# An Application of BOTDR to the Measurement of the Curing of a Bored Pile
## 3 Field Test
### Data Processing  
In this test, the first measurement time is after concrete pouring, and then every 24 h, the outdoor temperature is recorded. The test time is early May, the temperature is stable, the weather is clear, and the temperature difference between day and night is small. The measurement interval and ambient temperature are shown in Table 3.  
The first measurement data is taken as the initial value, the subsequent measurement data are compared, the Brillouin frequency shift is obtained and the strain information is calculated, and the pile deformation during concrete curing is evaluated. Using the monitoring data, the strain with distance curve of optical fiber sensor during concrete curing is drawn, and three distinct turning points are identified in Figure 5; they are at 19, 61, and 102 m. The two turning points are at 19 and 102 m, which are the positions of optical fiber sensor outlet at the top of the pile. The central turning point at 61 m is the transition section for the bottom of the pile. The curve is symmetrical at the bottom of the pile, which is consistent with the symmetrical arrangement of optical fiber sensor on the steel cage. The data of the pile are selected from Figure 6, the first data are taken as the benchmark. The subsequent data are subtracted from it to calculate the strain variation, but the data still need to be further processed. This is mainly due to the engineering practice, the instrument system error, and the surrounding environment agents, such as atmosphere, temperature, pressure, etc. As well as the influence of various factors such as artificial observation, the monitoring data obtained are mixed with useless noise signal, and the noise effect can lead to a wrong conclusion. The wavelet function is used to denoise the data, and the moving average method is used to smooth the data curve. The wavelet threshold de-noising is to achieve the approximate optimal under the minimum mean square error. This denoising method has the advantages of less calculation,  
Figure 5: Strain with distance of optical fiber sensor. (��1) is the data of the first collection; �2 is the data of the second collection; �3) is the data of the third collection; �4) is the data of the fourth collection). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Serial Number**} & **Measurement** & **Ambient** & **Note** \\  & **Interval** & **Temperature** & \\ \hline �1 & 0 h & 23 \({}^{\circ}\)C & Measure after \\  & 24 h & 20 \({}^{\circ}\)C & concrete pouring \\  & 48 h & 19 \({}^{\circ}\)C & \\  & 72 h & 21 \({}^{\circ}\)C & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Measurement interval and ambient temperatureeasy implementation, and reducing most of the noise signals [23]. The commonly used denoising methods are Harr wavelet denoising, bior wavelet denoising, and DB wavelet denoising. Table 4 shows the characteristics of the above three wavelet basis functions. In order to observe the denoising effect of the three wavelet basis functions more intuitively, the appropriate denoising method is selected. The threshold level is set to 3, and the Haar wavelet function, bior2.2 wavelet function, and DB4 wavelet function are used to denoise, and the denoising results are compared. The denoising results are shown in Figure 6. It can be seen that the denoising result of Haar wavelet function presents a step shape, which is different from the real situation (Figure 6a). The denoising results of Bior2.2 wavelet function and DB4 wavelet function are similar, which eliminates the influence of noise and avoids the distortion of data. The denoising result of Bior2.2 wavelet function is sharper at the turning point, while the denoising result of DB4 wavelet function is smoother (Figure 6b). Considering the comprehensive comparison, the DB4 wavelet function is selected as the denoising method of this experiment (Figure 6c). The db4 wavelet function is used. Through further selection, the threshold level is set as 4 to conduct denoising processing on the data.