Consolidation in Soft Soil – Case Study on Prefabricated Vertical Drains (PVDs)
Abstract
Background:
Constructing on soft ground is one of the challenges of geotechnical engineering. The unpredictable behaviour and characteristics of soft soil can cause much damage resulting in high maintenance costs in the post-construction phase.
Objective:
The purpose of this study is to analyse the consolidation process and ground improvement method using surcharge and a prefabricated vertical drain by measuring the accuracy of the prediction settlement value with the actual site settlement results.
Methods:
An effective ground improvement method is the application of a surcharge and prefabricated vertical drains (PVDs). Various methods can be used to predict the settlement effectively, one such method being PLAXIS 3D simulation. A case study on ground improvement works was selected for this research, where PVDs were constructed and implemented at the site. A few undisturbed samples were collected from the site to generate the parameters based on the lab test conducted in the simulation process. This parameter was carefully studied and representing the principal input for the 3D model, which is generated and represents the actual ground improvement method for the selected case study. The analysis was performed using a borehole and soft soil model to generate the diagram. The prediction settlement value was generated from the PLAXIS 3D analysis as the baseline comparing to the actual results. The factors that influence the settlement value, such as the length and spacing of the prefabricated vertical drain, construction method, and soil characteristics, are also discussed.
Results:
A predicted settlement of 2553 mm was generated by the simulation, while the actual settlement outcome at the site was 2096 mm, a difference of 457 mm, and a prediction accuracy of 82.1%.
Conclusion:
The study found that the combination of surcharge and prefabricated vertical drain in the ground improvement worked well. Also, discussed were the factors that influenced the accuracy of the prediction and the site results.
