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Intelligent Assessment of Axial Capacity of Pipe Piles Using High Strain Dynamic Pile Load Tests in Offshore Environment

Gohil, R and Parthasarathy, CR (2023) Intelligent Assessment of Axial Capacity of Pipe Piles Using High Strain Dynamic Pile Load Tests in Offshore Environment. In: Lecture Notes in Civil Engineering, 16 - 18 December 2021, Trichy, pp. 271-287.

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Official URL: https://doi.org/10.1007/978-981-19-6513-5_24


In this work, the authors have used 63 high strain dynamic pile load tests conducted by the authors in different offshore environments to develop neural network models, accurately predicting the total ultimate pile capacity and shaft resistance and end bearing. This model is developed explicitly considering the offshore pile driving scenario, where open-ended steel pipe piles are driven using different hammers. The pile’s capacity is found using Pile Driving Analyzer [PDA], and post-analysis is done using Case Pile Wave Analyses Program [CAPWAP], which is the standard practice for offshore pile testing. The input parameters used in this prediction involve pile geometry and soil properties available from CPT tests as well as the dynamic measurements obtained from strain gauges and accelerometers during the pile. Also, the pile driveability characteristics like measured blow counts and dynamic stress wave data during the field test are used as inputs to predict pile capacity. The Bayesian regularization function (as the optimization algorithm) with two hidden layers was the best from the different architectures of neural network models used here. The model developed performs the best during the training and testing, where an entirely new set of data is used. This approach is easy to apply in the field and is found to be accurate when the results of CAPWAP from dynamic pile load tests are to be verified because CAPWAP requires high expertise and lacks a unique solution.

Item Type: Conference Paper
Publication: Lecture Notes in Civil Engineering
Publisher: Springer Science and Business Media Deutschland GmbH
Additional Information: The copyright for this article belongs to Springer Science and Business Media Deutschland GmbH.
Keywords: Forecasting; Load testing; Multilayer neural networks; Offshore oil well production; Pile driving; Software testing; Soil testing, ANN; Case pile wave analyze program; Dynamic pile load test; High strains; Offshore environments; Pile capacity; Pile driving analyzers; Pile load test; Strain dynamics; Wave analysis, Piles
Department/Centre: Division of Mechanical Sciences > Civil Engineering
Date Deposited: 25 Jan 2023 05:43
Last Modified: 25 Jan 2023 05:43
URI: https://eprints.iisc.ac.in/id/eprint/79482

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