Ensuring reliable facility services with aging infrastructure and growing deferred maintenance backlogs in water and fuel systems requires properly managing the maintenance of these critical systems. Dormitories safeguard the comfort and health of their occupants, but they rely on proactive management of and response to infrastructure failures. This study employed a data and text-mining approach to analyze 43,704 water and fuel work orders queried from a military maintenance database to identify the most common and costly failure types and the components that most frequently fail in dormitories. Statistical and correlation analysis indicated that leaks, clogs, and temperature regulation are problems that cost the most and occur most frequently, and toilets, sinks, and showers are the components that most often need repair. This study provides an intellectual framework for analyzing past maintenance work orders to reveal trends in failure types and components. The study contributes to the practice of infrastructure management by revealing that design and maintenance management should focus on preventing and repairing these common and costly failures. This study provides infrastructure maintenance managers with an objective understanding of the most common failure types and associated costs for water, sewer, and fuel supply infrastructure in dormitories to better inform resourcing decisions. By using readily available data from a work order database and incorporating data and text-mining techniques, this research highlights for maintenance managers the components that fail most often, the type of failure they experience, and costs associated with the repair of these failures. This research found that leaks, clogs, and problems with temperature regulation are the most common problems, and they occur most frequently in toilets, sinks, and showers in priority order. This research found that 74% of costs are associated with these common failure modes. Understanding common failure modes allows maintenance managers to efficiently determine parts on-hand levels, the composition of repair crews, and expected costs over a budget cycle. These findings allow maintenance managers to make better-informed budget and resourcing decisions using objectively identified maintenance trends so that they do not have to rely on experience or gut feelings. [ABSTRACT FROM AUTHOR]