# Machine Learning Based Design of Railway Prestressed Concrete Sleepers
## 5 Conclusions  
This study develops a new machine learning application to aid in the design of railway prestressed concrete sleepers. In this study, about 3000 sets of design data have been collected and used for machine learning development. The capabilities of machine learning for the design application have been demonstrated. The expected contribution of the study is fulfilled based on the performances of the developed machine learning model. The accuracies are high, while the overall errors are relatively low. This proves that the developed machine learning models can be applied in the design process of prestressed concrete sleepers. It is believed that the models can shorten the time of the design process. At the same time, is the models are another tool that designers can use to recheck a design based on the traditional approach. Based on a critical literature review, three machine learning techniques that are the most suitable for application have been considered, including deep learning, Bayesian learning, and the random forest technique. The quality and quantity of the database have a great impact on the algorithms of machine learning. Especially in the deep learning model, performance could be partly affected by the design data structure. The deep learning algorithm does not perform well in this study, although the design database was simultaneously cleaned and normalised. It is suggested that (i) more historical data be collected to expand the design databases and (ii) the data structure be simplified for deep learning.  
The random forest algorithm performs very well in this study. The method forbids a simple interpretation of the relationship between response and predictor variables, and the data are generally overfitted. Our results reveal that this algorithm outperforms other machine learning techniques.  
Based on the investigations into all three machine learning models, it can be seen that the random forest algorithm and Bayesian network can be successfully applied to the design of railway prestressed concrete sleepers. Machine learning can help automate the complex and repetitive procedures of the design of railway prestressed concrete sleepers. Notably, the deep learning algorithm did not perform very well, considering all performance indicators.  
Future work will benchmark the results with other machine learning models [41], in order to improve the automation process. In addition, other parameters will also be considered to be predicted using machine learning, such as the other dimensions of sleepers, spacing of sleepers, or type of sleepers. The insight will be essential in enabling the component digital twin, aiding in the design and manufacture of railway prestressed concrete sleepers. This is the precursor to a reduction in material waste and an improvement in the circular economy of railway prestressed concrete sleepers.  
Conceptualisation, S.K. and Y.Z.; methodology, Y.Z.; software, Y.Z.; validation, J.S., J.H., and C.N.; formal analysis, Y.Z.; investigation, Y.Z.; resources, Y.Z.; data curation, A.M.R.; writing--original draft preparation, Y.Z.; writing--review and editing, J.S.; visualisation, J.H.; supervision, C.N.; project administration, A.M.R.; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Commission grant number 691135.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The authors also wish to thank the European Commission for the financial sponsorship of the H2020-RISE project no. 691135 \"RISEN: Rail Infrastructure Systems Engineering Network\", which enables a global research network that addresses the grand challenge of railway infrastructure resilience and advanced sensing in extreme environments (www.risen2 rail.eu, accessed on 30 September 2021). The APC is kindly sponsored by the MDPI's Invited Paper Initiative.  
**Conflicts of Interest:** The authors declare no conflict of interest.