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Buildings
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Intelligent Automation in Construction Management
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Intelligent Automation in Construction Management

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 December 2025 | Viewed by 124

Special Issue Editors

Prof. Dr. Hyunsu Lim

E-Mail Website
Guest Editor
Department of Architecture, Soonchunhyang University, Asan 31538, Republic of Korea
Interests: intelligent automation; construction robotics; artificial intelligence; site monitoring; advanced construction method
Prof. Dr. Taehoon Kim

E-Mail Website
Guest Editor
Department of Architectural Engineering, Chosun University, Gwangju 61452, Republic of Korea
Interests: construction automation and robotics; advanced construction technology; construction management; data acquisition; decision support
Dr. Chang-Won Kim

E-Mail Website
Guest Editor
New Growth Procurement Research Department, Korea Institute of Procurement, Seoul 06228, Republic of Korea
Interests: performance measurement and management; digital construction; decision-makimg model; advanced construction method; bigdata

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the integration of intelligent automation technologies in the construction management of buildings. As the building sector continues to embrace digital transformation, technologies such as artificial intelligence, robotics, digital twins, and IoT are increasingly being utilized to enhance the efficiency, accuracy, and safety of construction processes. Intelligent automation offers new possibilities for optimizing construction scheduling, resource allocation, site monitoring, and quality control.

We invite original research articles and case studies that explore how these technologies are being integrated into building construction sites to improve management practices and project outcomes. This Special Issue emphasizes innovations that support the real-time control, coordination, and decision-making processes during the construction phase.

Topics of interest include, but are not limited to, the following:

  • Robotics and automation in building construction activities;
  • AI-assisted construction scheduling and resource management;
  • Digital twin applications for construction monitoring and control;
  • IoT-based systems for site safety and progress tracking;
  • Real-time data analytics for construction decision support;
  • Computer vision applications in construction management;
  • Human–automation interaction in building construction sites;
  • Challenges and implementation strategies for automated construction management.

Prof. Dr. Hyunsu Lim
Prof. Dr. Taehoon Kim
Dr. Chang-Won Kim
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

  • intelligent automation
  • construction management
  • artificial intelligence
  • robotics
  • digital twin
  • IoT
  • computer vision
  • site monitoring

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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

32 pages, 1845 KiB  
Article
Enhancing Smart and Zero-Carbon Cities Through a Hybrid CNN-LSTM Algorithm for Sustainable AI-Driven Solar Power Forecasting (SAI-SPF)
by Haytham Elmousalami, Felix Kin Peng Hui and Aljawharah A. Alnaser
Buildings 2025, 15(15), 2785; https://doi.org/10.3390/buildings15152785 - 6 Aug 2025
Abstract
The transition to smart, zero-carbon cities relies on advanced, sustainable energy solutions, with artificial intelligence (AI) playing a crucial role in optimizing renewable energy management. This study evaluates state-of-the-art AI models for solar power forecasting, emphasizing accuracy, reliability, and environmental sustainability. Using operational [...] Read more.
The transition to smart, zero-carbon cities relies on advanced, sustainable energy solutions, with artificial intelligence (AI) playing a crucial role in optimizing renewable energy management. This study evaluates state-of-the-art AI models for solar power forecasting, emphasizing accuracy, reliability, and environmental sustainability. Using operational data from Benban Solar Park in Egypt and Sakaka Solar Power Plant in Saudi Arabia, two of the world’s largest solar installations, the research highlights the effectiveness of hybrid AI techniques. The hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model outperformed other models, achieving a Mean Absolute Percentage Error (MAPE) of 2.04%, Root Mean Square Error (RMSE) of 184, Mean Absolute Error (MAE) of 252, and R2 of 0.99 for Benban, and an MAPE of 2.00%, RMSE of 190, MAE of 255, and R2 of 0.98 for Sakaka. This model excels at capturing complex spatiotemporal patterns in solar data while maintaining low computational CO2 emissions, supporting sustainable AI practices. The findings demonstrate the potential of hybrid AI models to enhance the accuracy and sustainability of solar power forecasting, thereby contributing to efficient, resilient, and zero-carbon urban environments. This research provides valuable insights for policymakers and stakeholders aiming to advance smart energy infrastructure. Full article
(This article belongs to the Special Issue Intelligent Automation in Construction Management)
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