Engin Burak Anil

TL;DR: The research described in this paper provides a taxonomy for patterns of deviations and sources of errors and demonstrates that it is possible to identify the source, magnitude, and nature of errors by analyzing the deviation patterns.

TL;DR: This paper outlines the research group's progress in developing methods to automate the process of creating as-built BIMs and in creating suitable methods to represent and visualize the information that is unique to as- Built Information Models.

TL;DR: The data and as-is BIM QA requirements of civil engineers are presented and it is demonstrated that the required QA information can be derived by analyzing the patterns in the deviations between the data and the as- is BIMs.

TL;DR: In this article, the authors leverage the advancements in automated field data capture technologies to support decisions during construction and facility operations, which can be used not only for acquiring data about the various operations being carried out at construction sites but also for gathering information about the context surrounding these operations and monitoring the workflow of activities during these operations.

TL;DR: A new approach for QA that analyzes patterns in the raw 3D data and compares the3D data with the as-is BIM geometry to identify potential errors in the model can help identify the sources of errors and does not require additional physical access to the facility.