Modeling of shield-ground interaction using an adaptive relevance vector machine

•The developed model considers soil behavior in response to tunnel excavation.•Adaptive feature scaling factors are adopted to improve model performance.•The relative importance of features related to ground settlement is prioritized.•The model parameters can be determined automatically in the training process. Shield tunneling method is widely adopted in tunneling projects. Analysis of ground settlement is required as an effective way for minimizing the potential damage caused by tunneling. Many efforts have been devoted for this purpose using various methods such as empirical approaches and numerical modeling. However, there are multiple factors that may influence the ground settlement, and the shield-ground relationship is highly non-linear and complex. To understand the complex soil behavior in response to shield penetration, a model that can establish the relationship and make accurate predictions for tunneling-induced ground settlement is needed. This paper proposed a model based on relevance vector machines (RVMs) to develop the predictive relations. Adaptive feature scaling factors were introduced as an inherent mechanism that enables RVMs to identify the relative importance of each input factor, and an optimization method for obtaining the appropriate values of feature scaling factors is proposed. The potential of the proposed adaptive model was investigated by applying it to tunnels that bored by an earth pressure balance (EPB) shield machine. Three categories of factors, namely tunnel geometry, geological conditions and shield operational parameters were considered in the model. The results demonstrate that the proposed model has competitive predictive capacities and that the adoption of adaptive feature scaling factors can enhance the prediction accuracy and provide a measure of the relative importance of each input factor. Moreover, the implementation of the adaptive RVM model is relatively simple. There is no need to set model parameters because they can be automatically optimized during model training, which makes the method a practical tool for geotechnical engineers to evaluate ground reactions during tunnel excavation.