November 2024
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86 Reads
Civil Engineering and Architecture
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November 2024
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86 Reads
Civil Engineering and Architecture
October 2024
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27 Reads
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1 Citation
This study investigates the adoption of green design strategies in Nigerian public buildings , focusing on architects' perspectives and practices. Through qualitative interviews, architects demonstrated a shift towards sustainability, emphasising passive design, active technologies, water conservation, and sustainable materials. Challenges of the adoption include cost constraints and technical complexities, among others. The study recommends that prioritised collaborative action, community engagement, and policy advocacy would facilitate the implementation of green architecture. Despite limitations, the study findings contribute to Nigeria's architectural landscape sustainability. The study underscores the potential for Nigeria to lead in sustainable architecture, aligning with global goals for climate resilience and environmental stewardship.
July 2024
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78 Reads
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11 Citations
Buildings
Buildings significantly contribute to global energy consumption and greenhouse gas emissions. This systematic literature review explores the potential of artificial intelegence (AI) to enhance sustainability throughout a building’s lifecycle. The review identifies AI technologies applicable to sustainable building practices, examines their influence, and analyses implementation challenges. The findings reveal AI’s capabilities in optimising energy efficiency, enabling predictive maintenance, and aiding in design simulation. Advanced machine learning algorithms facilitate data-driven analysis, while digital twins provide real-time insights for decision-making. The review also identifies barriers to AI adoption, including cost concerns, data security risks, and implementation challenges. While AI offers innovative solutions for energy optimisation and environmentally conscious practices, addressing technical and practical challenges is crucial for its successful integration in sustainable building practices.
July 2024
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98 Reads
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5 Citations
Buildings significantly contribute to global energy consumption and greenhouse gas emissions. This systematic literature review explores the potential of artificial intelegence (AI) to enhance sustainability throughout a building's lifecycle. The review identifies AI technologies applicable to sustainable building practices, examines their influence, and analyses implementation challenges. The findings reveal AI's capabilities in optimising energy efficiency, enabling predictive maintenance, and aiding in design simulation. Advanced machine learning algorithms facilitate data-driven analysis , while digital twins provide real-time insights for decision-making. The review also identifies barriers to AI adoption, including cost concerns, data security risks, and implementation challenges. While AI offers innovative solutions for energy optimisation and environmentally conscious practices, addressing technical and practical challenges is crucial for its successful integration in sustainable building practices.
July 2024
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171 Reads
Civil Engineering and Architecture
May 2024
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842 Reads
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1 Citation
With buildings accounting for a significant portion of global energy consumption and greenhouse gas emissions, the application of artificial intelligence (AI) holds promise for enhancing sustainability in the building lifecycle. This systematic literature review addresses the current understanding of AI's potential to optimize energy efficiency and minimize environmental impact in building design, construction, and operation. A comprehensive literature review and synthesis were conducted to identify AI technologies applicable to sustainable building practices, examine their influence, and analyze the challenges of implementation. The review was guided by a meticulous search strategy utilizing keywords related to AI application in sustainable building design, construction, and operation. The findings reveal AI's capabilities in optimizing energy efficiency through intelligent control systems, enabling predictive maintenance, and aiding design simulation. Advanced machine learning algorithms facilitate data-driven analysis and prediction, while digital twins provide real-time insights for informed decision-making. Furthermore, the review identifies barriers to AI adoption, including cost concerns, data security risks, and challenges in implementation. AI presents a transformative opportunity to enhance sustainability in the built environment, offering innovative solutions for energy optimization and environmentally conscious practices. However, addressing technical and practical challenges will be crucial for the successful integration of AI in sustainable building practices.
May 2024
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168 Reads
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1 Citation
Civil Engineering and Architecture
January 2024
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1 Read
... One factor affecting BIM usage for construction processes is the willingness to invest in BIM adoption [10]. There is also a lack of motivation toward BIM adoption [11], [12]. The major barrier to using BIM among construction professionals is the insufficient involvement of stakeholders in BIM adoption and investment [13]. ...
October 2024
... The creation of AI-driven material passports and sophisticated algorithms for product lifecycle management is poised to revolutionize resource utilization and waste reduction in manufacturing. AI-enabled tracking of materials and equipment has been shown to reduce waste by over 40%, leading to significant cost savings and environmental benefits [127]. AI-based models have better prediction abilities compared to other models used in forecasting solid waste generation and recycling [128]. ...
July 2024
... AI in green building refers to the application of advanced technologies to optimize energy efficiency, reduce environmental impact, and enhance overall sustainability throughout the building lifecycle. A recent study emphasized that AI-driven design processes offer unprecedented capabilities to achieve sustainability goals by analysing multiple architectural and engineering design options to minimize embodied carbon and maximize energy efficiency [17]. One such method involves the use of Artificial Neural Networks (ANNs), a deep learning technique that quantifies the carbon footprint of various building designs, ultimately enabling more effective Net Zero Carbon Emission (NZCE) strategies [18]. ...
July 2024
Buildings
... The case study research approach was adopted for this research, as it allows for the exploration and understanding of complex issues [29]. Notable studies [30][31][32] also made use of case study method. The case study method was defined as a method used to observe data at a fundamental level. ...
May 2024
Civil Engineering and Architecture
... Through AI, organizations can enhance sustainability across various sectors. AI technologies enable the optimization of energy consumption, resource management, waste reduction, and the integration of renewable energy sources, fostering sustainable practices (Dikshit et al., 2023;Adewale et al., 2024;Adewumi et al., 2024;Chandratreya, 2024). Moreover, AI contributes to improving energy efficiency, reducing carbon footprints, and addressing key challenges in sectors like energy, transportation, and marketing (Hermann, 2023;Dikshit et al., 2023;Tomar and Grover, 2023). ...
May 2024