Reliability Analysis Methodology and Review on Design of Wick Drains in Soft Clay

Kamal, Mohamed Ashraf; Rabie, Mohammed Hassanien; Hussein, Hussein Mahmoud

doi:10.21608/erj.2025.341583.1151

Reliability Analysis Methodology and Review on Design of Wick Drains in Soft Clay

Document Type : Original Article

Authors

¹ Department of civil engineering, Faculty of engineering matariya branch, Helwan university, Cairo, Egypt

² Professor of Geotechnical Engineering, Helwan University, Cairo, Egypt.

³ Lecturer of Geotechnical Engineering, Helwan University, Cairo, Egypt.

10.21608/erj.2025.341583.1151

Abstract

This study outlines the current advancements in designing soil improvement systems utilizing preloading and prefabricated vertical drains (PVDs) in large soft soil deposits. Settlement values often exhibit significant variability due to uncertainties, particularly those associated with over-consolidation, compressibility characteristics, and the stratification of clay layers. Accurately quantifying these uncertainties poses a considerable challenge because of the typically limited and inconsistent data available.
To address these uncertainties, reliability analysis can be employed, allowing the estimation of the probability distribution of consolidation settlement rather than relying on a single deterministic value. This probabilistic approach provides valuable insights and a more comprehensive understanding of the outcomes. Stochastic methods, such as Monte Carlo simulations (MC), are commonly utilized in reliability analysis. These simulations involve selecting random values for input variables by constructing models that represent potential outcomes. By doing so, Monte Carlo simulations assess the impact of uncertainty and risk on predictions, thereby enhancing accuracy.
This paper provides state of the art for design of wick drains. In addition, this paper proposes a detailed methodology for conducting reliability analysis in the design of wick drains. The methodology incorporates soil variability and settlement monitoring data to improve the precision of settlement predictions for structures.

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