Publications / 2015 Proceedings of the 32nd ISARC, Oulu, Finland

Challenges in Generation of As-Is Bridge Information Model: A Case Study

Varun Kasireddy, Burcu Akinci
Pages 1-8 (2015 Proceedings of the 32nd ISARC, Oulu, Finland, ISBN 978-951-758-597-2, ISSN 2413-5844)
Abstract:

Most of bridges in the United States (US) were constructed long ago, so they have only 2D drawings and sketches, depicting the design information, as part of their documentation. Moreover, since routine inspection is conducted once every two years, it is challenging for inspectors in terms of situational and spatial awareness in order to thoroughly recall past condition information, just by looking at the existing documentation. An as-is 3D model of a bridge can potentially offer an accurate and up-to-date repository and enable visualizing and interpreting bridge details and or previously undertaken maintenance actions.

With the advent and success of semantically-rich building information models (BIMs) for buildings, several researchers have been working on extending such a specification for bridges. However, there are several challenges for researchers and bridge practitioners who intend to create an integrated as-is model based repository. This paper discusses these challenges in detail through a case study on an overpass bridge.

The findings include challenges and lessons learnt during the case study in various phases such as information extraction from documentation, 3D modelling and updating, and 3D model augmentation, for creating as-is bridge information model (BrIM). This work can contribute towards advancing state-of-the-art knowledge for semantically representing defects in this domain, and towards formalizing an approach to use multiple data acquisition modes, such as laser scanning and imaging, as a means to triangulate as-is information of the bridge.

Keywords: Automation, Robotics, Laser scanning, Bridge Information Modeling, As-is BrIM, Bridges, Bridge Inspection, Point Cloud, Deviation Analysis, Infrastructure Modeling, Semantic-rich bridge models