Probabilistic identification of subsurface gypsum geohazards using artificial neural networks

Subsurface gypsum dissolution hazards imply risks to the construction and operation of new transport infrastructure including subsidence, cavity collapse and cavity flooding. This is a concern in Abu Dhabi, United Arab Emirates, where gypsum geohazards are observed and an extensive transportation network is planned. This paper proposes an artificial neural network (ANN)-based approach for the prediction of underground gypsum. Moreover, the approach is developed to provide the expected probability of gypsum presence and to generate gypsum hazard maps. Such maps provide both a general planning instrument and an input for the decision support systems. An application to Masdar City, Abu Dhabi, is discussed at the site of a planned metro line. Twenty-one boreholes are used to train and validate the ANN that is used to produce a 3D geological model identifying the expected presence of gypsum. Most significantly, the application illustrates how gypsum hazard maps can be obtained at any required depth providing planners and designers with essential information for risk assessment and management.