Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
2.5
Journals
Applied Sciences
Special Issues
AI-Assisted Building Design and Environment Control
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

AI-Assisted Building Design and Environment Control

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 10242

Special Issue Editors

Dr. Teng Shao

E-Mail Website
Guest Editor
Associate Professor, School of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xi'an 710129, China
Interests: green building design; building performance optimization; building physical environment; artificial intelligence application in building design
Special Issues, Collections and Topics in MDPI journals
Dr. David Hou Chi Chow

E-Mail Website
Guest Editor
School of Architecture, University of Liverpool, Liverpool L69 7ZN, UK
Interests: hydrology; climatology and meteorology; sustainable architecture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has been widely used to solve various building problems, especially in the field of building design and environmental control, where it can improve the design level, optimize building performance, and control the comfort of indoor environments. The generation of design schemes using artificial intelligence models and platforms, the identification of building design elements, the machine learning-based prediction of building performance, and algorithm-driven multi-objective collaborative optimization have brought about innovative changes to traditional design work. Among the AI functions increasingly attracting the interest of the research community is its potential to help create healthier and more comfortable indoor environments by monitoring and analyzing temperature, humidity, air quality, lighting, and occupant behavior data and then regulating heating, cooling, ventilation, and lighting systems to control and improve indoor environmental quality.

In this Special Issue, we invite submissions exploring cutting-edge research and recent advances in the field of AI-assisted building design and environmental control. Both theoretical and experimental studies are welcome, as well as comprehensive review and survey papers.

Dr. Teng Shao
Dr. David Hou Chi Chow
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 250 words) can be sent to the Editorial Office for assessment.

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. Applied Sciences 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 2400 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

  • artificial intelligence technology
  • smart building design
  • building performance optimization
  • indoor environment control and optimization

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

31 pages, 13384 KB  
Article
Physics-Informed and Explainable Graph Neural Networks for Generalizable Urban Building Energy Modeling
by Rudai Shan, Hao Ning, Qianhui Xu, Xuehua Su, Mengjin Guo and Xiaohan Jia
Appl. Sci. 2025, 15(16), 8854; https://doi.org/10.3390/app15168854 - 11 Aug 2025
Cited by 4 | Viewed by 6066
Abstract
Urban building energy prediction is a critical challenge for sustainable city planning and large-scale retrofit prioritization. However, traditional data-driven models struggle to capture real urban environments’ spatial and morphological complexity. In this study, we systematically benchmark a range of graph-based neural networks (GNNs)—including [...] Read more.
Urban building energy prediction is a critical challenge for sustainable city planning and large-scale retrofit prioritization. However, traditional data-driven models struggle to capture real urban environments’ spatial and morphological complexity. In this study, we systematically benchmark a range of graph-based neural networks (GNNs)—including graph convolutional network (GCN), GraphSAGE, and several physics-informed graph attention network (GAT) variants—against conventional artificial neural network (ANN) baselines, using both shape coefficient and energy use intensity (EUI) stratification across three distinct residential districts. Extensive ablation and cross-district generalization experiments reveal that models explicitly incorporating interpretable physical edge features, such as inter-building distance and angular relation, achieve significantly improved prediction accuracy and robustness over standard approaches. Among all models, GraphSAGE demonstrates the best overall performance and generalization capability. At the same time, the effectiveness of specific GAT edge features is found to be district-dependent, reflecting variations in local morphology and spatial logic. Furthermore, explainability analysis shows that the integration of domain-relevant spatial features enhances model interpretability and provides actionable insight for urban retrofit and policy intervention. The results highlight the value of physics-informed GNNs (PINN) as a scalable, transferable, and transparent tool for urban energy modeling, supporting evidence-based decision making in the context of aging residential building upgrades and sustainable urban transformation. Full article
(This article belongs to the Special Issue AI-Assisted Building Design and Environment Control)
►▼ Show Figures

Figure 1

29 pages, 10104 KB  
Article
Multi-Objective Optimization for the Energy, Economic, and Environmental Performance of High-Rise Residential Buildings in Areas of Northwestern China with Different Solar Radiation
by Teng Shao, Jin Wang, Ruixuan Wang, David Chow, Han Nan, Kun Zhang and Yanna Fang
Appl. Sci. 2024, 14(15), 6719; https://doi.org/10.3390/app14156719 - 1 Aug 2024
Cited by 6 | Viewed by 2312
Abstract
Currently, the construction and operation of buildings are responsible for 36% of global final energy usage and nearly 40% of energy-related carbon dioxide (CO2) emissions. From the perspective of sustainable development, and taking into account economy and thermal comfort, it is [...] Read more.
Currently, the construction and operation of buildings are responsible for 36% of global final energy usage and nearly 40% of energy-related carbon dioxide (CO2) emissions. From the perspective of sustainable development, and taking into account economy and thermal comfort, it is crucial to consider the influence of multi-objective realization on design parameters. In this paper, high-rise residential buildings in the cities of Xi’an and Yulin, which have differences in solar radiation, in the western solar enrichment area of China are taken as the research objects. The four objectives of building energy consumption, thermal comfort, life-cycle cost, and life-cycle carbon emissions are weighed using the SPEA-2 algorithm by adjusting eleven design variables, thereby obtaining the Pareto non-dominated solutions. The TOPSIS method is applied to obtain the suitable parameter combinations under different scenarios. The results show that the differences in climate and solar radiation influence the solution distribution, the range of objective function values, and the values of the design variables in Pareto non-dominated solutions. The obtained optimal scheme for the Xi’an area has an energy-saving rate of 61.7%, a TDHP improvement rate of 20.3%, an LCC of 254.8 CNY/m2, and an LCCO2 of 72.3 kgCO2/m2. The corresponding values in the Yulin area are 69.7%, 19.4%, 230.2 CNY/m2, and 0 kgCO2/m2. This reflects the potential of solar energy utilization to reduce buildings’ energy consumption and carbon emissions. The methodology and findings can provide references for high-rise residential building design in Northwestern China. Full article
(This article belongs to the Special Issue AI-Assisted Building Design and Environment Control)
►▼ Show Figures

Figure 1

Review

Jump to: Research

28 pages, 833 KB  
Review
Mechanisms and Integrated Pathways for Tropical Low-Carbon Healthy Building Envelopes: From Multi-Scale Coupling to Intelligent Optimization
by Qiankun Wang, Chao Tang and Ke Zhu
Appl. Sci. 2026, 16(1), 548; https://doi.org/10.3390/app16010548 - 5 Jan 2026
Viewed by 576
Abstract
Tropical buildings face the coupled effects of four-high environmental factors, which accelerate thermal–humidity degradation, increase operational energy demands, and diminish building health attributes. This paper systematically integrates global research advancements to establish a theoretical framework for Tropical Low-Carbon Healthy Building Enclosures (TLHBEs) by [...] Read more.
Tropical buildings face the coupled effects of four-high environmental factors, which accelerate thermal–humidity degradation, increase operational energy demands, and diminish building health attributes. This paper systematically integrates global research advancements to establish a theoretical framework for Tropical Low-Carbon Healthy Building Enclosures (TLHBEs) by linking materials, structures, and buildings across scales. It identifies three key scientific questions: (1) Establishing a multi-scale parametric design model that couples materials, structures, and architecture. (2) Elucidating experimental and simulated multi-scale equivalent relationships under the coupled effects of temperature, humidity, radiation, and salinity. (3) Design multi-objective optimization strategies balancing energy efficiency, comfort, indoor air quality, and carbon emissions. Based on this, a technical implementation pathway is proposed, integrating multi-scale unified parametric design, multi-physics testing and simulation, machine learning, and intelligent optimization technologies. This aims to achieve multi-scale parametric design, data–model fusion, interpretable decision-making, and robust performance prediction under tropical climatic conditions, providing a systematic technical solution to address the key scientific questions. This framework not only provides scientific guidance and engineering references for designing, retrofitting, and evaluating low-carbon healthy buildings in tropical regions but also aligns with China’s dual carbon goals and healthy building development strategies. Full article
(This article belongs to the Special Issue AI-Assisted Building Design and Environment Control)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop