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Buildings
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Advancements in Adaptive, Inclusive, and Responsive Buildings
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Advancements in Adaptive, Inclusive, and Responsive Buildings

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Architectural Design, Urban Science, and Real Estate".

Deadline for manuscript submissions: closed (2 April 2024) | Viewed by 2810

Special Issue Editors

Dr. Szu-Cheng Chien

E-Mail Website
Guest Editor
Engineering Cluster, Singapore Institute of Technology, 10 Dover Drive, Singapore 138683, Singapore
Interests: high performance building design and diagnostics; BIM-based management system; human-computer interaction; human-centric lighting
Special Issues, Collections and Topics in MDPI journals
Dr. Aung Myat

E-Mail Website
Guest Editor
Department of Engineering and Technology, Southeast Missouri State University, Cape Girardeau, MO 63701, USA
Interests: building energy management; combined heat and power (thermal components); optimizing HVAC system; desalination; HVAC system for indoor farming; waste heat recovery from power plants; global warming and GHGs emissions; passive cooling system; hybrid cooling; Artificial Intelligence algorithm for energy efficiency and SMART control for the built environment
Special Issues, Collections and Topics in MDPI journals
Dr. Tzen-Ying Ling

E-Mail Website
Guest Editor
Department of Architecture, Tamkang University, New Taipei City 25137, Taiwan
Interests: urban resilience; healthy design and architecture design thinking; GIS simulations; adaptive design and resilient habitat; urban green-scape

Special Issue Information

Dear Colleagues,

This Special Issue, "Advancements in Adaptive, Inclusive, and Responsive Buildings," seeks to showcase the latest research and developments in the design, technology, and performance of buildings that emphasize adaptability, inclusivity, and responsiveness to accommodate diverse occupants and changing conditions. This Issue will explore a broad range of topics, including innovative building systems and designs tailored to the needs of the occupants, ranging from individuals with typical abilities to those with special needs, including the elderly, low-vision individuals, children, pregnant women, and hospital patients, as well as strategies for creating inclusive and accessible environments.

In addition, the Issue will investigate advances in high-performance building operation and management, district energy supply and demand optimization, and the reduction of infection rates within the built environment, drawing on lessons learned from the COVID-19 pandemic. It will also address the challenges and opportunities in balancing occupant comfort, energy efficiency, and carbon emission reduction, highlighting emerging approaches that enhance user experience while mitigating environmental impacts.

This collection of articles will bring together interdisciplinary perspectives from architects, engineers, urban planners, and researchers, showcasing insights and best practices for developing adaptive, inclusive, and responsive buildings. Through these advancements, this Special Issue aims to inspire the design, operation and maintenance of future buildings that can cater to the diverse needs of their occupants while promoting sustainability, health, and comfort in our rapidly evolving urban landscapes.

We welcome the submission of original research papers focusing on this field.

We look forward to receiving your contributions.

Dr. Szu-Cheng Chien
Dr. Aung Myat
Dr. Tzen-Ying Ling
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 monthly 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

  • adaptive buildings
  • inclusive design
  • responsive architecture
  • special needs
  • sustainability
  • high-performance buildings
  • accessibility
  • occupant comfort
  • energy efficiency
  • universal design

Published Papers (4 papers)

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Research

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27 pages, 12719 KiB  
Article
Optimizing the Shading Device Configuration of Kinetic Façades through Daylighting Performance Assessment
by Dong-Hyun Kim, Hieu Trung Luong and Trang Thao Nguyen
Buildings 2024, 14(4), 1038; https://doi.org/10.3390/buildings14041038 - 08 Apr 2024
Viewed by 342
Abstract
When designing a façade, it is essential to consider the impact of daylight and how it can be optimized through external movable shading devices. To accurately evaluate the lighting performance of a kinetic facade, it is crucial to consider the operation of these [...] Read more.
When designing a façade, it is essential to consider the impact of daylight and how it can be optimized through external movable shading devices. To accurately evaluate the lighting performance of a kinetic facade, it is crucial to consider the operation of these shading devices, as they can significantly impact performance. This study proposes a high-precision methodology that utilizes digital tools and hourly data to examine the effectiveness of dynamic shading device systems in enhancing daylight performance and optimizing shading configurations using the Genetic Optimization algorithm. The study’s results demonstrate that the proposed methodology is accurate and effective, showing that the optimal operation scenario can exceed LEED v4.1 requirements while meeting daylight availability standards. Designers can achieve optimal performance by adjusting each parameter for a lighting energy-conserving kinetic façade. The limitations and applicability of this method are also discussed. Full article
(This article belongs to the Special Issue Advancements in Adaptive, Inclusive, and Responsive Buildings)
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23 pages, 2031 KiB  
Article
Kano Model for Apartment-Unit Specialized Planning Guidelines to Prevent Infectious Diseases
by Seung-Ju Han, Eun-Jeong Kim and Mi-Kyung Kim
Buildings 2024, 14(3), 606; https://doi.org/10.3390/buildings14030606 - 25 Feb 2024
Viewed by 509
Abstract
Owing to the continued occurrence of infectious diseases, proactive prevention and management plans are required. This study aimed to develop design guidelines to effectively respond to infectious diseases based on the needs of apartment residents, which focused on a South Korean setting. The [...] Read more.
Owing to the continued occurrence of infectious diseases, proactive prevention and management plans are required. This study aimed to develop design guidelines to effectively respond to infectious diseases based on the needs of apartment residents, which focused on a South Korean setting. The research method included a literature review to identify apartment planning concepts for preventing and managing infectious diseases, a survey of 300 participants using the Kano model, and an analysis of the quality attributes (QAs) of the survey results to prioritize design guidelines. After reviewing 20 studies, 65 items related to apartment-unit planning for infectious disease prevention, including 108 keywords, were identified. Using thematic analysis, the keywords converged into three planning concepts: hygiene, convenience, and comfort. Based on the literature review, 27 survey questions were derived, and a Kano model QA analysis was performed. As a result, 17 attractive QAs, two one-dimensional QAs, seven indifferent QAs, and one reverse QA were identified. Among these, 13 items that had a significant impact on residents’ satisfaction were classified as essential requirements, and the remaining 14 items were classified as recommended design guidelines. The results of this study provide insights into an evidence-based framework for complex building design guidelines to prevent the spread of infectious diseases. Full article
(This article belongs to the Special Issue Advancements in Adaptive, Inclusive, and Responsive Buildings)
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21 pages, 2951 KiB  
Article
Enhancing Day-Ahead Cooling Load Prediction in Tropical Commercial Buildings Using Advanced Deep Learning Models: A Case Study in Singapore
by Namitha Kondath, Aung Myat, Yong Loke Soh, Whye Loon Tung, Khoo Aik Min Eugene and Hui An
Buildings 2024, 14(2), 397; https://doi.org/10.3390/buildings14020397 - 01 Feb 2024
Viewed by 528
Abstract
Commercial buildings in hot and humid tropical climates rely significantly on cooling systems to maintain optimal occupant comfort. A well-accurate day-ahead load profile prediction plays a pivotal role in planning the energy requirements of cooling systems. Despite the pressing need for effective day-ahead [...] Read more.
Commercial buildings in hot and humid tropical climates rely significantly on cooling systems to maintain optimal occupant comfort. A well-accurate day-ahead load profile prediction plays a pivotal role in planning the energy requirements of cooling systems. Despite the pressing need for effective day-ahead cooling load predictions, current methodologies have not fully harnessed the potential of advanced deep-learning techniques. This paper aims to address this gap by investigating the application of innovative deep-learning models in day-ahead hourly cooling load prediction for commercial buildings in tropical climates. A range of multi-output deep learning techniques, including Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory networks (LSTMs), are employed to enhance prediction accuracy. Furthermore, these individual deep learning techniques are synergistically integrated to create hybrid models, such as CNN-LSTM and Sequence-to-Sequence models. Experiments are conducted to choose the time horizons from the past that can serve as input to the models. In addition, the influence of various categories of input parameters on prediction performance has been assessed. Historical cooling load, calendar features, and outdoor weather parameters are found in decreasing order of influence on prediction accuracy. This research focuses on buildings located in Singapore and presents a comprehensive case study to validate the proposed models and methodologies. The sequence-to-sequence model provided better performance than all the other models. It offered a CV-RMSE of 7.4%, 10%, and 6% for SIT@Dover, SIT@NYP, and the simulated datasets, which were 2.3%, 3%, and 1% less, respectively, than the base Deep Neural Network model. Full article
(This article belongs to the Special Issue Advancements in Adaptive, Inclusive, and Responsive Buildings)
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Review

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34 pages, 2994 KiB  
Review
A Systematic Review of Climate Change Implications on Building Energy Consumption: Impacts and Adaptation Measures in Hot Urban Desert Climates
by Najeeba Abdulla Kutty, Dua Barakat, Abeer Othman Darsaleh and Young Ki Kim
Buildings 2024, 14(1), 13; https://doi.org/10.3390/buildings14010013 - 20 Dec 2023
Cited by 1 | Viewed by 942
Abstract
The climate change–built environment nexus is complex and intertwined. Recognizing the rising air temperatures and solar radiations owing to climate-induced global warming, it is critical to manage the increased building energy and cooling loads in the Middle East Gulf states’ hot desert climates [...] Read more.
The climate change–built environment nexus is complex and intertwined. Recognizing the rising air temperatures and solar radiations owing to climate-induced global warming, it is critical to manage the increased building energy and cooling loads in the Middle East Gulf states’ hot desert climates (Bwh). One of the top climate priorities is to promote climate resilience by reducing risks and enhancing adaptation options. This study aims to systematically review the existing literature to document building energy performances in and the associated adaptation measures of the Middle East Gulf states, regarding the implications of climate change. It is accomplished by answering the following questions: ‘How well do we understand the effects of climate change on building energy use in hot urban deserts?’ and ‘What are the most appropriate adaptation strategies to reduce energy use in hot urban deserts?’. Using the Preferred Reporting Items for Systematic review and Meta-Analysis protocols (PRISMA), 17 studies on the influence of present and future weather scenarios on building performance are examined, considering variations in typology, methods, and input variables. Finally, the paper identifies the preferred methods and input variables for modelling building energy performance under predicted climatic changes. Passive design considerations are considered highly effective in mitigating and adapting to climate change implications. Thermal insulation and efficient window glazing are identified as the best-performing strategies, while the use of solar Photovoltaic (PV) is considered efficient in meeting the primary energy demands. The study’s findings can assist planners and designers in projecting future climatic influences on the energy usage of existing buildings. Full article
(This article belongs to the Special Issue Advancements in Adaptive, Inclusive, and Responsive Buildings)
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