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Title

Predicting the driveability of large diameter piles in sands and clays.

Posted
Reference   (Please mention Stopdodo/Environment Jobs in your application)
Sectors Sustainability, Climate, CSR, EMS
Location Denmark - Europe
Type Fixed Term and Permanent Roles
Status Full Time
Level Mid Level
Deadline 01/05/2013
Company Name Dong Energy
Contact Name
Website Further Details / Applications
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Description

Back analysis of the driveability of large diameter piles using the installation records and site investigation data that DONG Energy have from numerous projects in order to refine empirical factors used to predict the pile driveability.

 Depending on the number of students the project team may wish to limit the study to either sand or clay.

 

DONG Energy have built the largest number of offshore wind turbines in the world. One of the most significant costs in the construction is the installation vessels for the large piles which cost several hundred thousand pounds a day. To accurately cost a project it is essential to know how easy it will be to drive piles into the ground. The driving of the piles into the ground also causes fatigue in the piles, this needs to be taken into account during the pile design.

 

Current methods of driveability prediction have been generally developed for the use on much smaller piles than the large diameter monopiles used in offshore wind, and also contain a significant number of empirical factors. Precise predictions of driveability are therefore very difficult, and typically lead to the use of a bound theorem in practice. By back analysing the quantity of data that DONG Energy now have it is expected that these methods can be refined.

 

SUGGESTED METHDOLOGY

1.     Undertake literature review of various methods of predicting pile driveability.

2.     Choose reference locations, interpret suitable soil profiles from these location, and analyse driveability using methods that have been identified.

3.     Back analyse pile driving records and compare these to the predictions.

4.     Consider failure mechanisms, and the differences between the different predictions and the true results and hypothesise on alternative methods.

5.     Test hypothesis using driving records and other locations.


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