# Machine Learning Based Design of Railway Prestressed Concrete Sleepers
## 2 Literature Review  
Over the past decades, there have been many research investigations with respect to prestressed concrete sleepers. However, only several investigations pay special attention to the design, reliability, and safety factors that correlate the field condition to the design approaches. In Europe, EN13230 \"Railway application-Track-Concrete sleepers and bearers\" [4], which has been published by the European Committee for Standardization (CEN), indicates design requirements for railway concrete sleepers at rail seat sections and the centre (or midspan) section. The design procedures include the calculation of dynamic rail seat loads, calculation of bending moments, check of resultant stresses, static test verifications, and so on. The concept is similar to other standards such as UIC 713: 1 ED 2004 [5], AS1085.14, AREMA Chapter 30, and JSA--JIS E 1201 [9; 10]; however, the design methodology in Europe is based on the limit state design principle. First, the quasi-static loads that apply to sleepers have to be calculated based on the axle loads and speeds of the rolling stock. The serviceability limit state of the track is also an important parameter. Then, concrete sleepers are designed to ensure that they can safely bear the loads over their entire service life. General manufacturing guidance for prestressed concrete sleepers was reviewed and recommended by fib [11]. However, the focus of the guidance was mainly placed on the manufacturing of concrete sleepers, with little evidence related to the design and realistic behaviours of sleepers throughout their entire service life. Note that European Standard EN 13230 [4] has very recently introduced a design guideline on a pilot stage (prEN13230-6), based on the UIC approach using quasi-static factors to represent dynamic loading, uncertainties of support conditions, track maintenance level, and so on. The new design guideline for dynamic loading and action calculations is still being validated by field data. In practice, the static resistance of concrete is usually used for structural design, in accordance with EuroCode2 (for concrete structures), because the high-cycle fatigue failure of sleepers is rarely observed in the field, if ever. As a result, cyclic tests become optional and uncommon, as per EN 13230. Table 1 shows the common critical issues related to railway concrete sleepers based on international survey outcomes from railway networks globally. More information can be found in [12]. [PERSON] and [PERSON] [13] demonstrated four steps to design and develop prestressed concrete sleepers, including force analysis, determination of shape size, design of bear capacity, and testing. After determining information about the key design parameters such as the geometry of the section and the design of load, the sleeper limit state and fatigue rupture can be analysed. The geometry of cross-sections, material strengths, and external load exist as key information in the design. When such parameters as material strengths and external loads can be determined, the sleeper cross-section can be optimised during the analysis and design. When applying machine learning to predict the deformation of the shield tunnelling surface, [PERSON] et al. [14] divided machine learning into three steps, including database building, model building, and model evaluation. Among them, the data were cleaned and normalised in establishing the database. Data cleaning was performed to remove outliers and null values from the database, while normalisation can eliminate the effects of magnitude between data. [PERSON] et al. [15] quantified and normalised disaster factors when studying area landslides. They created the format of unified disaster factors, and the degree of impact can be uniformly evaluated. The dimensional differences are eliminated, reducing the error of the results. None of these feature factors are intuitive. Factor quantification and normalisation are required. As is seen from the above, normalisation and data cleaning are useful for machine learning.