# Machine Learning Based Design of Railway Prestressed Concrete Sleepers
## 2 Literature Review
However, the data in this study are all intuitive numerical values, and the unit of them is unified. The machine learning system is categorised into three types, Supervised Learning, Unsupervised Learning, and Reinforcement Learning [16]. In this study, the main purpose is to predict some unknown features (i.e., design capacities) that are needed through certain design data. Therefore, the appropriate machine learning type needs to be selected. Regression is a type of supervised learning, and its primary objective is to build an effective model that can predict the dependent characteristics from a variety of attribute variables. Regression analysis is performed to determine the correlation between two or more variables having cause-effect relations and to make a prediction for the topic by using relations. Regression using a single independent variable is called univariate regression analysis, while analysis using more than two independent variables is called multivariate regression analysis [16]. As can be seen from the previous studies, the regression algorithms \begin{table}
\begin{tabular}{c c c c} \hline
**Main Causes** & **Problems** & **North American Response** & **International Response** \\ \hline Lateral load & \begin{tabular}{c} Abrason on rail seat \\ Shoulder/fastening system wear \\ or fatigue \\ \end{tabular} & 6.43 & 3.15 \\ Vertical dynamic load & \begin{tabular}{c} Cracking from dynamic loads \\ \end{tabular} & 6.38 & 5.5 \\  & \begin{tabular}{c} Deraliment damage \\ \end{tabular} & 4.57 & 4.57 \\  & \begin{tabular}{c} Cracking from centre binding \\ \end{tabular} & 4.5 & 5.36 \\ Manufacturing and \begin{tabular}{c} maintenance defects \\ \end{tabular} & \begin{tabular}{c} Tamping damage \\ \end{tabular} & 4.14 & 6.14 \\  & \
\begin{tabular}{c} Others (e.g., manufactured defect) \\ \end{tabular} & 3.57 & 4.09 \\ Environmental considerations &
\begin{tabular}{c} Cracking from environmental or \\ chemical degradation \\ \end{tabular} & 3.5 & 4.67 \\ \hline \end{tabular}
\end{table} 
Table 1: Most commonly found problems related to railway concrete sleepers based on international survey outcomes from railway industry networks globally (ranked from 1 to 8, with 8 being the most critical). in machine learning will apply to this study. Many algorithms can be used for regression such as deep learning, random forest, support vector regression, ridge regression, and so on [17]. [PERSON] et al. [18] believed that deep learning can be suitable for processing large data and complexity. It allows for big data analysis using relatively simple linear models. This was proven by [PERSON] et al. [19]. Deep learning has been used in some big data areas, such as computer vision [20] and speech recognition [21]. [PERSON] and [PERSON] [22] adopted an approach, which is the combination of Bayesian ridge regression and ensemble empirical mode decomposition, to handle complex time series of wind speeds. They successfully obtained the accurate, effective, and practical significance for a wind-speed-prediction value. [PERSON] et al. [23] developed a random forest prediction model to predict the likelihood that concrete-pouring requests would be accepted. Similarly, [PERSON] et al. [24] believed that random forests, as a common algorithm, are highly efficient and accurate. They perform well in processing big data [25]. [PERSON] et al. [26] proved that random forests are feasible in predicting land subsidence induced by tunnels. In addition, machine learning is widely applied in different areas such as industry [27], manufacturing [28; 29], composites [30], and so on. From the above, it can be seen that deep learning, Bayesian ridge, and random forest are commonly applied to feature prediction using big data. However, three points are worth noting. First, there is not much research on applying machine learning to the design of building structures or engineering.