Sinkholes are a significant underground hazard that threatens infrastructure and lives and sometimes results in fatalities. The annual cost of sinkhole damages exceeds $300 million, although this estimate is likely underestimated due to the need for national tracking. Sinkholes can also alter natural drainage patterns, leading to increased flood risk. While natural sinkholes occur, those in urban areas are predominantly manmade, caused by soil erosion from defective pipes, typically due to aging. Climate change, storm surges, and urbanization have accelerated subsidence in urban environments, posing greater risks to critical infrastructure and densely populated areas. Extensive research has focused on soil erosion in dams; however, this knowledge does not necessarily apply to erosion through orifices, where gravity and other factors play significant roles. This paper presents a critical literature review on internal soil erosion due to defective pipes (SEDP). The review highlights that hydraulic loading, backfill type, and pipe conditions (defect shape, size, and depth) influence SEDP. Key findings from experiments and numerical studies are summarized, while mechanisms and knowledge gaps are identified. However, it is concluded that the current understanding in this field remains limited, underscoring the urgent need for further experimental and numerical research to expand the knowledge base on SEDP.

Subsurface characterisation relies heavily on geological information such as borehole data, which are often sparse and uncertain. Traditionally borehole interpretation is often qualitative and without formal incorporation of uncertainties. In this paper, we propose a methodology based on multivariate Gaussian probability distributions to generate probabilistic 3D geologic models using borehole data including the associated uncertainties. We focus on uncertainties related to geologists' interpretation of where interfaces between geologic layers occur. This is done using multivariate Gaussian distributions with parameters optimised based on fitted probability distributions from borehole data and expert input. More specifically, at each borehole location, the geological layers are fitted with 3D Gaussian distributions that reflect the uncertain geologic knowledge. The proposed method is quite versatile allowing one to include different uncertainties in the formulation of the optimisation process. The model and the algorithms were implemented in MatLab and applied to the case of the Masdar City Subsurface in Abu Dhabi, United Arab Emirates for the identification of Sabkha, a high salinity geologic layer characteristic of the region, which is prone to dissolution and large settlements. The results are in the form of the probabilistic profiles and maps that reflect the probability of occurrence of the different geologies.