tightly constrained LLM configurations can augment, rather than replace, professional cost verification practices in public-sector construction. Full article
(This article belongs to the Special Issue Knowledge Management in the Building and Construction Industry)
►▼ Show Figures

Figure 1

34 pages, 4044 KB  
Article
Modular Chain-of-Thought (CoT) for LLM-Based Conceptual Construction Cost Estimation
by Prashnna Ghimire, Kyungki Kim, Terry Stentz and Tirthankar Roy
Buildings 2026, 16(2), 396; https://doi.org/10.3390/buildings16020396 - 18 Jan 2026
Viewed by 1200
Abstract
The traditional cost estimation process in construction involves extracting information from diverse data sources and relying on human intuition and judgment, making it time-intensive and error-prone. While recent advancements in large language models offer opportunities to automate these processes, their effectiveness in cost [...] Read more.
The traditional cost estimation process in construction involves extracting information from diverse data sources and relying on human intuition and judgment, making it time-intensive and error-prone. While recent advancements in large language models offer opportunities to automate these processes, their effectiveness in cost estimation tasks remains underexplored. Prior studies have investigated LLM applications in construction, but there is a lack of studies that have systematically evaluated their performance in cost estimation or proposed a framework for systematic evaluations of their performance in cost estimation and ways to enhance their accuracy and reliability through prompt engineering. This study evaluates the performance of pre-trained LLMs (GPT-4o, LLaMA 3.2, Gemini 2.0, and Claude 3.5 Sonnet) for conceptual cost estimation, comparing zero-shot prompting with a modular chain-of-thought framework. The results indicate that zero-shot prompting produced incomplete responses with an average confidence score of 1.91 (64%), whereas the CoT framework improved accuracy to 2.52 (84%) and achieved significant gains across BLEU, ROUGE-L, METEOR, content overlap, and semantic similarity metrics. The proposed modular CoT framework enhances structured reasoning, contextual alignment, and reliability in estimation workflows. This study contributes by developing a conceptual cost estimation framework for LLMs, benchmarking baseline model performance, and demonstrating how structured prompting improves estimation accuracy. This offers a scalable foundation for integrating AI into construction cost estimation workflows. Full article
(This article belongs to the Special Issue Knowledge Management in the Building and Construction Industry)
►▼ Show Figures

Figure 1

35 pages, 7791 KB  
Article
Inspection Data-Driven Machine Learning Models for Predicting the Remaining Service Life of Deteriorating Bridge Decks
by Gitae Roh, Changsu Shim and Hyunhye Song
Buildings 2025, 15(15), 2799; https://doi.org/10.3390/buildings15152799 - 7 Aug 2025
Cited by 5 | Viewed by 1848
Abstract
The bridge deck is more vulnerable to deterioration than other structural components. This is due to its direct exposure to environmental factors such as vehicular loads, chloride ingress, and freeze–thaw cycles. The resulting accelerated degradation often results in a serviceability life that is [...] Read more.
The bridge deck is more vulnerable to deterioration than other structural components. This is due to its direct exposure to environmental factors such as vehicular loads, chloride ingress, and freeze–thaw cycles. The resulting accelerated degradation often results in a serviceability life that is shorter than the intended design life. However, the absence of standardized condition assessment methods coupled with clear definitions of remaining service life has limited the establishment of rational guidelines for repair and strengthening. In a bid to address this lack, this study focuses on PSC-I type bridges in South Korea, utilizing long-term field inspection data to analyze environmental, structural, and material factors—including reinforcement corrosion, chloride diffusion, and freeze–thaw actions. Environmental zoning was applied based on regional conditions, while structural zoning was performed according to load characteristics, thereby allowing the classification of deck regions into moment zones and cantilever sections. Machine learning models were employed to identify dominant deterioration mechanisms, with the validity of the zoning classification being evaluated via model accuracy and SHAP value analysis. Additionally, a regression-based approach was proposed to estimate the remaining service life of the bridge deck for each corrosion phase, thereby providing a quantitative framework for durability assessment and maintenance planning. Full article
(This article belongs to the Special Issue Knowledge Management in the Building and Construction Industry)
►▼ Show Figures

Figure 1

22 pages, 14848 KB  
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 - 8 Jun 2025
Cited by 3 | Viewed by 1548
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop