The DINGO database of axial pile load tests for the UK: settlement prediction in fine-grained soils. Issue 4 (2nd October 2022)

• Record Type: Journal Article
• Authors: Voyagaki, Elia; Crispin, Jamie J.; Gilder, Charlotte E. L.; Ntassiou, Konstantina; O'Riordan, Nick; Nowak, Paul; Sadek, Tarek; Patel, Dinesh; Mylonakis, George; Vardanega, Paul J.
• Is Part Of: Georisk. Volume 16:Issue 4(2022)
• Page Start: 640
• Page End: 661
• Publication Date: 2022-10-02
• Subjects: Piles & piling -- full scale tests -- site investigation -- settlement -- soil/structure interaction; Environmental risk assessment -- Periodicals; Hazardous geographic environments -- Risk assessment -- Periodicals; Reliability (Engineering) -- Periodicals; Risk management -- Periodicals; 624.151
• DOI: 10.1080/17499518.2021.1971249
• Languages: English
• ISSNs: 1749-9518

Abstract

ABSTRACT: The availability of reliable field data is critical for the advancement of geotechnical engineering. This is particularly the case for piled foundations; due to the substantial geotechnical uncertainties. The settlement (performance) predictions from established analytical methods may deviate from field measurements by as much as an order of magnitude. This paper provides a statistical assessment of the uncertainty of predictions of pile performance under axial loading using an openly accessible geotechnical database of pile load tests from the United Kingdom. The collected database information was classified by pile type, location, test data quality and availability of geotechnical data. With reference to the data from fine-grained soils, two analytical models were employed to predict foundation settlement. The settlement prediction performance was then studied statistically and the model bias and error compared with reference to the aforementioned categories to identify the impact of different sources of uncertainty and evaluate the use of both models for future geotechnical practice. The two models investigated generally over-predict settlement, which is likely due to conservative selection of key model parameters, such as soil strength.