Digital Twins and Artificial Intelligence for Advancing Smart Green Building and City Resilience

Topic Information

Dear Colleagues,

The pace of climate change is accelerating beyond previous scientific predictions, and its impacts on communities are vast and often unimaginable. This urgency underscores the critical need to transform our buildings and cities into smart, green, and resilient environments. Fortunately, the rapid evolution of emerging digital technologies offers unprecedented opportunities to mitigate climate change impacts and enhance the resilience of urban infrastructure. Among these technologies, Digital Twins and Artificial Intelligence (AI) stand out as powerful tools to make cities smarter, greener, and more adaptable to the challenges posed by climate change.

Despite their potential, research on the applications of Digital Twins and AI in addressing climate change remains limited. Much of the existing work focuses on technologies that are inadequate in addressing the speed and scale of climate change impacts. This research aims to explore how Digital Twins and AI can be effectively integrated throughout the lifecycle of urban infrastructure—including construction, operation, maintenance, and renewal—to create environments that are not only technologically advanced but also environmentally sustainable and resilient to climate-related challenges.

The primary goal of this research is to foster interdisciplinary dialogue that generates actionable insights for designing and implementing resilient, sustainable, and technologically innovative cities of the future. Contributions are welcome in the form of original research articles, review articles, case studies, and conceptual pieces investigating the applications of Digital Twins and AI across various aspects of urban infrastructure. The ultimate aim is to advance knowledge and practice in making cities greener, smarter, and more resilient to the impacts of climate change.

Dr. Henry Abanda
Dr. Michel Mbessa
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Buildings buildings	3.4	5.6	2011	15.1 Days	CHF 2600	Submit
CivilEng civileng	2.8	4.4	2020	21.7 Days	CHF 1400	Submit
Infrastructures infrastructures	3.6	5.7	2016	18.3 Days	CHF 1800	Submit
Smart Cities smartcities	6.6	13.0	2018	25.2 Days	CHF 2000	Submit
Sustainability sustainability	4.1	8.9	2009	17.9 Days	CHF 2400	Submit
Technologies technologies	5.2	6.7	2013	19.1 Days	CHF 1800	Submit

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Published Papers (8 papers)

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All Buildings Smart Cities Sustainability CivilEng Infrastructures Technologies

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19 pages, 18377 KB  

Open AccessArticle

Prediction of the Vertical Bearing Capacity of Piles in Cold Saline Environments by a Multi-Dimensional Machine Learning Approach

by Yuhan Jia and Zhaochao Li

Buildings 2026, 16(10), 2042; https://doi.org/10.3390/buildings16102042 - 21 May 2026

Viewed by 292

Abstract

This study proposes an innovative methodology that integrates finite element simulation, machine learning, and interpretable model analysis to predict the vertical bearing capacity of piles in cold saline environments. Initially, Python scripts are developed to drive the ABAQUS platform, and LHS (Latin Hypercube [...] Read more.

This study proposes an innovative methodology that integrates finite element simulation, machine learning, and interpretable model analysis to predict the vertical bearing capacity of piles in cold saline environments. Initially, Python scripts are developed to drive the ABAQUS platform, and LHS (Latin Hypercube Sampling) is employed to generate random parameter combinations to construct a multi-dimensional ML (machine learning) database. Six ML models, including XGBoost and LightGBM, are developed with hyperparameters optimized by cross-validation and grid search. Model performance is evaluated by five metrics (R², MSE, RMSE, MAE, and MAPE). Finally, parametric sensitivity is analyzed by the SHAP (SHapley Additive exPlanations) method. The study demonstrates that: (1) the XGBoost and LightGBM models achieve optimal performance on the test set, and the generalization ability significantly exceeds other models; (2) pile diameter is the primary factor influencing vertical bearing capacity, and corrosion depth exhibits higher sensitivity than corrosion thickness; and (3) the bearing capacity of the pile is predicted by using the automated parametric modeling method based on Python (3.8)-ABAQUS (2022). The automated modeling and prediction framework may serve as a reference for pile design in similarly complex environments. Full article

(This article belongs to the Topic Digital Twins and Artificial Intelligence for Advancing Smart Green Building and City Resilience)

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24 pages, 6838 KB  

Open AccessArticle

Governing Urban AI from the Frontline: A Stage-Gate Framework for Municipal Algorithmic Decision-Making

by Tan Yigitcanlar, Anne David, Raveena Marasinghe, Sajani Senadheera, Tahsin Hossain, Xinyue Ye and Araz Taeihagh

Smart Cities 2026, 9(5), 81; https://doi.org/10.3390/smartcities9050081 - 8 May 2026

Viewed by 1456

Abstract

Artificial intelligence (AI) is increasingly embedded in how cities are governed, shaping decisions on mobility, land use, public services, and environmental management. Yet urban AI is predominantly governed through fragmented frameworks designed at national or corporate scales, offering limited guidance for municipal decision-making [...] Read more.

Artificial intelligence (AI) is increasingly embedded in how cities are governed, shaping decisions on mobility, land use, public services, and environmental management. Yet urban AI is predominantly governed through fragmented frameworks designed at national or corporate scales, offering limited guidance for municipal decision-making and overlooking place-specific social and ecological consequences. As the level of government closest to everyday urban life, cities are uniquely positioned to steer AI toward public value, but face persistent tensions between efficiency, equity, accountability, and sustainability. This paper argues that responsible urban AI cannot be governed through top-down or one-size-fits-all approaches. To address this, the study aims to conceptualise and advance a ground-up model of responsible urban AI governance that places cities and local governments at the centre of decision-making. It addresses the following research question: How can municipal authorities translate high-level ethical principles into practical, context-sensitive governance arrangements that respond to local capacities, risks, and public values? Drawing on global governance principles and illustrative city experiences, we propose a locally grounded, stage-based framework for municipal AI governance. The framework addresses institutional capacity gaps, fragmented responsibilities, and algorithmic externalities, advancing a participatory, place-sensitive, and adaptive model that aligns urban AI innovation with democratic legitimacy, social justice, and sustainable urban futures. Full article

(This article belongs to the Topic Digital Twins and Artificial Intelligence for Advancing Smart Green Building and City Resilience)

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20 pages, 1917 KB  

Open AccessArticle

EvoDeep-Quality: A Closed-Loop Hybrid Framework Integrating CNN-LSTM and NSGA-III for Adaptive Quality Optimization in Smart Manufacturing

by Shaymaa E. Sorour and Ahmed E. Amin

Sustainability 2026, 18(8), 3679; https://doi.org/10.3390/su18083679 - 8 Apr 2026

Viewed by 477

Abstract

This study proposes EvoDeep-Quality, a closed-loop hybrid framework integrating deep learning-based perception with multi-objective evolutionary optimization for adaptive quality control in smart manufacturing. The architecture combines a CNN-LSTM network for real-time spatiotemporal quality prediction with an NSGA-III-based optimization unit to balance conflicting objectives [...] Read more.

This study proposes EvoDeep-Quality, a closed-loop hybrid framework integrating deep learning-based perception with multi-objective evolutionary optimization for adaptive quality control in smart manufacturing. The architecture combines a CNN-LSTM network for real-time spatiotemporal quality prediction with an NSGA-III-based optimization unit to balance conflicting objectives of quality, cost, and energy efficiency. A continuous adaptive learning loop addresses concept drift and process variability. Evaluated on an industrial-inspired synthetic dataset of textile blends (N = 5000) and validated on the real-world SECOM semiconductor manufacturing dataset, the framework demonstrates strong predictive capability (R² = 0.947 ± 0.012, MAE = 0.035 ± 0.003) and significant manufacturing performance improvements, including a 23.5% quality enhancement and an 8.7–12.3% operational cost reduction compared to traditional and standalone AI models. Statistical significance testing (paired t-test, p < 0.01) confirms the superiority of the proposed approach. This deep-evolutionary framework advances proactive quality assurance and adaptive process control, offering a scalable solution aligned with Industry 4.0 and 5.0 paradigms. Full article

(This article belongs to the Topic Digital Twins and Artificial Intelligence for Advancing Smart Green Building and City Resilience)

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19 pages, 4721 KB  

Open AccessArticle

Prediction of Building Carbon Emissions in Campus Areas Based on Building a Carbon Emission Correlation Factor

by Jingjing Wang, Mingzhu Xiu, Bo Zhao and Li Song

Smart Cities 2026, 9(3), 47; https://doi.org/10.3390/smartcities9030047 - 4 Mar 2026

Viewed by 590

Abstract

This study introduces a new method for predicting carbon emissions from campus buildings, which is crucial to achieving low-carbon campuses in higher education and meeting “Carbon Peaking and Carbon Neutrality Goals”. The method begins with manually classifying buildings and introducing a carbon emission [...] Read more.

This study introduces a new method for predicting carbon emissions from campus buildings, which is crucial to achieving low-carbon campuses in higher education and meeting “Carbon Peaking and Carbon Neutrality Goals”. The method begins with manually classifying buildings and introducing a carbon emission correlation factor, linking each building type’s emissions to the total category emissions. Using this factor, three models—Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM), and Random Forest (RF)—were developed to predict emissions. The results show improved accuracy after adding the correlation factor: 17.23%, 6.159%, and 3.949% for the SARIMA model in Categories A, B, and C, respectively; 2.76%, 12.636%, and 3.370% for LSTM; and 3.61%, 10.893%, and 4.776% for Random Forest. These results demonstrate the value of using carbon emission correlation factors to improve prediction accuracy and promote sustainable campus development. Full article

(This article belongs to the Topic Digital Twins and Artificial Intelligence for Advancing Smart Green Building and City Resilience)

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16 pages, 5636 KB  

Open AccessArticle

Co-Creating Climate-Resilient Streets: Digital Twin-Based Simulations for Outdoor Thermal Comfort

by Koldo Urrutia-Azcona, Valentina Bonetti, Mohammad Mizanur, Nele Janssen, Niall Buckley, Mark De Wit, Kieran Murray and Niall Byrne

Smart Cities 2026, 9(2), 39; https://doi.org/10.3390/smartcities9020039 - 22 Feb 2026

Cited by 1 | Viewed by 1322

Abstract

Rapid urbanization and climate change are intensifying heat exposure in cities, making effective adaptation strategies essential. This study presents a streamlined digital twin modeling framework for simulating the impact of nature-based solutions (NBSs) on outdoor thermal comfort, developed within the Intelligent Communities Lifecycle [...] Read more.

Rapid urbanization and climate change are intensifying heat exposure in cities, making effective adaptation strategies essential. This study presents a streamlined digital twin modeling framework for simulating the impact of nature-based solutions (NBSs) on outdoor thermal comfort, developed within the Intelligent Communities Lifecycle (ICL) software suite. The approach automates the import of urban geometry from OpenStreetMap and integrates geolocated weather data, enabling users to efficiently test scenarios involving NBSs and surface material modifications. Outdoor thermal comfort is quantified using the Universal Thermal Climate Index (UTCI), with results visualized through an interactive cloud-based 3D platform to support participatory urban planning. The methodology is demonstrated in Meunierstraat, Leuven (Belgium), where three planning alternatives are compared across seasonal extremes. Simulations show that targeted NBS interventions, particularly temporary participatory measures, can improve thermal comfort under extreme heat. However, the benefits are seasonally dependent and spatially heterogeneous, emphasizing the value of high-resolution, scenario-based analysis. This integrated workflow enhances both technical evidence and stakeholder engagement. While the tool is capable of linking outdoor comfort improvements with building energy performance and carbon emissions, the present paper focuses solely on the outdoor thermal comfort results, leaving indoor–outdoor coupling analysis as a direction for future work. Full article

(This article belongs to the Topic Digital Twins and Artificial Intelligence for Advancing Smart Green Building and City Resilience)

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39 pages, 6883 KB  

Open AccessArticle

SYNTHUA-DT: A Methodological Framework for Synthetic Dataset Generation and Automatic Annotation from Digital Twins in Urban Accessibility Applications

by Santiago Felipe Luna Romero, Mauren Abreu de Souza and Luis Serpa Andrade

Technologies 2025, 13(8), 359; https://doi.org/10.3390/technologies13080359 - 14 Aug 2025

Cited by 2 | Viewed by 2029

Abstract

Urban scene understanding for inclusive smart cities remains challenged by the scarcity of training data capturing people with mobility impairments. We propose SYNTHUA-DT, a novel methodological framework that integrates unmanned aerial vehicle (UAV) photogrammetry, 3D digital twin modeling, and high-fidelity simulation in Unreal [...] Read more.

Urban scene understanding for inclusive smart cities remains challenged by the scarcity of training data capturing people with mobility impairments. We propose SYNTHUA-DT, a novel methodological framework that integrates unmanned aerial vehicle (UAV) photogrammetry, 3D digital twin modeling, and high-fidelity simulation in Unreal Engine to generate annotated synthetic datasets for urban accessibility applications. This framework produces photo-realistic images with automatic pixel-perfect segmentation labels, dramatically reducing the need for manual annotation. Focusing on the detection of individuals using mobility aids (e.g., wheelchairs) in complex urban environments, SYNTHUA-DT is designed as a generalized, replicable pipeline adaptable to different cities and scenarios. The novelty lies in combining real-city digital twins with procedurally placed virtual agents, enabling diverse viewpoints and scenarios that are impractical to capture in real life. The computational efficiency and scale of this synthetic data generation offer significant advantages over conventional datasets (such as Cityscapes or KITTI), which are limited in accessibility-related content and costly to annotate. A case study using a digital twin of Curitiba, Brazil, validates the framework’s real-world applicability: 22,412 labeled images were synthesized to train and evaluate vision models for mobility aids user detection. The results demonstrate improved recognition performance and robustness, highlighting SYNTHUA-DT’s potential to advance urban accessibility by providing abundant, bias-mitigating training data. This work paves the way for inclusive computer vision systems in smart cities through a rigorously engineered synthetic data pipeline. Full article

(This article belongs to the Topic Digital Twins and Artificial Intelligence for Advancing Smart Green Building and City Resilience)

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29 pages, 1306 KB  

Open AccessReview

Artificial Vision Systems for Mobility Impairment Detection: Integrating Synthetic Data, Ethical Considerations, and Real-World Applications

by Santiago Felipe Luna-Romero, Mauren Abreu de Souza and Luis Serpa Andrade

Technologies 2025, 13(5), 198; https://doi.org/10.3390/technologies13050198 - 13 May 2025

Cited by 2 | Viewed by 3722

Abstract

Global estimates suggest that over a billion people worldwide—more than 15% of the global population—live with some form of mobility disability, underscoring the pressing need for innovative technological solutions. Recent advancements in artificial vision systems, driven by deep learning and image processing techniques, [...] Read more.

Global estimates suggest that over a billion people worldwide—more than 15% of the global population—live with some form of mobility disability, underscoring the pressing need for innovative technological solutions. Recent advancements in artificial vision systems, driven by deep learning and image processing techniques, offer promising avenues for detecting mobility aids and monitoring gait or posture anomalies. This paper presents a systematic review conducted in accordance with ProKnow-C guidelines, examining key methodologies, datasets, and ethical considerations in mobility impairment detection from 2015 to 2025. Our analysis reveals that convolutional neural network (CNN) approaches, such as YOLO and Faster R-CNN, frequently outperform traditional computer vision methods in accuracy and real-time efficiency, though their success depends on the availability of large, high-quality datasets that capture real-world variability. While synthetic data generation helps mitigate dataset limitations, models trained predominantly on simulated images often exhibit reduced performance in uncontrolled environments due to the domain gap. Moreover, ethical and privacy concerns related to the handling of sensitive visual data remain insufficiently addressed, highlighting the need for robust privacy safeguards, transparent data governance, and effective bias mitigation protocols. Overall, this review emphasizes the potential of artificial vision systems to transform assistive technologies for mobility impairments and calls for multidisciplinary efforts to ensure these systems are technically robust, ethically sound, and widely adoptable. Full article

(This article belongs to the Topic Digital Twins and Artificial Intelligence for Advancing Smart Green Building and City Resilience)

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25 pages, 4009 KB  

Open AccessArticle

Implementing Building Information Modeling to Enhance Smart Airport Facility Management: An AHP-SWOT Approach

by Amirhossein Javaherikhah and Hadi Sarvari

CivilEng 2025, 6(1), 15; https://doi.org/10.3390/civileng6010015 - 18 Mar 2025

Cited by 5 | Viewed by 4631

Abstract

Airport facility management requires innovative and coordinated techniques due to the infrastructure’s complexity, stakeholders’ diversity, and the necessity of safety. Adopting building information management (BIM) as an advanced technology has several benefits, including increased productivity, lower cost, and higher quality of service. This [...] Read more.

Airport facility management requires innovative and coordinated techniques due to the infrastructure’s complexity, stakeholders’ diversity, and the necessity of safety. Adopting building information management (BIM) as an advanced technology has several benefits, including increased productivity, lower cost, and higher quality of service. This study seeks to determine the strategies for using BIM in airport facility management. In this vein, two questionnaires were developed to collect data based on a literature review. The first questionnaire was used to collect data for identifying and ranking the main criteria, and the second questionnaire was used to identify the practical strategies. The experts of this study answered five strengths, four weaknesses, five opportunities, and five threats using a standardized questionnaire. An integrated AHP-SWOT approach was used to identify and examine the practical strategies. Furthermore, a sensitivity analysis was used to ensure the results were correct. The findings showed that smart maintenance management, with a weight of 0.363, was the most important strength in the SWOT analysis. Resistance to change was the most important weakness, with a weight of 0.455. The increasing need for smart airports with a weight of 0.358 was the most important opportunity, while cybersecurity issues with a weight of 0.385 were the most important threat. Integrating BIM into the aviation sector can enhance efficiency and sustainability in airport facility management while addressing potential opportunities and shared hazards that extend beyond airport operations. Full article

(This article belongs to the Topic Digital Twins and Artificial Intelligence for Advancing Smart Green Building and City Resilience)

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