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Advances in Nano Research Volume 17, Number 6, December 2024 , pages 485-499 DOI: https://doi.org/10.12989/anr.2024.17.6.485 |
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Advancing nanomaterials research: A comprehensive review of artificial intelligence applications in geotechnical properties |
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Ahmed Cemiloglu, Licai Zhu, Sibel Arslan, Yaser A. Nanehkaran, Mohammad Azarafza and Reza Derakhshani
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| Abstract | ||
| This article explores the role of artificial intelligence (AI) in predicting nanomaterial properties, particularly its significance within geotechnical engineering. By analyzing multiple AI-based studies, the review concentrates on the forecasting of nanomaterial-altered soil characteristics and behaviors. Encouraging findings from these studies underscore AI's ability to accurately predict the geotechnical properties of nanomaterials, though challenges remain, particularly in quantifying nanomaterial percentages and their implications across various applications. Future research should address these challenges to enhance the accuracy of AI-based prediction models in geotechnical engineering. Nonetheless, the growing adoption of AI for predicting nanomaterial properties demonstrates its potential to revolutionize geotechnical engineering. AI's capacity to uncover intricate patterns and relationships beyond human capabilities enables more precise soil behavior predictions, fostering innovative solutions to geotechnical challenges. Its ability to process vast datasets, adapt to various scenarios, and continuously learn from new information makes AI an indispensable tool for understanding nanomaterial properties and their impact on soil behavior. In summary, the integration of AI and geotechnical engineering represents a pivotal advancement in comprehending nanomaterial properties and their practical applications. As research advances and AI technologies evolve, transformative progress in geotechnical engineering is expected. By harnessing AI's capabilities, researchers can unlock groundbreaking insights, drive innovation, and shape a more resilient and sustainable future for the geotechnical engineering industry. | ||
| Key Words | ||
| artificial intelligence; geotechnical engineering; intelligent models; nanomaterials; prediction | ||
| Address | ||
| Ahmed Cemiloglu and Licai Zhu: School of Information Engineering, Yancheng Teachers University, Yancheng 224002, Jiangsu, China Sibel Arslan: Faculty of Technology, Sivas Cumhuriyet University, Sivas 58140, Turkey Yaser A. Nanehkaran: School of Information Engineering, Yancheng Teachers University, Yancheng 224002, Jiangsu, China/ Department of Management Information Systems, Cankaya University, Ankara 06790, Turkey Mohammad Azarafza: Geotechnical Unit, Faculty of Civil Engineering, University of Tabriz, Tabriz 5166616471, Iran Reza Derakhshani: Department of Earth Sciences, Utrecht University, Netherlands/ Department of Geology, Shahid Shahid Bahonar University of Kerman, Kerman 76169-14111, Iran | ||