Context-Aware Framework for Highway Bridge Inspections

Bridge inspections are tedious, time consuming, and complex tasks in the field that require highly specific information pertinent to the decisions at hand. The use of a centralized inspection database and bridge inspection reporting software has been explored by several state DOTs in recent years. During an inspection routine, the inspector visually assesses the condition of a particular bridge component. Based on a priori knowledge of the bridge components' taxonomic hierarchy and ontology, the inspector navigates to the form corresponding to the component. The inspector then reports the component's condition to the database. Context-aware computing offers the possibility to make inspections more efficient by reducing the time required to navigate the software and the effort spent by inspectors to learn, remember, and recall the taxonomic hierarchy and ontology of bridge components. Context-aware computing leverages environmental variables, which define the inspector's context, and delivers streamlined information, pertinent to the task at hand, to assist decision making. This paper presents a computing framework that identifies the component of interest to the inspector and automatically queries the inspection database to retrieve information relevant to the component being assessed. The framework's run-time and space complexity are analyzed and presented. The uncertainty in sensing the inspector's location and line of sight are translated into errors in identifying the component of interest. Using a case study bridge, sensitivity analysis is performed to evaluate and characterize the errors in identifying the component of interest due to the errors in tracking technologies through simulation studies and field testing. The sensitivity analysis is used to evaluate the feasibility of employing global positioning systems (GPS) and magnetic compass technologies for location and line-of-sight tracking. Finally, the authors suggest a workflow design for integrating the framework into bridge inspection reporting software. (C) 2014 American Society of Civil Engineers.