Subsurface utility construction work often involves repositioning of, and working between, existing buried networks. While the amount of utilities in modern cities grows, excavation work becomes more prone to incidents. To prevent such incidents, excavation workers request existing 2D utility maps, use detection equipment and dig test trenches to validate their accuracy and completeness. Although test trenches are of significant importance to reveal information about subsurface conditions, the process of determining their location, number and size is not explicated by experts to date. This study therefore aimed to explicate the reasoning and logic behind the selection of utility test trenches, and to formalize this in a semantically-rich utility model. To this end, we conducted interviews with experienced excavator operators. We then derived heuristics and rules that the experts used to determine trench locations. Such rules related to, for example, the layout of the excavation site, and the type of utilities, and accuracy of available data. Based on these rules, we integrated various incomplete sources of data, and generated a 3D utility model that could generate several alternative construction situations. We used queries to identify the most suitable location for a test trench. The resulting answers to queries helped optimize the test trench selection process. Our prototype demonstrates that the identified rules (1) facilitate the generation of semantically rich 3D utility models, and (2) support test trench decision making.
|Title of host publication||Computing in Civil Engineering 2017: Smart Safety, Sustainability, and Resilience|
|Editors||Ken-Yu Lin, Nora El-Gohary, Pingbo Tang|
|Place of Publication||Seattle, Washington|
|Number of pages||8|
|Publication status||Published - Jun 2017|
Racz, P., Syfuss, L., Schultz, C., van Buiten, M., olde Scholtenhuis, L. L., Vahdatikhaki, F., & Doree, A. G. (2017). Decision Support for Test Trench Location Selection with 3D Semantic Subsurface Utility Models. In K-Y. Lin, N. El-Gohary, & P. Tang (Eds.), Computing in Civil Engineering 2017: Smart Safety, Sustainability, and Resilience (Vol. 2017, pp. 68-75). Seattle, Washington. https://doi.org/10.1061/9780784480847