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Buildings
Special Issues
Design Innovations in Sustainable Buildings Driven by Emerging Technologies
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Design Innovations in Sustainable Buildings Driven by Emerging Technologies

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 (30 September 2023) | Viewed by 15203

Special Issue Editors

Dr. Shuai Lu

E-Mail Website
Guest Editor
1. Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
2. School of Architecture, Design and Planning, The University of Sydney, Darlington, NSW 2008, Australia
Interests: room acoustics; acoustic simulation; anomalous acoustic reflection
Special Issues, Collections and Topics in MDPI journals
Dr. Junjie Li

E-Mail Website
Guest Editor
1. School of Architecture and Design, Beijing Jiaotong University, Beijing 100044, China
2. Department of Civil, Environmental and Geomatic Engineering, Swiss Federal Institute of Technology in Zurich (ETH), 8049 Zurich, Switzerland
Interests: sustainable building technology; green building design and construction; high-performance low-carbon buildings; net-zero-energy houses; human health; climate-adaptive design
Special Issues, Collections and Topics in MDPI journals
Dr. Chunxiao Wang

E-Mail Website
Guest Editor
School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China
Interests: sustainable landscape architecture; landscape heritage
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since the beginning of the 21st century, our built environment has been reshaped by various emerging technologies to respond to the severe environmental and energy crises and provide healthier and more comfortable human habitats. To name a few, building-integrated solar photovoltaics changed how buildings are empowered, recycled materials changed what buildings are made of, digital fabrication and automated construction changed how buildings are built, and, most noticeable to end users, intelligent service systems and products changed how buildings interact with people. These technologies and their influence on the built environment have been extensively explored in the existing literature.

This Special Issue, however, focuses on how architectural design has been and could be changed by emerging technologies. As the starting point of the life cycle, architecture design plays a vital role not only in the functionality and aesthetics of buildings, but also in their sustainability. Only with appropriate designs can emerging technologies realize their full potential in the built environment, just like Le Corbusier’s modern architecture making full use of concrete and steel, and Frank Gehry’s fabulous geometries promoting computer-aided design. Now, in the current era with more exciting technologies and more urgent environmental and energy problems, it is again time to explore opportunities in design to facilitate sustainability, energy efficiency, and health in buildings, especially since this has been largely overlooked by scientific research.

This Special Issue aims to report current and promising innovations in sustainable architecture design theories, methods, and outcomes, distinguishable from normal practice, as an immediate result of emerging technologies, providing new perspectives and possibilities for pursuing energy efficiency, low environmental impact, zero carbon, human health, as well as beauty and pleasantness in the built environment. The scope includes but is not limited to new paradigms of sustainable architectural design, performance-based and/or AI-aided design tools and workflows, and novel architectural aesthetics and/or occupant experience brought by heuristic integrations of technologies. While design is partially a creative process, the superiority of the design innovations is expected to be clearly demonstrated by empirical investigations, such as measurements of built cases, simulations of envisaged designs, and/or subjective evaluations by occupants.

You may choose our Joint Special Issue in Sustainability.

Dr. Shuai Lu
Dr. Junjie Li
Dr. Chunxiao Wang
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

  • sustainable building
  • building energy consumption
  • renewable energy
  • design innovation
  • computational design
  • intelligent building

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

Published Papers (2 papers)

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19 pages, 21353 KiB  
Article
Generative Design of Outdoor Green Spaces Based on Generative Adversarial Networks
by Ran Chen, Jing Zhao, Xueqi Yao, Sijia Jiang, Yingting He, Bei Bao, Xiaomin Luo, Shuhan Xu and Chenxi Wang
Buildings 2023, 13(4), 1083; https://doi.org/10.3390/buildings13041083 - 20 Apr 2023
Cited by 18 | Viewed by 4950
Abstract
Generative Adversarial Networks (GANs) possess a significant ability to generate novel images that adhere to specific guidelines across multiple domains. GAN-assisted generative design is a design method that can automatically generate design schemes without the constraints of human conditions. However, more research on [...] Read more.
Generative Adversarial Networks (GANs) possess a significant ability to generate novel images that adhere to specific guidelines across multiple domains. GAN-assisted generative design is a design method that can automatically generate design schemes without the constraints of human conditions. However, more research on complex objects with weak regularity, such as parks, is required. In this study, parks were selected as the research object, and we conducted our experiment as follows: (1) data preparation and collection; (2) pre-train the two neural network, then create the design layout generation system and the design plan generation system; (3) realize the data augmentation and enhanced hundred level dataset to thousand level dataset; (4) optimized training; (5) test the optimized training model. Experimental results show that (1) the machine learning model can acquire specific park layout patterns, quickly generate well-laid-out plan layout plans, and create innovative designs that differ from the human designer’s style within reasonable limits; (2) GAN-driven data augmentation methods can significantly improve the generative ability of algorithms, reduce generative pressure, and achieve better generative results; (3) pix2pix is prone to mode collapse, and CycleGAN has fixed rule errors in expressing certain design elements; and (4) GAN has the ability to mine design rules in the same way as humans. Full article
(This article belongs to the Special Issue Design Innovations in Sustainable Buildings Driven by Emerging Technologies)
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Review

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23 pages, 2287 KiB  
Review
A Review of Data-Driven Building Energy Prediction
by Huiheng Liu, Jinrui Liang, Yanchen Liu and Huijun Wu
Buildings 2023, 13(2), 532; https://doi.org/10.3390/buildings13020532 - 15 Feb 2023
Cited by 26 | Viewed by 9546
Abstract
Building energy consumption prediction has a significant effect on energy control, design optimization, retrofit evaluation, energy price guidance, and prevention and control of COVID-19 in buildings, providing a guarantee for energy efficiency and carbon neutrality. This study reviews 116 research papers on data-driven [...] Read more.
Building energy consumption prediction has a significant effect on energy control, design optimization, retrofit evaluation, energy price guidance, and prevention and control of COVID-19 in buildings, providing a guarantee for energy efficiency and carbon neutrality. This study reviews 116 research papers on data-driven building energy prediction from the perspective of data and machine learning algorithms and discusses feasible techniques for prediction across time scales, building levels, and energy consumption types in the context of the factors affecting data-driven building energy prediction. The review results revealed that the outdoor dry-bulb temperature is a vital factor affecting building energy consumption. In data-driven building energy consumption prediction, data preprocessing enables prediction across time scales, energy consumption feature extraction enables prediction across energy consumption types, and hyperparameter optimization enables prediction across time scales and building layers. Full article
(This article belongs to the Special Issue Design Innovations in Sustainable Buildings Driven by Emerging Technologies)
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