# Categorization of the Condition of Railway Embarments Using a Multi-Attribute Utility Theory
## 1 Introduction  
A significant part of the major infrastructure on European railway networks was built in the 19 th century, prior to the advent of modern design standards and specifications. Increased axle loading, aging, and climate impacts are known stressors to this infrastructure. In many parts of Europe, rainfall patterns are changing, in that longer dry spells are followed by periods of intense precipitation. Agged railway embarkments are extremely vulnerable to the impact of such events, as drying and cracking of near surface soils during dry periods allows rapid infiltration of water during rainfall, thereby reducing the soil strength and causing sudden failure [1]. The current approach for planning maintenance works on railway infrastructure is mostly reactive [2], since infrastructure managers usually do not have sufficient information and accurate models to assess and predict the condition. Forensic analyses of historic failures often reveal that indicators of distress were ignored due to lack of understanding or the absence of a proper framework for decision-making [3]. The decisions to perform maintenance are based mostly on visual observations, subjective judgments, and choices which are ruled by available budgets, planned schedules, or abrupt failures [4; 5]. Decisions based on these drivers often lead to undue maintenance and increased cost. Reactive maintenance should be avoided and railway agencies across the world are trying to move toward proactive maintenance planning, which would ensure safer, cost-effective, and improved network availability and reduce environmental impacts. The optimization of maintenance activities regarding technical and economic requirements is essential for transport infrastructure owners to fulfill societal expectations. Due to the long life time of rail infrastructure, especially engineeringstructures (often longer than 50 years), the assessment of technical and economic performance is necessary in order to optimize budget expenditure. Life cycle cost (LCC) analysis is a well-established methodology for the identification and assessment of maintenance trade-offs [6; 7; 8; 9]. Nevertheless, in order to predict maintenance interventions accurately, it is necessary to assess the current condition and predict the future performance. Through early identification of problems or hot-spot locations, low-cost remediation can be applied and thus costs can be reduced and failures avoided. The overall aim of the study is to develop a multi-attribute decision-making model to enable categorization of the condition of railway embankments across a network through the development of a ranking list. The steps involved in the categorization methodology are given within the paper. As a first step, several attributes are identified, and these include data from visual inspections and maintenance records available from the railway agency, as well as the information gathered by geophysical testing using a ground-penetrating radar. Ground Penetrating Radar (GPR) is a non-destructive tool that is widely implemented by railway owners, for example, to detect ballast fouling [10], ballast pockets [11], anomalies such as animal burrows [12], and the water content of the soil [13]. It has also been applied in a number of studies of embankment condition [14; 15]. The method is affected by some limitations, primarily related to the reliability of results, which is dependent on the set-up of the equipment and the knowledge and experience of the operators and those analyzing and interpreting the data. Despite these limitations, the rapid and non-destructive nature of GPR investigations makes the method ideal for the categorization of embankments. After evaluating the selected attributes on the investigated line, we utilize the multi-attribute utility theory to develop a categorization procedure to be used for proactive decision-making related to the maintenance of railway embankments.