Deep Learning Models in Buildings

Dear Colleagues,

This Special Issue explores the cutting-edge integration of deep learning technologies within the construction management and building sectors, emphasizing the transformative impact these technologies have on enhancing efficiency, sustainability and innovation in building design, construction and maintenance. This issue delves into how deep learning models, as a subset of artificial intelligence (AI), are revolutionizing the approach to analyzing and managing vast amounts of data within the production and management of buildings. By leveraging complex algorithms, these models provide unprecedented insights into optimizing building performance, energy usage and material selection, thereby supporting the construction industry's shift toward more sustainable and smart building solutions.

The contributions within this Special Issue highlight the application of deep learning in various stages of the building lifecycle, from predictive maintenance and automated defect detection to energy consumption optimization and enhanced design decision-making. This focus on deep learning models showcases their potential to drive advancements in construction management practices, promoting not only environmental sustainability, but also operational efficiency and cost-effectiveness in the face of evolving industry challenges.

Topics include, but are not limited to:

• Deep learning applications and strategies in energy-efficient building design;
• The role of AI in enhancing building sustainability;
• Predictive analytics for the maintenance and operations of smart buildings;
• Deep learning algorithms for automated defect detection in construction;
• Optimization of construction material usage through machine learning models;
• The impact of AI on the lifecycle assessment of building projects;
• Integration knowledge graph and AI for building production and management;
• Advanced data analytics for improving construction supply chain sustainability;
• Techniques for processing and analyzing large-scale construction data using AI;
• Integration of IoT and deep learning for intelligent building systems;
• Case studies on the use of deep learning in construction project management;
• Ethical and societal considerations of AI in the construction industry.

Dr. Tae Wan Kim
Dr. Jun Young Jang
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.

• smart building technologies
• building performance optimization
• predictive maintenance in buildings
• automated defect detection
• construction data analysis
• energy consumption optimization
• AI-driven design decision-making
• deep learning in construction