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Advanced Studies in Smart Construction
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Advanced Studies in Smart Construction

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

Deadline for manuscript submissions: 30 April 2026 | Viewed by 594

Special Issue Editor

Dr. Changwan Kim

E-Mail Website
Guest Editor
Department of Architectural Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
Interests: construction IT convergence; scan to BIM; scan vs. BIM; construction big data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart construction has emerged as a transformative solution to longstanding challenges in the construction industry, including stagnant productivity, increasing safety incidents, and declining quality. These challenges are compounded by a chronic shortage of skilled labor, driven by the industry's labor-intensive nature and its perception as a "3D job" (dirty, dangerous, and difficult), which deters new talent from entering the field. In this context, smart construction is gaining recognition as a promising alternative to revolutionize the industry.

This Special Issue, titled "Advanced Studies in Smart Construction", invites cutting-edge research and innovative solutions to address pressing issues and opportunities in this rapidly evolving domain. The focus is on integrating advanced technologies—such as artificial intelligence, the Internet of Things (IoT), robotics, and big data—into construction processes. Topics of interest include, but are not limited to, the following:

  • Advancements and applications of smart construction technologies;
  • Automation and robotics in construction;
  • The role of AI and big data in construction decision-making;
  • Enhancing safety on construction sites with smart technologies;
  • Innovations in off-site construction;
  • Workforce transformation in the era of smart construction;
  • Government policies and the role of public institutions in smart construction.

This Special Issue aims to serve as a platform for interdisciplinary research, bringing together engineers, practitioners, and policymakers to bridge the gap between theoretical advancements and practical applications.

Dr. Changwan Kim
Guest Editor

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

  • smart construction
  • automation and robotics
  • ai and big data
  • construction decision-making
  • construction safety
  • off-site construction

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

Published Papers (1 paper)

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Research

24 pages, 6772 KB  
Article
A Closed-Loop Scheduling Framework for Prefabricated Bridge Girders: Bayesian Regression and TCTO-Based Optimization
by Dae Young Kim, Ryang Gyun Kim and Hyun Seok Kwak
Buildings 2025, 15(22), 4168; https://doi.org/10.3390/buildings15224168 - 19 Nov 2025
Viewed by 286
Abstract
Prefabricated construction has emerged as a key strategy to enhance productivity and quality in infrastructure projects. Yet, construction scheduling for prefabricated infrastructure projects often suffers from persistent discrepancies between planned and actual performance due to static assumptions of task durations and fragmented management [...] Read more.
Prefabricated construction has emerged as a key strategy to enhance productivity and quality in infrastructure projects. Yet, construction scheduling for prefabricated infrastructure projects often suffers from persistent discrepancies between planned and actual performance due to static assumptions of task durations and fragmented management methods. To address this challenge, this study proposes a closed-loop framework that integrates probabilistic estimation, prescriptive planning, and performance feedback for prefabricated girder bridge construction. Standard task time (ST) is dynamically modeled using Bayesian regression, which incorporates prior knowledge and updates continuously with new field data. The updated ST distributions are embedded into a time–cost trade-off (TCTO) optimization algorithm to generate resource-constrained schedules. Execution data are captured through an object-based digital logging system, and performance is evaluated using the Schedule Performance Index (SPI). The accumulated results are then used to update the Bayesian model, creating a self-correcting cycle of plan → execution → performance → updating. Using eleven prefabricated girder projects, we standardized task definitions and quantified the plan and actual gaps that motivate the framework. Six projects formed the training set for Bayesian regression to estimate ST with priors; four projects were scheduled with TCTO using the posterior ST, and execution outcomes were compared with the generated plans to validate accuracy, while the collected evidence was used to update the Bayesian model; one final project received the full closed-loop application for comparative assessment of plan versus outcome, with SPI used in the closed-loop evaluation. The deployments improved alignment between plan and actual, narrowed uncertainty in ST over time, and supported credible schedules, real time progress visibility, and resource efficient planning in repetitive prefabrication. From a managerial perspective, the implemented system operationalizes feedback between planning and execution with configurable update cadences such as daily logs, repetitive unit cycles, and project close out. This study provides a validated and extensible template for closed-loop schedule management in prefabricated settings and clarifies the novelty of unifying Bayesian estimation, TCTO optimization, and digital performance feedback in one practical workflow. Full article
(This article belongs to the Special Issue Advanced Studies in Smart Construction)
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