# Machine Learning Based Design of Railway Prestressed Concrete Sleepers
###### Abstract
The other operational criteria that are critical to the design include static and dynamic vertical wheel loads, axle loads, load redistribution between sleepers, lateral loads, longitudinal loads, rail seat loads, and bending moments. [PERSON] et al. [6] demonstrated that the design of traditional prestressed concrete sleepers is inefficient and that large-scale parameter analysis consumes a lot of time and energy. Machine learning can learn the underlying rules and performance requirements from the design data features. It can perform a huge optimisation for the design in terms of required parameters and efficiency. The main work of designers today includes structural modelling, scheme adjustment, and design drawing. These tasks are time-consuming and repetitive, and the design technology for each task is declining. More and more young engineers are no longer willing to do this task [6]. With the rise of artificial intelligence, machine learning can be potentially introduced to the design of railway prestressed concrete sleepers. The learning speed of machine learning is relatively fast; machine learning can handle complex nonlinear problems, and it is free of human intervention. In recent years, it has been favoured by experts and scholars and is widely used for various applications. Machine learning is a method to learn  
Figure 1: End-of-life railway prestressed concrete sleepers. Concrete is relatively intact in many sleepers after 50 years, raising a concern for better design optimisation. (Courtesy: Abetong AB). rules and gain experience from large amounts of data, and then it can achieve the goal by intelligently screening or predicting new samples and new data through probabilistic and mathematical algorithms. Notably, most of the design data of the current prestressed concrete sleepers are recorded in practice, as part of the authoritative product-approval process. This becomes a comprehensive and truly effective source of design data sets that can benefit the development of machine learning for design automation. Over the decades, various machine learning algorithms have been developed for many engineering applications. They cover a variety of data and problem types presented in different machine learning problems [7]. It can be observed that different machine learning algorithms offer different prediction accuracy depending on the data structures, data features, and attributes [8]. This study aims to use machine learning in the design of railway prestressed concrete sleepers, in accordance with the limit state design method in EN13230. To automate complex design procedures, machine learning has been employed in the design of prestressed concrete sleepers. In this study, the maximum positive and negative bending moment capacities of prestressed concrete sleepers are the target predictions through machine learning. Therefore, the developed machine learning models are regression models. Note that the maximum positive bending moment at the rail seat and the maximum negative bending moment at the midspan of sleepers are the governing factors in the design. Key design features include the cross-section dimensions of the sleepers, eccentricity, total area of the prestressed tendons, tensile strength of prestressed tendons, and compressive strength of concrete. The machine learning algorithm has then been developed to predict the design capacities of railway prestressed concrete sleepers. Then, the performance of each model will be compared to select the most suitable model using different indicators. The novelty of the study is that it presents the application of machine learning in the support of a prestressed concrete sleeper design, which has never been accomplished before. As mentioned, this study aims to predict the bending moments that are critical in prestressed concrete sleeper design. The contribution of the study is that the developed machine learning model can be used to support the design process and shorten the time spent on the design.