Large amounts of empirical data on transportation infrastructure assets continue to be collected at the network-level due to advancements in technology and in response to data-driven processes. These vast amounts of new data, combined with existing data, leave practitioners searching for ways to transform disparate datasets into effective information. This study expands the use of these data into new areas of application, namely roadway and roadside diagnostics. Providing diagnostics informs practitioners not only about the needs of an infrastructure project, but the causes of those needs. Using network-level data to diagnose fundamental causes improves the engineering aspect of early project development decision making. Mobile light detection and ranging (LiDAR) technology was used to create a new dataset by evaluating road and roadside surface geometry and drainage conditions. Temporal patterns in pavement condition data were mined to inform engineers about the health of the pavement. The geometric and drainage information was combined with information gleaned from mining the pavement condition data and publicly available soils data to provide improved diagnostic analysis of roadway projects. The study capitalizes on graph theory to convert network-level data into diagnostic information. The primary contribution of this study lies in developing new analytical methods that use network-level data to provide comprehensive diagnoses of roadway infrastructure projects and systems. Using these diagnostics early in project development has the potential to reduce late project problems that cost both time and money.