Towards the development of a harmonized inventory database for decision support: automatized information extraction

Decisions made during tunnel construction are ultimately always based on the opinion of safety-oriented engineers and therefore utilize the knowledge of the employed human experts in the best possible way. Because every tunnel is unique to some extent, it can be assumed that experts’ decisions are often "reinvented" on-site at short notice. Given the number of completed, ongoing, and planned tunnel projects in Europe and worldwide, it is possible to identify projects which could be used as an extra reference to assist the decision-making processes for new constructions. For human experts, though, it is difficult and time-consuming to identify all similar reference projects. Aiming at developing a harmonized inventory database for decision support (DS) during the tunnel’s planning and construction phases, this work discusses a pathway for retrieving and processing data from archived projects. The major steps for information extraction are described, and the process of developing a harmonized database based on archived documentation is discussed. This work specifically addresses the process of extracting tabular information from images using machine learning methods.