## 5 Conclusions  
Accurate prediction of ground settlements surrounding a foundation pit is a prerequisite for ensuring construction safety during excavation works. A majority of existing settlement prediction models focus on the prediction of the maximum ground settlement and are unable to perform dynamic and real-time predictions. Although some studies have declared the development of dynamic settlement prediction models by involving dynamically collected field settlement monitoring data in their proposed machine learning-based models, time-dependent influence factors that may significantly affect daily ground settlements were generally neglected in their models, limiting the settlement-prediction accuracy of their models. Accordingly, the proposed study developed a BPNN, taking into consideration various time-dependent influence factors, to improve the BPNN's prediction accuracy on daily ground settlement. Data collected from a cut-and-cover highway tunnel project in western Hangzhou, China were used to demonstrate the feasibility and applicability of the proposed method. The average absolute error and the average absolute error percentage of the prediction results of the optimum BPNN at five settlement observation points were 0.57 mm and 7.02%, respectively, indicating that the proposed BPNN can accurately predict daily ground settlements. Moreover, the impact of the size of the training dataset on BPNNs' prediction accuracy was investigated, and the results demonstrated that increasing the number of training data can improve the prediction accuracy of BPNNs. Furthermore, based on the result of the sensitivity analysis of the impact of the types of input parameters on BPNNs' prediction accuracy, one can conclude that time-dependent influence factors, including construction stages and weather conditions, play vital roles in establishing an optimum BPNN with high settlement prediction accuracy. Finally, the result of the sensitivity analysis of the impact of the source of training data on BPNNs' prediction accuracy reveals that the BPNN with training data collected from multiple sources displays higher settlement prediction accuracy than those BPNNs trained using data collected from a single source. It is hoped that the present study will serve as a basis for further research on the accurate and dynamic prediction of ground settlements during the construction of foundation pits. Specifically, the proposed BPNN-based method improves the settlement prediction accuracy of current models by taking into consideration time-dependent influence factors. Moreover, the proposed methodology enables decision makers to know ground settlements in advance and formulate timely and appropriate control measures. This makes sense in situations where it is expected that the execution process will have to be adjusted during the  
Figure 11: Comparison of prediction results of M1, M2, and M3 at five settlement observation points: (a) 350–1; (b) 350–2; (c) 350–3; (d) 350–4; (e) 350–5. construction or if the initial observations show that the construction is not going according to predictions made in the design stage. Nonetheless, it has some limitations that should be acknowledged. First, this study considers two types of time-dependent influence factors in the proposed BPNN, while other time-dependent influence factors that may contribute to the daily ground settlement, such as truckloads and temporary surcharge loads, were neglected due to the lack of data. The subsequent study may emphasize the collection of data with thorough details to take into consideration additional time-dependent influence factors in BPNNs. Moreover, the proposed BPNN performs well when the construction sequence and weather conditions are well known and followed during the execution; however, the deviation of the construction sequence and weather conditions may result in inaccurate results.