Recent Studies and Innovative Approaches to Sustainable Communities, Buildings, Cities and Infrastructure

Abstract submission deadline

20 March 2026

Manuscript submission deadline

20 May 2026

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Topic Information

Dear Colleagues,

Nations have developed visions and policies on advancing sustainable, liveable cities and resilient communities, addressing critical themes related to Sustainable Development Goals (SDGs) in the built environment. Innovative solutions integrating AI, Digital Twin technology, and IoT-enabled intelligent systems transform urban design, construction, and community resilience as urban spaces evolve. This collection seeks to capture how emerging technologies, innovative methods, and sustainable practices contribute to creating resilient, adaptable spaces. We welcome contributions exploring the following themes:

• Resilient and Liveable Cities: Strategies for creating adaptable, sustainable cities focused on human well-being, climate resilience, and quality of life.
• Innovative Pathways to Sustainable Development Goals: Future initiatives, policies, and technologies driving progress toward achieving the SDGs through urban planning and building design.
• Smart Technologies and AI Applications: Use of AI, Digital Twins, and the IoT in urban planning, building systems, and infrastructure to enhance efficiency, predictive maintenance, and environmental impact reduction.
• Green Buildings and Circular Economy: Advancements in eco-friendly materials, sustainable construction practices, and circular economy principles to minimize waste and reduce carbon footprints.
• Community Engagement and Climate Adaptation: Involving communities in participatory planning, developing climate-resilient infrastructure, and implementing nature-based solutions for sustainable urban development.

This topic collection aims to advance the discourse on sustainable urban development, bridging innovation and sustainability to create intelligent, future-ready environments.

Dr. Samad Sepasgozar
Dr. Sara Shirowzhan
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
Architecture architecture	-	-	2021	36.3 Days	CHF 1000	Submit
Buildings buildings	3.1	3.4	2011	15.3 Days	CHF 2600	Submit
CivilEng civileng	-	2.8	2020	24.4 Days	CHF 1200	Submit
Energies energies	3.0	6.2	2008	16.8 Days	CHF 2600	Submit
Materials materials	3.1	5.8	2008	13.9 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700	Submit
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400	Submit

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Published Papers (5 papers)

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All Applied Sciences Architecture Buildings CivilEng Energies Materials Remote Sensing Sustainability

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21 pages, 4770 KiB  

Open AccessArticle

Prediction of Compressive Strength of Sustainable Concrete Incorporating Waste Glass Powder Using Machine Learning Algorithms

by Sushant Poudel, Bibek Gautam, Utkarsha Bhetuwal, Prabin Kharel, Sudip Khatiwada, Subash Dhital, Suba Sah, Diwakar KC and Yong Je Kim

Sustainability 2025, 17(10), 4624; https://doi.org/10.3390/su17104624 - 18 May 2025

Viewed by 77

Abstract

The incorporation of waste ground glass powder (GGP) in concrete as a partial replacement of cement offers significant environmental benefits, such as reduction in CO₂ emission from cement manufacturing and decrease in the use of colossal landfill space. However, concrete is a [...] Read more.

The incorporation of waste ground glass powder (GGP) in concrete as a partial replacement of cement offers significant environmental benefits, such as reduction in CO₂ emission from cement manufacturing and decrease in the use of colossal landfill space. However, concrete is a heterogeneous material, and the prediction of its accurate compressive strength is challenging due to the inclusion of several non-linear parameters. This study explores the utilization of different machine learning (ML) algorithms: linear regression (LR), ElasticNet regression (ENR), a K-Nearest Neighbor regressor (KNN), a decision tree regressor (DT), a random forest regressor (RF), and a support vector regressor (SVR). A total of 187 sets of pertinent mix design experimental data were collected to train and test the ML algorithms. Concrete mix components such as cement content, coarse and fine aggregates, the water–cement ratio (W/C), various GGP chemical properties, and the curing time were set as input data (X), while the compressive strength was set as the output data (Y). Hyperparameter tuning was carried out to optimize the ML models, and the results were compared with the help of the coefficient of determination (R²) and root mean square error (RMSE). Among the algorithms considered, SVR demonstrates the highest accuracy and predictive capability with an R² value of 0.95 and RMSE of 3.40 MPa. Additionally, all the models exhibit R² values greater than 0.8, suggesting that ML models provide highly accurate and cost-effective means for evaluating and optimizing the compressive strength of GGP-incorporated sustainable concrete. Full article

(This article belongs to the Topic Recent Studies and Innovative Approaches to Sustainable Communities, Buildings, Cities and Infrastructure)

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31 pages, 6301 KiB  

Open AccessArticle

Fostering Resilient Communities Through the Interaction of Heritage, Policy, and Participation: Insights from a Lithuanian Case Study

by Lina Seduikyte, Indre Grazuleviciute-Vileniske, Aušra Mlinkauskienė and Eglė Januškienė

Sustainability 2025, 17(9), 3883; https://doi.org/10.3390/su17093883 - 25 Apr 2025

Viewed by 247

Abstract

This study explores community-driven projects, participatory governance, and adaptive urban policies to enhance social resilience and sustainability in urban environments. By combining a comprehensive literature review, a questionnaire survey, and practical recommendations, it underscores the importance of socioeconomic equality, cultural heritage preservation, and [...] Read more.

This study explores community-driven projects, participatory governance, and adaptive urban policies to enhance social resilience and sustainability in urban environments. By combining a comprehensive literature review, a questionnaire survey, and practical recommendations, it underscores the importance of socioeconomic equality, cultural heritage preservation, and inclusive growth. Through both qualitative and quantitative analyses that incorporate a broad review of scientific literature and a focused survey within Lithuania, this study identifies key strategies for strengthening urban resilience. It highlights the critical role of community engagement in urban planning and the preservation of heritage as a means to maintain local identity and foster civic participation. These elements collectively contribute to more resilient, inclusive, and sustainable urban spaces. Full article

(This article belongs to the Topic Recent Studies and Innovative Approaches to Sustainable Communities, Buildings, Cities and Infrastructure)

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22 pages, 56507 KiB  

Open AccessArticle

Study on the Correlations Between Spatial Morphology Parameters and Solar Potential of Old Communities in Cold Regions with a Case Study of Jinan City, Shandong Province

by Fei Zheng, Peisheng Liu, Zhen Ren, Xianglong Zhang, Yuetao Wang and Haozhi Qin

Buildings 2025, 15(8), 1250; https://doi.org/10.3390/buildings15081250 - 10 Apr 2025

Viewed by 258

Abstract

Currently, urban development has entered the stage of renewal and transformation. Energy transition is an important trend for sustainable urban development, and the assessment of solar energy potential in old residential areas in cold regions is of great significance. This study selects 47 [...] Read more.

Currently, urban development has entered the stage of renewal and transformation. Energy transition is an important trend for sustainable urban development, and the assessment of solar energy potential in old residential areas in cold regions is of great significance. This study selects 47 old residential communities in Jinan, a cold region of China, as case samples. Using clustering algorithms based on spatial form characteristic parameters, the study divides the samples into five categories. The study then uses the Ladybug tool to simulate the distribution and total solar energy utilization potential of buildings in the five categories and analyzes the correlation between eight spatial form parameters and building solar energy potential. A linear regression model is established, and strategies for the application of BIPV in community buildings are proposed. The study finds that factors such as plot ratio, building density, open space ratio, volume-to-surface ratio, and form coefficient have a significant impact on the solar energy potential of residential communities; the p-values are −0.785, −0.783, 0.783, −0.761, and 0.724, respectively. Among these, building density (BD) is the most crucial factor affecting the solar energy potential of building facades. Increasing by one unit can reduce the solar energy utilization potential by 28.00 kWh/m²/y. At the same time, installing photovoltaic panels on old residential buildings in cold regions can reduce building carbon emissions by approximately 48%. The research findings not only provide methodological references for photovoltaic technology application at varying neighborhood scales in urban settings but also offer specific guidance for low-carbon retrofitting of aging urban communities, thereby facilitating progress in urban carbon emission reduction. Full article

(This article belongs to the Topic Recent Studies and Innovative Approaches to Sustainable Communities, Buildings, Cities and Infrastructure)

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30 pages, 1120 KiB  

Open AccessArticle

Soft Skills for Teams in Public Linear Infrastructure: The Development of a Decision Support Tool

by Hollie K. Davies, John J. Posillico and David J. Edwards

Buildings 2025, 15(7), 1197; https://doi.org/10.3390/buildings15071197 - 5 Apr 2025

Viewed by 371

Abstract

Despite the plethora of digital and technological advances made in the construction industry over the past three decades, at its core, the sector remains human-centric. Consequently, this research investigates the core soft skills employed on public linear infrastructure (PLI) projects (during the construction [...] Read more.

Despite the plethora of digital and technological advances made in the construction industry over the past three decades, at its core, the sector remains human-centric. Consequently, this research investigates the core soft skills employed on public linear infrastructure (PLI) projects (during the construction phase) that are digitally enabled and concludes with the development of a decision support tool for PLI project team management. A mixed philosophical stance is implemented using interpretivism, postpositivism and grounded theory together with abductive reasoning to examine subject matter experts’ perceptions of the phenomena under investigation. Textual analysis is then utilised to formulate a decision support tool as a theoretical construct. The research findings demonstrate that communication, leadership and creativity/curiosity are the three main soft skills required of PLI projects. Furthermore, the key elements of a decision support tool—namely, trackable and measurable data, clear objectives and success criteria, and an easy-to-understand visual format—were identified. Such knowledge provides a strong base for building an emotionally intelligent project team. This research constitutes the first attempt to understand the essential soft skills required on PLI projects and, premised upon this, generate a decision support tool for project management in teams that helps to augment project performance through workforce investment via a learning organisation. Full article

(This article belongs to the Topic Recent Studies and Innovative Approaches to Sustainable Communities, Buildings, Cities and Infrastructure)

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28 pages, 2010 KiB  

Open AccessArticle

Challenges and Benefits of Implementing AI in Timber Construction for Circular Economy Goals

by Mohsen Ghobadi and Samad M. E. Sepasgozar

Buildings 2025, 15(7), 1073; https://doi.org/10.3390/buildings15071073 - 26 Mar 2025

Viewed by 470

Abstract

Artificial intelligence (AI) is considered an essential enabler of a circular economy (CE) in the construction industry. AI can significantly enhance the efficiency of applying innovative CE practices in other construction projects. However, it has not yet been fully integrated into the application [...] Read more.

Artificial intelligence (AI) is considered an essential enabler of a circular economy (CE) in the construction industry. AI can significantly enhance the efficiency of applying innovative CE practices in other construction projects. However, it has not yet been fully integrated into the application of CE principles and has explicitly been overlooked in the context of timber construction. This study aims to bridge this gap by examining the potential contributions of AI applications to achieving CE in timber construction, as well as identifying the associated benefits and challenges. Through a mixed-methods approach, the research utilizes both qualitative data, collected through timber construction industry interviews, and quantitative analysis to explore professional construction perspectives and uncover actionable insights. The findings highlight the transformative potential of AI to enhance sustainability and operational efficiency in timber construction. Moreover, six potential benefits and 11 challenges for integrating AI and a CE in timber construction are identified that can act as an accelerator for advancing circularity in timber construction. Based on the results, the reduction in construction waste and facilitating the deconstruction and reuse process emerge as the most important benefits. Data obstacles, technological integration, finance and resources, and organizational and industry are determined as the main challenges. This study makes novel contributions to the field by providing empirical evidence in the form of qualitative and quantitative data, in addition to practical recommendations for advancing the integration of AI to promote CE goals and improve sustainability in the timber construction sector. Full article

(This article belongs to the Topic Recent Studies and Innovative Approaches to Sustainable Communities, Buildings, Cities and Infrastructure)

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