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Digital Technologies in Buildings and Critical Infrastructure: Transforming Design, Construction,...
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Digital Technologies in Buildings and Critical Infrastructure: Transforming Design, Construction, and Operations

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

Deadline for manuscript submissions: 3 August 2026 | Viewed by 6829

Special Issue Editors

Dr. Issa Ramaji

E-Mail Website
Guest Editor
School of Engineering, Computing and Construction Management, Roger Williams University, Bristol, RI 02809, USA
Interests: digital twin; digital engineering; building operation; building automation systems; IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Soheil Sabri

E-Mail Website
Guest Editor
1. School of Modeling, Simulation and Training, University of Central Florida, Orlando, FL, USA
2. Urban Digital Twin Lab, School of Modeling Simulation and Training, University of Central Florida, 3100 Technology Parkway, Orlando, FL 32826, USA
Interests: urban digital twins; automated building approvals; modeling and simulation; planning support systems; geospatial analytics; rule-based compliance assessment; environmentally sustainable design; GeoAI; AI-ready data center siting and urban integration; human–environment interaction modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to this Special Issue, which focuses on the transformative role of digital technologies in the Architecture, Engineering, Construction, and Operation (AECO) industry. The building industry is undergoing a profound digital revolution driven by advancements that are redefining how projects are designed, executed, and managed. Additionally, it is believed that digital transformation in the AECO industry can address the growing complexity of our critical infrastructure and megaprojects, including transportation hubs (airports, seaports, and real-way stations), data centers, campuses, and power plants.

By integrating cutting-edge tools and emerging knowledge and skills, such as digital twins, artificial intelligence (AI), real-time data analytics, digital engineering, and robotics, these innovations can enhance efficiency, improve collaboration, advance safety, and foster sustainability across all stages of project lifecycles. Nevertheless, we also need to pay attention to the socio-technical aspects through multi-modal and multi-dimensional data acquisition and integration, ensuring preparedness for the adoption of these technologies for their success and mitigating the risks of failure.  

Aim of the Special Issue

This Special Issue will explore how digital technologies are reshaping AEC workflows and enabling smarter, more resilient, and sustainable built environments. It aligns with the journal’s focus on innovation and practical application in construction and engineering disciplines. By gathering groundbreaking research and case studies, this collection seeks to provide a comprehensive understanding of the current state and future directions of digital transformation in the construction industry.

Suggested Themes and Article Types for Submissions

In this Special Issue, we welcome original research articles and review papers for submission. Research areas may include (but are not limited to) the following:

  • Applications of digital twins for real-time monitoring, optimization, and lifecycle management.
  • The role of AI in generative design, risk management, safety, and operational efficiency.
  • The integration of software and hardware in digital engineering for optimized workflows.
  • AI-powered systems for automated code checking, compliance, and quality assurance.
  • Advanced applications of building information modeling (BIM) in planning, coordination, and visualization.
  • Robotics in construction for material handling, automated workflows, and precision tasks.
  • Simulation tools and parametric design for improving constructability and optimization.
  • Multi-disciplinary collaboration platforms for seamless stakeholder communication.
  • Data-driven methods for sustainability and resource management in AEC.

We look forward to receiving your contributions and advancing knowledge in this rapidly evolving field.

Dr. Issa Ramaji
Dr. Soheil Sabri
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

  • digital twin
  • digital engineering
  • Artificial Intelligence (AI)
  • building information modeling (BIM)
  • robotics
  • compliance checking
  • generative design
  • operation and maintenance
  • real-time assessment
  • automation

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

Published Papers (4 papers)

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Research

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30 pages, 4082 KB  
Article
Integrating Traditional Architectural Knowledge with Digital Innovation for Climate-Responsive Construction in Remote Mountain Regions: A Case Study in Neelum Valley, Pakistan
by Adnan Anwar, Shakir Ullah, Yasmeen Ahmed and Rizwan Farooqui
Buildings 2026, 16(7), 1383; https://doi.org/10.3390/buildings16071383 - 1 Apr 2026
Viewed by 631
Abstract
Mountainous areas are prone to extreme climatic conditions, and the lack of modern infrastructure makes it difficult to achieve sustainable construction. To overcome the challenges of thermal comfort, robustness, and post-occupancy performance in hazard zones like the Neelum Valley in Pakistan, this research [...] Read more.
Mountainous areas are prone to extreme climatic conditions, and the lack of modern infrastructure makes it difficult to achieve sustainable construction. To overcome the challenges of thermal comfort, robustness, and post-occupancy performance in hazard zones like the Neelum Valley in Pakistan, this research proposes a Digital–Vernacular Integration Model (DVIM), which integrates traditional architectural expertise with modern digital technology. The research design was based on mixed-methods research with the integration of qualitative information obtained through interviews and household surveys (n = 120), and quantitative measures of indoor thermal environments and hazards-based spatial analysis. Vernacular buildings made of wood, stone, and mud were digitally reconstructed using geometric modeling with SketchUp and Autodesk Revit with building information (BIM)-based modeling for assigning materials’ properties. Simulations were carried out using DesignBuilder software with EnergyPlus engines for assessing thermal environment, snow resistance, and seismic resistance to local hazards. The incorporation of the double-layered wall resulted in the improvement of heat retention by 12 to 15%. Moreover, the optimized roof and walls of the hybrid model resulted in the reduction of the sensible heating demand by 42% when compared to the conventional log houses and nearly 80% when compared to the conventional concrete block houses of the modern era. The proposed hybrid model resulted in R-values ranging from 33 to 40 m2·K/W, which are significantly higher when compared to the R-values for conventional timber walls (R = 15 m2·K/W) and concrete block walls (R = 1.0 to 1.3 m2·K/W). These results show the effectiveness of the digitally optimized hybrid model in improving the thermal performance in severe climatic conditions. The results clearly show that the integration of traditional architecture with digital simulation can ensure that modern comfort and safety standards are met without affecting the cultural identity of the region. The proposed framework will be implemented in pilot projects to ensure that the hybrid architectural models are incorporated into regional building regulations. Full article
(This article belongs to the Special Issue Digital Technologies in Buildings and Critical Infrastructure: Transforming Design, Construction, and Operations)
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27 pages, 6251 KB  
Article
Drift-Free BIM Alignment for Mixed Reality Visualization Through Image Style Transfer and Feature Matching
by Mohamed Zahlan Abdul Muthalif, Davood Shojaei, Kourosh Khoshelham and Debaditya Acharya
Buildings 2026, 16(4), 852; https://doi.org/10.3390/buildings16040852 - 20 Feb 2026
Viewed by 636
Abstract
Accurate localization is a persistent challenge for Mixed Reality (MR) applications in the construction industry, where reliable alignment between digital building models and physical environments is critical. Commercial MR devices such as the Microsoft HoloLens rely on Visual-Inertial Simultaneous Localization and Mapping (VISLAM) [...] Read more.
Accurate localization is a persistent challenge for Mixed Reality (MR) applications in the construction industry, where reliable alignment between digital building models and physical environments is critical. Commercial MR devices such as the Microsoft HoloLens rely on Visual-Inertial Simultaneous Localization and Mapping (VISLAM) for pose estimation, but accumulated drift over extended trajectories and visually ambiguous indoor spaces often reduces localization accuracy. This paper presents a complementary localization refinement methodology that integrates HoloLens spatial tracking with image style transfer and geometry-based pose estimation for Building Information Modeling (BIM)-aligned MR visualization. Image style transfer is used to reduce appearance discrepancies between real-world images and synthetic BIM renderings, improving feature correspondence for geometric alignment. Pose refinement is then applied using feature matching and Perspective-n-Point (PnP) estimation to mitigate accumulated drift when sufficient visual evidence is available. The method is evaluated using 1408 image pairs captured along an indoor trajectory, demonstrating improved BIM alignment, significantly reducing accumulated drift to 1–2 pixels. The proposed approach supports more reliable MR visualization for construction-related tasks such as inspection, coordination, and spatial decision-making. Full article
(This article belongs to the Special Issue Digital Technologies in Buildings and Critical Infrastructure: Transforming Design, Construction, and Operations)
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24 pages, 5485 KB  
Article
Digital Twin-Enabled Framework for Intelligent Monitoring and Anomaly Detection in Multi-Zone Building Systems
by Faeze Hodavand, Issa Ramaji, Naimeh Sadeghi and Sarmad Zandi Goharrizi
Buildings 2025, 15(22), 4030; https://doi.org/10.3390/buildings15224030 - 8 Nov 2025
Cited by 4 | Viewed by 3065
Abstract
The growing complexity of modern building systems requires advanced monitoring frameworks to improve fault detection, energy efficiency, and operational resilience. Digital Twin (DT) technology, which integrates real-time data with virtual models of physical systems, has emerged as a promising enabler for predictive diagnostics. [...] Read more.
The growing complexity of modern building systems requires advanced monitoring frameworks to improve fault detection, energy efficiency, and operational resilience. Digital Twin (DT) technology, which integrates real-time data with virtual models of physical systems, has emerged as a promising enabler for predictive diagnostics. Despite growing interest, key challenges remain, including the neglect of short- and long-term forecasting across different scenarios, insufficiently robust data preparation, and the rare validation of models on multi-zone buildings over extended test periods. To address these gaps, this study presents a comprehensive DT-enabled framework for predictive monitoring and anomaly detection, validated in a multi-zone educational building in Rhode Island, USA, using a full year of operational data for validation. The proposed framework integrates a robust data processing pipeline and a comparative analysis of machine learning models, including LSTM, RNN, GRU, ANN, XGBoost, and RF, to forecast short-term (1 h) and long-term (24 h) indoor temperature variations. The LSTM model consistently outperformed other methods, achieving R2 > 0.98 and RMSE < 0.55 °C for all tested rooms. For real-time anomaly detection, we applied the hybrid LSTM–Interquartile Range (IQR) method on one-step-ahead residuals, which successfully identified anomalous deviations from expected patterns. The model’s predictions remained within a ±1 °C error margin for over 90% of the test data, providing reliable forecasting up to 16 h ahead. This study contributes a validated, generalizable DT methodology that addresses key research gaps, offering practical tools for predictive maintenance and operational optimization in complex building environments. Full article
(This article belongs to the Special Issue Digital Technologies in Buildings and Critical Infrastructure: Transforming Design, Construction, and Operations)
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Review

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35 pages, 2881 KB  
Review
Systematic Mapping of Artificial Intelligence Applications in Finite-Element-Based Structural Engineering
by Villem Vaktskjold, Lars Olav Toppe, Marcin Luczkowski, Anders Rønnquist and David Morin
Buildings 2026, 16(3), 644; https://doi.org/10.3390/buildings16030644 - 3 Feb 2026
Viewed by 1475
Abstract
This study systematically maps how artificial intelligence (AI) has been applied within finite-element (FE)-based structural engineering. A corpus of 5995 unique English-language publications was compiled and classified by discipline, with 3345 relevant papers further categorized by application group. A representative subset of 372 [...] Read more.
This study systematically maps how artificial intelligence (AI) has been applied within finite-element (FE)-based structural engineering. A corpus of 5995 unique English-language publications was compiled and classified by discipline, with 3345 relevant papers further categorized by application group. A representative subset of 372 studies underwent detailed full-text classification across seven analytical dimensions covering AI methods, element formulations, materials, and structural objects. The analysis reveals rapid growth after 2015, including a pronounced expansion of surrogate modeling and data-driven prediction methods. The disciplinary composition of the literature has also evolved, with structural engineering studies becoming more prominent in recent years relative to earlier decades. Optimization & Design remains the largest application area across the full dataset, while Structural Performance Prediction and FEM Acceleration/Surrogate Modeling show the fastest growth, reflecting increasing emphasis on predictive, solver-efficient, and hybrid physics–data approaches. These findings indicate a maturing field in which AI is increasingly embedded across all stages of FE-based analysis and design. This study provides a structured overview of methodological patterns, identifies emerging hybrid strategies, and highlights opportunities for future research and industrial integration. Full article
(This article belongs to the Special Issue Digital Technologies in Buildings and Critical Infrastructure: Transforming Design, Construction, and Operations)
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