Computerized maintenance management systems (CMMSs) of large commercial and institutional buildings archive work order logs. These logs contain invaluable operational performance indices pertaining to occupants and building systems. However, there are barriers to extract and visualize meaningful metrics from CMMSs. This study proposes a three-part framework to facilitate this process. The first part includes text mining techniques to preprocess and normalize the textual and other types of data. The second part develops benchmarking methods and metrics to evaluate the maintenance performance of buildings as well as to identify the buildings with most work orders on a complex. The last part adopts visualization methods to transform valuable information such as total work orders, the intensity of work orders of buildings and their change rates into more intuitive forms. To do so, a visualization method is implemented via building information model (BIM). Finally, the framework is applied to a case study to demonstrate how effectively the proposed methods perform in practice. The application of the framework can be used to quantify the distribution of WOs among buildings. For example, the case study revealed that 80 percent of all WOs originated from fewer than half of the buildings of a university campus.

大型商业和机构建筑的计算机化维护管理系统 (CMMS) 归档工作订单日志。这些日志包含与居住者和建筑系统有关的宝贵操作性能指标。然而，从 CMMS 中提取和可视化有意义的指标存在障碍。本研究提出了一个由三部分组成的框架来促进这一过程。第一部分包括文本挖掘技术来预处理和规范化文本和其他类型的数据。第二部分开发了基准测试方法和指标，以评估建筑物的维护性能，并确定综合体中工单最多的建筑物。最后一部分采用可视化方法对总工单等有价值的信息进行转化，建筑物的工单强度及其变化率转换为更直观的形式。为此，通过建筑信息模型 (BIM) 实现了一种可视化方法。最后，将该框架应用于案例研究，以证明所提出的方法在实践中的执行效果如何。该框架的应用可用于量化建筑物之间 WO 的分布。例如，案例研究表明，80% 的 WO 来自不到一半的大学校园建筑。该框架的应用可用于量化建筑物之间 WO 的分布。例如，案例研究表明，80% 的 WO 来自不到一半的大学校园建筑。该框架的应用可用于量化建筑物之间 WO 的分布。例如，案例研究表明，80% 的 WO 来自不到一半的大学校园建筑。