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Towards Implementation of Artificial Intelligence in Predicting Pile Driving Blow Counts

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Proceedings of the 2nd Vietnam Symposium on Advances in Offshore Engineering (VSOE2021 2021)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 208))

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Abstract

The objective of this paper is to present the potential use of Artificial Intelligence in offshore energy industry to predict pile driving blow counts at new locations based on previous installation records. To this end, a machine learning (ML) model was developed using a public dataset comprising geotechnical and pile driving records, such as cone penetration test data (CPTu), pile geometry and hammer ENTHRU energy versus depth at several locations in the North Sea. The ML model combines the principles of stacking and blending state of the art ML algorithms for better and more efficient predictions of the blow counts. The model’s performance was assessed by the root mean squared error metric, in order to measure the difference between the recorded and the predicted blow counts values at each location. Results show that the adopted model allows a reasonably accurate prediction of the blow counts and faster than conventional method. Notwithstanding, the use of AI for pile drivability has some limitations and geotechnical specialists shall supervise the process and ensure the quality control of the results.

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Acknowledgements

The authors would like to acknowledge the dataset provider (ISFOG2020) which has been used, as well as Fugro management for devoting a considerable amount of time to perform this research and development work.

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Correspondence to Z. L. Delimi .

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Delimi, Z.L., Amavasai, A., Cristobal-Huerta, A., Clavaud, R. (2022). Towards Implementation of Artificial Intelligence in Predicting Pile Driving Blow Counts. In: Huynh, D.V.K., Tang, A.M., Doan, D.H., Watson, P. (eds) Proceedings of the 2nd Vietnam Symposium on Advances in Offshore Engineering. VSOE2021 2021. Lecture Notes in Civil Engineering, vol 208. Springer, Singapore. https://doi.org/10.1007/978-981-16-7735-9_45

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  • DOI: https://doi.org/10.1007/978-981-16-7735-9_45

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7734-2

  • Online ISBN: 978-981-16-7735-9

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