Experimental Investigation and Modelling of Monotonic and Cyclic Soil-Pipeline Interaction in Multiphase Soil

The worldwide growing demand for energy requires a significant expansion of underground pipeline infrastructures. However, buried pipelines are prone to failure due to changing earth pressures under monotonic and cyclic loads. Pipelines, often shallowly buried in naturally unsaturated soils, have climate-related wetting and drying cycles in the surrounding soil, affecting the matrix suction stress and, thus, the soil’s shear strength, stiffness, and overall soil-pipeline interaction. This thesis investigates the complex interactions between soil and buried pipelines, which play an important role in the design, installation, integrity, functionality, performance, and lifespan of energy networks. The particular focus is on the interaction of unsaturated sandy soil conditions. The goal is to improve the understanding of the soil-pipeline interaction, thus providing engineers and practitioners with valuable insights beyond empirical approaches or single-phase soil mechanical assumptions. The study employs a comprehensive methodology, including experimental investigations, analytical analysis, constitutive modelling, and artificial neural network (ANN) model development

Effective thermal conductivity of unsaturated soils based on deep learning algorithm

Soil thermal conductivity plays a critical role in the design of geo-structures and energy transportation systems. Effective thermal conductivity (ETC) of soil depends primarily on the degree of saturation, porosity and mineralogical composition. These controlling parameters have nonlinear dependencies, thus making prediction a nontrivial task. In this study, an artificial neural network (ANN) model is developed based on the deep learning (DL) algorithm to predict the effective thermal conductivity of unsaturated soil. A large dataset is constructed including porosity, degree of saturation and quartz content from literature to train and validate the developed model. The model is constructed with a different number of hidden layers and neurons in each hidden layer. The standard errors for training and testing are calculated for each variation of hidden layers and neurons. The network with the least error is adopted for prediction. Two sand types independent of training and validation data reported in the literature are considered for prediction of the ETC. Five simulation runs are performed for each sand, and the computed results are plotted against the reported experimental results. The results conclude that the developed ANN model provides an efficient, easy and straightforward way to predict soil thermal conductivity with reasonable accuracy