Phd Defense Ramon ter Huurne| navigating the underground exploring and supporting ground penetrating radar-enhanced utility surveying
Ramon ter Huurne is a PhD student in the department Market Dynamics. Promotors are prof.dr.ir. A.G. Doree and dr.ir. L.L. Olde Scholtenhuis from the Faculty Technology and Engineering.
Summery
Utility strikes pose a significant challenge in the construction industry. In 2022, the Netherlands reported approximately 47,000 such incidents, leading to cost overruns, service disruptions, environmental damage, and safety risks. Although utility maps and trial trenches (i.e., cut-and-cover excavation) are commonly used to survey utilities before excavation work, these methods often fall short. The geophysical ground-penetrating radar (GPR) method offers a non-intrusive and rapid alternative. However, GPR’s effectiveness is often hindered by insufficient insights into its local use dynamics, resulting in failed applications and limited adoption among construction organizations.
This PhD dissertation addresses this gap by providing context-rich, practice-based insights into GPR’s use in utility surveying. These insights enhance our understanding of the advantages and challenges associated with GPR-enhanced utility surveying. To improve practitioners’ understanding of GPR deployment, these insights are used to develop operational decision support and guidance for the use of GPR onsite. By doing so, this dissertation provides practical tools for navigating underground utilities with GPR, aiming to enhance its local use and contribute to reducing utility strikes.
The dissertation is structured into two research phases. In the problem exploration phase, I used a technology-in-practice lens to unravel the early-stage innovation adoption dynamics of GPR in construction site settings. This phase revealed mechanisms of change and stability within existing utility surveying routines when confronted with GPR. Additionally, through a bespoke methodological approach, three key roles for GPR were identified: as a supporting, complementary, and substituting tool for trial trenches. Building on the practice-based insights from this first phase, the dissertation translates practical GPR deployment strategies into the technical development and assessment of a machine-learning-driven decision model in the subsequent support development phase.
The outcomes of this dissertation have the potential to enhance the effectiveness, efficiency, and safety of utility surveying practices. However, to realize this potential, it is essential to communicate a realistic understanding of GPR’s value within the surveying context, enhance its legitimacy, and educate practitioners and organizations on its use. The insights presented in this dissertation can serve as a valuable resource in this regard.
