Advancing building management with nano-enhanced carbon materials: a machine learning-driven business and economic analysis

Yuan Zhu; Khidhair Jasim Mohammed; Mohamed Gamal Elsehrawy; H. Elhosiny Ali; Hakim AL Garalleh

doi:10.1007/s42823-024-00826-x

Advancing building management with nano-enhanced carbon materials: a machine learning-driven business and economic analysis

Carbon Letters
Abbr : Carbon Lett.
2025, 35(2), pp.781~802
DOI : 10.1007/s42823-024-00826-x
Publisher : Korean Carbon Society
Research Area : Natural Science > Natural Science General > Other Natural Sciences General
Received : May 23, 2024
Accepted : October 17, 2024
Published : June 5, 2025

Yuan Zhu ¹, Khidhair Jasim Mohammed ², Mohamed Gamal Elsehrawy ³, H. Elhosiny Ali ⁴, Hakim AL Garalleh ⁵

¹Yangzhou University
²Al-Mustaqbal University College
³Prince Sattam Bin Abdulaziz University
⁴King Khalid University
⁵University of Business and Technology-Dahban

Accredited

ABSTRACT

Carbon aerogels including graphite and graphene have unique properties such as lightweight, strong, and insulative to roofing applications. Carbon aerogels offer innovative solutions in building management by enhancing thermal and acoustic insulation while reducing structural weight, aligning with the focus on economic and business analysis driven by machine learning. Traditional building materials often fail to meet contemporary energy efficiency and sustainability demands, underscoring the necessity for more advanced solutions. This project is dedicated to integrating carbon aerogels into roofing systems and employs Deep Neural Networks (DNNs) to optimize their performance and integration. The novelty of this study lies in its application of carbon aerogel technology—a cutting-edge, lightweight, and highly insulative material—specifically within roofing to analyze the practical evaluation of carbon aerogels’ thermal properties and economic viability in the construction industry. This study aims to rigorously assess carbon aerogels’ performance and financial impact on roofing applications. By conducting the thermal guard test and economic lifecycle evaluation, the study seeks to validate carbon aerogels’ enhanced energy efficiency and cost-effectiveness compared to traditional roofing materials. The study demonstrates that carbon aerogels offer superior thermal insulation in roofing applications, with a thermal conductivity of 0.02 W/m·K, significantly outperforming traditional materials. Economically, the high initial cost of carbon aerogels is effectively offset by substantial energy savings, estimated at $300 annually per square meter, resulting in a payback period of approximately 1.05 years. These findings are supported by rigorous testing and optimization through DNN, highlighting the material’s potential to enhance energy efficiency and sustainability in building practices.

KEYWORDS

Carbon aerogels, Energy efficiency, Roofing applications, Deep neural networks, Lifecycle cost analysis, Sustainable building practices

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This is the result of checking the information with the same ISSN, publication year, volume, and start page between the WoS and the KCI journals. (as of 2026-07-09)

Total Citation Counts(KCI+WOS) (2) This is the number of times that the duplicate count has been removed by comparing the citation list of WoS and KCI.

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Carbon Letters 2025 KCI Impact Factor : 0.73 KCI-WoS Impact Factor : 6.26

Advancing building management with nano-enhanced carbon materials: a machine learning-driven business and economic analysis

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Total Citation Counts(KCI+WOS) (2) This is the number of times that the duplicate count has been removed by comparing the citation list of WoS and KCI.

* References for papers published after 2025 are currently being built.

Carbon Letters 2025 KCI Impact Factor : 0.73 KCI-WoS Impact Factor : 6.26

Advancing building management with nano-enhanced carbon materials: a machine learning-driven business and economic analysis

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WoS Citation Counts (2) This is the result of checking the information with the same ISSN, publication year, volume, and start page between the WoS and the KCI journals. (as of 2026-07-09)

Total Citation Counts(KCI+WOS) (2) This is the number of times that the duplicate count has been removed by comparing the citation list of WoS and KCI.

REFERENCES (156) * References for papers published after 2025 are currently being built.

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* References for papers published after 2025 are currently being built.