## 2 Materials and Methods  
### Laboratory Test Procedure  
A series of 44 small-scale uplift resistance tests were performed and the measured peak uplift resistance was used to calibrate advanced numerical models employing neural networks. Figure 1 shows the layout of the pullout system used for the small-scale uplift resistance tests. The pullout system comprised a rectangular test chamber, and a pulling arm system joined to an AC motor, which applied an uplift force to the buried pipes. More details regarding the testing methodology can be found in [PERSON] et al. [8] and [PERSON] et al. [9] The small-scale tests investigated the effect of varying the pipe diameter, pipe burial depth, and geogrid geometry on the uplift resistance of buried pipes, so as to identify the most suitable input parameters affecting the peak uplift resistance of pipes. Table 2 lists the range parameter values investigated in the 44 small-scale uplift resistance tests performed in this research. In addition, Figures 2 and 3 show typical uplift resistance tests in reinforced sand conducted in this study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & & & \\
**(Year)** & **Aim** & **SC Technique** & **R or R2** \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON] et al. \\ [6] \\ \end{tabular} } & To develop six different ANN models, all with & & \\  & a single hidden layer but with diverse neurons & & \\  & in the hidden layer, to forecast performance of & ANN & Various R2 are available. Highest R2 = 0.995 \\  & offshore pipelines. & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON]- \\ [PERSON] et al. \\ [52] \\ \end{tabular} } & To develop an uplift capacity prediction model & & \\  & in clay. & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} [PERSON] \\ [53] \\ \end{tabular} } & To develop new generic uplift capacity & & & M5-GP-1 R: train = 0.976; test = \\  & formulae by means of an AI-based model, a & Model tree and GP & & 0.996 \\  & composite of M5 and GP call. & & M5-GP-2 R: train = 0.986; test = \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} [PERSON] \\ [54] \\ \end{tabular} } & 1. To exploit the fuzzy sets of theory and GA & & \\  & optimization to examine the impact of input & & \\  & uncertainties on the uplift capacity estimations & & \\  & made by different available models. & Genetic algorithm (GA) & Improved GP R = train = 0.996; test \\  & 2. To modify the previously proposed & and fuzzy set theory & = 0.997 \\  & \"M5-GP\"-based models of the uplift capacity & & \\  & estimation for becoming less vulnerable to & & \\  & input uncertainties and more accurate. & & \\ \hline \multirow{3}{*}{
\begin{tabular}{c} [PERSON] et al. \\ [12] \\ \end{tabular} } & To carry out a parametric study of strain & & \\  & demands of X80 pipelines subjected to fault & ANN & Various R are available. Highest R = 0.995 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter/Category** & **Unit** & **Range** & **Average** \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Pipe diameter (D)/Input \\ \end{tabular} } & mm & 25–55 & 38.6 \\  & burial depth (H)/Input & mm & 100 and 150 & 134 \\ \cline{1-1  
Figure 1: The pullout system for conducting the small-scale peak uplift resistance tests.  
Figure 3: Typical layout of the uplift resistance test with two-layer geo-grid reinforcement.  
Figure 2: Typical layout of uplift resistance test with single layer geo-grid reinforcement.