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Buildings
Special Issues
Enhancing Building Resilience Under Climate Change
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Enhancing Building Resilience Under Climate Change

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Energy, Physics, Environment, and Systems".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2796

Special Issue Editors

Dr. Yundan Liao

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Guest Editor
School of Civil Engineering and Transportation, Guangzhou University, Guangzhou 510006, China
Interests: intelligent control; energy system; building performance; energy saving; building environment
Dr. Chengliang Fan

E-Mail Website
Guest Editor
College of Architecture and Urban Planning, Guangzhou University, Guangzhou 510006, China
Interests: heat wave; heat risk; resilient building; building energy; building environment
Special Issues, Collections and Topics in MDPI journals
Dr. Hang Wan

E-Mail
Guest Editor
Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong
Interests: thermal storage; moisture buffering; radiant cooling; intelligent control; building performance

Special Issue Information

Dear Colleagues,

In the face of the ongoing and intensifying impacts of climate change, enhancing the resilience of buildings has become a paramount concern for architects, engineers, policymakers, and communities alike. Climate change, characterized by rising air temperatures, more frequent and intense extreme weather events, and shifting precipitation patterns, poses significant challenges to the safety, durability, and functionality of our built environment. Consequently, there is a growing recognition that building resilience is not just an option but a necessity to ensure the sustainability and livability of our cities and towns.

The benefits of enhancing building resilience are multifaceted. Resilient buildings can reduce the risk of damage and loss during extreme weather events, lowering insurance costs and minimizing disruptions to daily life. They can also contribute to energy savings and environmental sustainability by incorporating efficient systems and materials. Furthermore, resilient buildings can enhance the overall quality of life for occupants by providing safe, comfortable, and healthy indoor environments.

This Special Issue considers a comprehensive range of topics that include the physical structure of buildings, encompassing energy efficiency, building performance simulation, water management, green spaces, and natural-based solutions. By incorporating resilient design principles and innovative technologies, we can create buildings that not only survive but also thrive in the face of climate-related hazards.

Dr. Yundan Liao
Dr. Chengliang Fan
Dr. Hang Wan
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

  • building performance simulation
  • climate change effects
  • building resilience evaluation
  • building optimization
  • built environment
  • heat risk evaluation
  • nature-based solutions
  • building energy

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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

20 pages, 4777 KiB  
Article
Study on the Leakage Diagnosis of a Chilled Water Pipeline Network System Based on Pressure Variation Rate Analysis for Climate Change Mitigation
by Xuan Zhou, Fei Liu, Lisheng Luo, Shiman Peng and Junlong Xie
Buildings 2025, 15(8), 1384; https://doi.org/10.3390/buildings15081384 - 21 Apr 2025
Viewed by 112
Abstract
In the context of increasing climate variability and extreme weather, chilled water systems face mounting challenges due to amplified heating and cooling demands and prevalent pipe leakages. Such leakages reduce system lifespan, raise maintenance costs, and degrade operational efficiency. To overcome the limitations [...] Read more.
In the context of increasing climate variability and extreme weather, chilled water systems face mounting challenges due to amplified heating and cooling demands and prevalent pipe leakages. Such leakages reduce system lifespan, raise maintenance costs, and degrade operational efficiency. To overcome the limitations of current methods, such as insufficient interpretability and computational complexity in leak localization, this paper proposes a novel leakage diagnosis and localization scheme based on pressure variation rate analysis in closed chilled water pipeline networks. Hydraulic models under both normal and leakage conditions are established and experimentally validated. Work conditions under various leakage points and flow rates were simulated, and the results reveal that pressure variation rates systematically increase with the leakage flow rate and vary with the distance from the leakage point. Specifically, when a leakage flow rate reaches 20% of the total rated flow, the pressure variation rate is 12.27% at the water supply side of the leaking branch and 20.27% at the return side. Furthermore, other monitoring points can be categorized into three distinct levels with variation rates ranging from approximately 3.36% to 19.65%. Overall, as the leakage flow increases from 2% to 20% of the design flow, the maximum pressure variation rate rises from 0.411% to 20.27%. A threshold of 3% for this novel leakage diagnosis and localization scheme is used for prompt leakage detection. This scheme not only enhances leak localization accuracy but also contributes to more efficient and reliable system operation under the pressure imposed by climate change. Full article
(This article belongs to the Special Issue Enhancing Building Resilience Under Climate Change)
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16 pages, 1960 KiB  
Article
Multi-Building Energy Forecasting Through Weather-Integrated Temporal Graph Neural Networks
by Samuel Moveh, Emmanuel Alejandro Merchán-Cruz, Maher Abuhussain, Saleh Alhumaid, Khaled Almazam and Yakubu Aminu Dodo
Buildings 2025, 15(5), 808; https://doi.org/10.3390/buildings15050808 - 3 Mar 2025
Viewed by 751
Abstract
While existing building energy prediction methods have advanced significantly, they face fundamental challenges in simultaneously modeling complex spatial–temporal relationships between buildings and integrating dynamic weather patterns, particularly in dense urban environments where building interactions significantly impact energy consumption patterns. This study presents an [...] Read more.
While existing building energy prediction methods have advanced significantly, they face fundamental challenges in simultaneously modeling complex spatial–temporal relationships between buildings and integrating dynamic weather patterns, particularly in dense urban environments where building interactions significantly impact energy consumption patterns. This study presents an advanced deep learning system combining temporal graph neural networks with weather data parameters to enhance prediction accuracy across diverse building types through innovative spatial–temporal modeling. This approach integrates LSTM layers with graph convolutional networks, trained using energy consumption data from 150 commercial buildings over three years. The system incorporates spatial relationships through a weighted adjacency matrix considering building proximity and operational similarities, while weather parameters are integrated via a specialized neural network component. Performance evaluation examined normal operations, data gaps, and seasonal variations. The results demonstrated a 3.2% mean absolute percentage error (MAPE) for 15 min predictions and a 4.2% MAPE for 24 h forecasts. The system showed robust data recovery, maintaining 95.8% effectiveness even with 30% missing values. Seasonal analysis revealed consistent performance across weather conditions (MAPE: 3.1–3.4%). The approach achieved 33.3% better prediction accuracy compared to conventional methods, with 75% efficiency across four GPUs. These findings demonstrate the effectiveness of combining spatial relationships and weather parameters for building energy prediction, providing valuable insights for energy management systems and urban planning. The system’s performance and scalability make it particularly suitable for practical applications in smart building management and urban sustainability. Full article
(This article belongs to the Special Issue Enhancing Building Resilience Under Climate Change)
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18 pages, 246 KiB  
Article
The Patronization of Pluvial Flood Risk and Adaptation Among Tenant-Owned Housing Associations in Sweden
by Mattias Hjerpe, Erik Glaas and Sofie Storbjörk
Buildings 2025, 15(2), 300; https://doi.org/10.3390/buildings15020300 - 20 Jan 2025
Cited by 1 | Viewed by 713
Abstract
Pluvial floods are increasingly affecting urban areas worldwide. Despite growing media attention and clear owner responsibility for reducing climate-related risk for buildings in Swedish national adaptation policy, adaptation action remains slow. Understanding how different property owner categories view and act on flood risks [...] Read more.
Pluvial floods are increasingly affecting urban areas worldwide. Despite growing media attention and clear owner responsibility for reducing climate-related risk for buildings in Swedish national adaptation policy, adaptation action remains slow. Understanding how different property owner categories view and act on flood risks is key for developing better incentive structures and support for accelerating adaptation action. While tenant-owned housing is a common form of housing tenure in Sweden, studies are lacking. This study enhances understanding of pluvial flood risk and adaptation views and actions by tenant-owned housing associations in two Swedish cities. It is based on assessments of 69 apartment buildings within eleven tenant-owned associations and semi-structured interviews with their eleven chairpersons. The study indicates that tenant-owned associations grossly underestimate their flood risks and responsibilities for climate adaptation, even though many buildings studied are at significant risk, and most associations have been impacted by floods, some severely and recurrently. The patronization of flood risk and responsibility for adaptation is attributed to several factors: underestimating risks and consequences, devaluing the benefit of one’s own adaptation actions, lacking knowledge about climate adaptation measures for buildings, and (overly) generous insurance terms. The findings confirm low adaptation action among housing associations, even those with recurring floods, which is concerning given the strong reliance on property-owner adaptation in national adaptation policy. Full article
(This article belongs to the Special Issue Enhancing Building Resilience Under Climate Change)
19 pages, 5592 KiB  
Article
Assessing the Air Humidity Characteristics of Local Climate Zones in Guangzhou, China
by Xiao Tan, Qi Zhang, Yiqi Chen, Junsong Wang, Lihua Zhao and Guang Chen
Buildings 2025, 15(1), 95; https://doi.org/10.3390/buildings15010095 - 30 Dec 2024
Viewed by 785
Abstract
An urban canopy’s humidity significantly affects thermal comfort, public health, and building energy efficiency; however, it remains insufficiently understood. This study employed 3-year (2020–2022) fixed measurements from Guangzhou to investigate the temporal patterns of relative humidity (RH), vapor pressure (Ea), and vapor pressure [...] Read more.
An urban canopy’s humidity significantly affects thermal comfort, public health, and building energy efficiency; however, it remains insufficiently understood. This study employed 3-year (2020–2022) fixed measurements from Guangzhou to investigate the temporal patterns of relative humidity (RH), vapor pressure (Ea), and vapor pressure deficit (VPD) across eight local climatic zones (LCZs). Clear and distinct patterns in the humidity characteristics among the LCZs were revealed on multiple timescales. The RH and VPD of each zone were higher in summer than in winter, with peak RH observed in LCZ A (83.45%) and peak VPD in LCZ 3 (13.6 hPa). Furthermore, a significant daytime urban dry island (UDI) effect in the summer and a nighttime urban moisture island (UMI) effect in the winter were observed in terms of the Ea difference between urban and rural areas. The strongest UMI occurred during winter nights in LCZ 8, with a peak intensity of 0.8 hPa, while the UDI was more frequent during summer days in LCZ 1, with a maximum value of −1.2 hPa; meanwhile, compact areas had a slightly higher frequency of UDI than open areas. Finally, the effects of the urban heat island (UHI) and wind speed (V) on UMI were analyzed. During the daytime, a weak correlation was observed between the UHI and UMI. Wind enhanced the intensity of the nighttime UMI. This research offers further insights into the canopy humidity characteristics in low-latitude subtropical cities, thereby contributing to the establishment of a universal model to quantify the differences in moisture between urban and rural areas. Full article
(This article belongs to the Special Issue Enhancing Building Resilience Under Climate Change)
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