Predictive models in machine learning for strength and life cycle assessment of concrete structures

Dinesh, A.; Rahul Prasad, B.

doi:10.1016/j.autcon.2024.105412

Automation in Construction

2024

DOI: 10.1016/j.autcon.2024.105412

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Predictive models in machine learning for strength and life cycle assessment of concrete structures

A. Dinesh,

B. Rahul Prasad

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Cited by 2 publications

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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.

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Predictive models in machine learning for strength and life cycle assessment of concrete structures

Cited by 2 publications

References 105 publications

Development sustainable concrete with high-volume wastes tile ceramic: Role of silica nanoparticles amalgamation

Development sustainable concrete with high-volume wastes tile ceramic: Role of silica nanoparticles amalgamation

A Systematic Review of the Applications of AI in a Sustainable Building’s Lifecycle

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