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The Twin Green-Digital Transition in the Built Environment: Multiscale and Interdisciplinary Perspectives

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1293

Special Issue Editors


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Guest Editor
Department of Civil, Building and Environmental Engineering DICEA, Sapienza University of Rome, 00176 Rome, Italy
Interests: digital technologies; BIM/HBIM; IEQ; LCA; urban resilience; building hygiene

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Guest Editor
ISPC, CNR, Institute of Heritage Science, National Research Council, 00010 Montelibretti, Italy
Interests: built heritage digitalization; HBIM; semantic web technologies; digital survey; ontologies

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Guest Editor
Science and Technology in Archaeology (STARC), The Cyprus Institute (CyI), Nicosia, Cyprus
Interests: heritage BIM; BIM-based workflows; BIM to BPS interoperability; retrofitting built heritage; XR in cultural heritage

Special Issue Information

Dear Colleagues,

The digital and green transitions are redefining how sustainability is interpreted and implemented within the Architecture, Engineering, Construction, and Operations (AECO) sector. Aligned with key European policy frameworks—such as the Green Deal, the Renovation Wave, and the broader Twin Transition agenda—emerging strategies move beyond energy efficiency to address resilience, health, and lifecycle-based responsibility. Digital technologies—such as Building Information Modeling (BIM), Heritage BIM (HBIM), Digital Twins, Extended Reality (XR), and Artificial Intelligence (AI)—are enabling integrated, data-driven workflows that support sustainable design, construction, management, and reuse of the built environment.

This Special Issue aims to explore multiscale and interdisciplinary perspectives on the convergence of digitization and sustainability in the built environment. Contributions are encouraged to critically address spatial and temporal scales ranging from district and urban scales to individual buildings and construction systems, and to bridge disciplines such as architecture, engineering, environmental science, and data management. Topics of interest include digital workflows for Life Cycle Assessment (LCA), Indoor Environmental Quality (IEQ), Circular Economy (CE), and climate resilience; as well as the development of interoperable and open-standard platforms that support stakeholder engagement and collaborative decision-making. Special emphasis is placed on methods and applications that promote adaptive reuse, retrofit strategies, social inclusion, cultural heritage valorization, material traceability, and ethical data governance. Key enabling technologies—such as Digital Twins, IoT-based sensing systems, AI-driven decision support tools, and Extended Reality (XR)—are investigated for their potential to foster participatory and context-aware design and operational strategies. The role of Common Data Environments (CDEs) and semantic interoperability is also highlighted, particularly in enhancing cross-disciplinary coordination throughout the entire building lifecycle.

We invite original research articles and reviews covering, but not limited to, the following themes:

  • BIM/HBIM-based workflows for sustainable renovation and adaptive reuse.
  • Digital Twin and AI applications for IEQ and Healthy Buildings.
  • Multi-scale LCA digital integration in design and operations.
  • Data-driven approaches for occupant comfort and energy optimization.
  • Semantic web technologies and ontologies for supporting interoperability.
  • AI and IoT for real-time monitoring of comfort and environmental quality.
  • XR-enhanced workflows for the experiential design and management of the built environment.
  • Open data standards and metadata structuring for lifecycle analysis.
  • Participatory and multi-stakeholder frameworks CDE-based for sustainable practices.
  • Best practices in circular design, resilience strategies, and digital heritage valorization.

We look forward to receiving your contributions that will advance knowledge and practice in shaping intelligent, green, and human-centered built environments.

Dr. Alessandro D'Amico
Dr. Stefano Cursi
Dr. Kristis C. Alexandrou
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. Sustainability 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 2400 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

  • BIM
  • HBIM
  • digital twin
  • XR
  • IoT
  • IEQ
  • LCA
  • circular economy
  • semantic web
  • healthy buildings

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Published Papers (1 paper)

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Research

48 pages, 27526 KB  
Article
Skipping Energy Simulation with S-TCML: A Surrogate Machine Learning Sustainable Framework for Real-Time Thermal Comfort Evaluation in Office Buildings
by Mayar El-Sayed Moeat, Naglaa Ali Megahed, Rehab F. Abdel-Kader and Dina Samy Noaman
Sustainability 2026, 18(7), 3381; https://doi.org/10.3390/su18073381 - 31 Mar 2026
Viewed by 656
Abstract
The digital and green transitions in the AEC sector require rapid, data-driven workflows to redefine sustainability through real-time performance evaluation. However, the high computational cost of traditional energy simulations often lacks evidence-based feedback during early-stage design. This study introduces a surrogate machine learning [...] Read more.
The digital and green transitions in the AEC sector require rapid, data-driven workflows to redefine sustainability through real-time performance evaluation. However, the high computational cost of traditional energy simulations often lacks evidence-based feedback during early-stage design. This study introduces a surrogate machine learning framework (S-TCML) designed to bypass traditional energy simulation by providing an instantaneous assessment of thermal comfort. Using a parametric Grasshopper–Honeybee environment, a dataset of 3072 configurations was generated for an office room in Cairo, Egypt. Six machine learning algorithms were benchmarked, with Gradient Boosting and Random Forest demonstrating superior performance in capturing non-linear thermal physics. Validation against the EnergyPlus engine confirmed that S-TCML models deliver predictions in milliseconds—a 99.9% reduction in computational time. The Gradient Boosting model achieved exceptional accuracy with an R2 of 0.999 and RMSE of 0.013 for PMV and an R2 of 0.995 and RMSE of 0.46% for PPD prediction. Feature importance analysis proved that a tree-based ML model can capture the underlying physical relationship between variables. To bridge the feedback gap, a web-based graphical user interface (GUI) was developed to facilitate proactive design exploration. This framework supports sustainable decision-making and design efficiency, offering scalable, user-friendly tools that protect occupant health and ensure thermal resilience in hot–arid environments. Full article
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