Predict settlement of soft clay soil treated with temperature using Deep Neural Network

Many The treatment of soft clay soil at different temperatures substantially influences its settlement properties. Increased temperatures can accelerate the consolidation of soft clay, resulting in alterations in settlement behavior. Estimating the settlement of soft clay soil beneath building foundations is a fundamental aspect of geotechnical engineering design. Traditionally, engineers rely on empirical formulas and classical consolidation theories, such as Terzaghi’s one-dimensional consolidation model, to predict settlement behavior. While these conventional methods provide useful approximations, they often struggle to capture the highly nonlinear and site-specific characteristics of soft clay soils. Recently, artificial intelligence (AI) models, particularly Deep Neural Networks (DNN) and other machine learning algorithms, have emerged as powerful tools for forecasting settlement more accurately. This study applied one of intelligent technique by using deep learning artificial Settlement model (SDNN) model to forecast underground settlement of soft clay soil treated with temperature. DNN model adopted 365 data set, input data parameters such as time period, various of distance between the heaters, temperature and load applied. The outcomes indicate that the SDNN model effectively forecasted ground settlement, showing similar results between actual and predicted values. The performance of the SDNN network model was notably impressive, achieving a mean absolute error (MAE) of 4.3356 % and a mean squared error (MSE) of 0. 4494%. The artificial model also demonstrated strong efficiency and a favorable variance calculation coefficient.