Estimation of consolidation settlement caused by groundwater drawdown using artificial neural networks

Abstract

The method of back-propagation neural networks was employed in this study to develop a model for estimating the consolidation settlements caused by transient or long-term groundwater drawdown along the main Red line sections of Kaohsiung mass rapid transit, Taiwan. The available on-site boring test data including soil void ratio, groundwater drawdown depth and total unit weight of soil were taken as the input parameters. Three neural networks models with different combinations of these inputs were examined, which showed that the groundwater drawdown depth was the dominating factor to affect the consolidation settlement. The estimated results were compared with theoretical results, and statistical t-tests were performed to enhance the reliability of neural networks model. From the overall estimated results, the potential hazardous regions were identified along the Red line sections. It was found that there exists either a higher initial groundwater level or a thicker low compressibility clay layer, which might be the major reasons to cause the severely settlements, and must be carefully evaluated during the construction in these regions.