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Revolutionizing the construction industry by cutting edge artificial intelligence approaches: a review
Eliezer Zahid Gill1, Daniela Cardone1, Alessia Amelio2
1Department of Engineering and Geology, University "G. d'Annunzio" Chieti-Pescara, Pescara, Italy.
Frontiers in Artificial Intelligence
|December 27, 2024
Summary
Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are revolutionizing the construction industry. These technologies enhance environmental sustainability, operational efficiency, and worker safety through predictive analytics and real-time monitoring.
Area of Science:
- Construction Engineering and Management
- Artificial Intelligence in Civil Engineering
- Sustainable Construction Technologies
Background:
- The construction industry faces persistent environmental and operational challenges.
- Industry 4.0 technologies, including AI, ML, and DL, offer novel solutions.
- There is a growing need to integrate advanced computational methods for improved performance and sustainability.
Purpose of the Study:
- To review the application of AI, ML, and DL in addressing construction industry challenges.
- To explore AI's role in predicting air pollution, enhancing material quality, and monitoring worker safety.
- To assess AI's contribution to Cyber-Physical Systems (CPS) in construction.
Main Methods:
- Evaluation of various AI and ML models, including Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs).
- Assessment of optimization techniques such as whale and moth flame optimization.
- Review of research papers on AI applications in predicting material properties and monitoring worker health indicators.
Main Results:
- AI models effectively predict air pollutant levels (PM2.5, PM10, NO2, CO, SO2, O3) and material compressive strength.
- AI enhances construction material quality and real-time worker safety monitoring (posture, ECG, GSR).
- Advancements in AI, including Explainable AI and Petri Nets, are improving CPS for construction.
Conclusions:
- AI and ML technologies are highly adaptable and effective for current and future construction needs.
- These technologies show significant potential to promote sustainable practices, boost operational efficiency, and improve safety.
- Further research is needed for broader AI integration and real-world validation across diverse construction environments.
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