Predicting settlement along railway due to excavation using empirical method and neural networks

More and more excavation projects are being performed near existing buildings and structures due to large-scale urban construction, in which the excavation unavoidably causes settlement and potential danger to the surrounding construction and buildings. For linear traffic facilities parallel to the excavation, the settlement profile parallel to the excavation, namely, the settlement along the traffic line, should also be considered. Moreover, the precise control of the differential settlement along the traffic lines also plays a very important role. Thus, it is necessary to establish a quick prediction model, which is able to consider both vertical and parallel settlement profiles, using the basic information on the excavation. Based on the large amount of field data, the characteristics of the settlement profiles are analyzed. A simplified empirical method is proposed; it is established based on the Rayleigh and Gauss distribution functions for predicting the ground settlement along railways induced by an excavation. Meanwhile, back-propagation neural networks are also used to predict the settlement behavior. A comparison between the predicted results and the monitoring data is given to verify the feasibility of the proposed method. A good agreement indicates that the proposed method can be employed to predict the settlement along railways due to an adjacent excavation. (C) 2019 Production and hosting by Elsevier B.V. on behalf of The Japanese Geotechnical Society.