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Three dimensional (3D) imaging sensors, such as laser scanners, are being used to create building information models (BIMs) of the as-is conditions of buildings and other facilities. Quality assurance (QA) needs to be conducted to ensure that the models accurately depict the as-is conditions. We propose a new approach for QA that analyzes patterns in the raw 3D data and compares the 3D data with the as-is BIM geometry to identify potential errors in the model. This "deviation analysis" approach to QA enables users to analyze the regions with significant differences between the 3D data and the reconstructed model or between the 3D data of individual laser scans. This method can help identify the sources of errors and does not require additional physical access to the facility. To show the approach's potential effectiveness, we conducted case studies of several professionally conducted as-is BIM projects. We compared the deviation analysis method to an alternative method -the physical measurement approach -in terms of errors detected and coverage. We also conducted a survey and evaluation of commercial software with relevant capabilities and identified technology gaps that need to be addressed to fully exploit the deviation analysis approach.

Documenting as-is conditions of buildings using 3D laser scanning and Building Information Modeling (BIM) technology is being adopted as a practice for enhancing effective management of facilities. Many service providers generate as-is BIMs based on laser-scanned data. It is necessary to conduct timely and comprehensive assessments of the quality of the laser-scanned data and the as-is BIM generated from the data before using them for making decisions about facilities. This paper presents the data and as-is BIM QA requirements of civil engineers and demonstrates that the required QA information can be derived by analyzing the patterns in the deviations between the data and the as-is BIMs. We formalized this idea as a deviation analysis method for efficient and effective QA of the data and as-is BIMs. An evaluation of results obtained through this approach shows the potential of this method for achieving timely, detailed, comprehensive, and quantitative assessment of various types of data/model quality issues.

Obtaining and utilizing as-is information with 3D imaging technologies, such as laser scanners, during various phases of facility life-cycle is becoming common practice. Both during construction and facility operations information derived from laser scanner data can serve many purposes by providing accurate information about the conditions of the facilities at the time of the scanning. Currently, the information derived from point clouds is typically represented as building information models (BIMs). Despite the benefits of having accurate as-is BIMs, current BIM approaches and tools have limitations in representing as-is information. The reason is partly that current methods of as-is BIM generation are based on existing as-designed BIM generation and representation processes. We identified five main concepts that are unique to as-is BIMs and are not represented with existing BIMs. These characteristics are point density, noise, occlusions, model deviations, and the links between the points and building components. This paper investigates these unique characteristics of as-is conditions and discusses how representing them within a BIM can provide advantages to downstream users, such as enabling decisions based on more complete data than would otherwise be possible.