## 4 Case Study  
### Background  
The case study is based on a cut-and-cover highway tunnel project in Yuhang District, Hangzhou, China, and this tunnel is a key link to connect the Hangzhouxi Railway Station to the expressway network in Hangzhou, as shown in Figure 3. Construction of this highway tunnel was started from April 2021 and is expected to be finished in December 2022. The highway tunnel was 3.63 km long and was divided into 14 foundation pits, with  
Figure 2: A specific BPNN for the proposed study.  
their depths ranging from 0 to 15.2 m and their width ranging from 31.5 m to 35.9 m. The types of supporting structures of these foundation pits included the gravity-retaining wall, the soil-mixing wall, the bored pile, the drilling bitten pile, and the slurry wall. Moreover, given that there were generally four types of soils above the bottom of these foundation pits, this study considers four soil layers, and thereby, the number of input parameters in the fifth category in Table 1 is four. Nevertheless, the number of soil layers above the bottom of foundation pits may be different in other projects, and such a number could be determined according to practical projects. Finally, the optimum number of previous settlement monitoring data considered as input parameters was determined by a set of preliminary experiments, where the number of previous settlement monitoring data was set from one day to nine days, as referred to previous literature [3; 27; 39], and the results showed that the highest prediction accuracy was obtained when previous monitoring data collected in the last three days were considered. Accordingly, three previous monitoring data were set as input parameters of the proposed BPNN.  
Settlement observation points were distributed in five rows along supporting structures outside these foundation pits, and the distances from these five rows to the outer edge of the supporting structures were 2 m, 5 m, 10 m, 15 m, and 20 m, respectively, as shown in Figure 4. Construction monitors measured settlements of these observation points once a day during the construction of the highway tunnel using a dummy level, as shown in Figure 5, and put the monitoring data, detailed construction stages, and weather conditions in the daily monitoring report. Data used in this paper were extracted from the daily monitoring report from 8 June 2021 to 20 October 2021, and, finally, 16,275 sets of data were obtained. The parameters of the proposed BPNN that were found to result in optimal computational efficiency were found to be an epoch of 200, a learning rate of 0.1, and a tolerance of 0.00001. The MATLAB 2019a language was used to program the BPNN, and all tests were performed on an Intel(R) Core(tm) 19-10900 CPU @ 2.80 GHz with 32 GB RAM in the environment of Microsoft Windows 10.  
The results of the case study are discussed in the following section. First, the optimum number of neurons in the hidden layer are investigated in Section 4.2.1. Then, the prediction results of the optimum BPNN are demonstrated in Section 4.2.2. Next, the impacts of the size of the training dataset, types of input parameters, and the source of training data on the settlement-prediction accuracy of BPNNs are discussed in Sections 4.2.3-4.2.5, respectively.  
Figure 3: Location of the cut-and-cover highway tunnel project.  
Figure 4: Locations of settlement observation points in the case study.  
Figure 5: Construction monitors were measuring settlements of observation points.