BIM has become an intrinsic tool in managing building projects due to its ability to comprehensively represent information in digital form. However, using BIM as an information exchange tool is still in its infancy, particularly with regard to construction progress monitoring beyond time schedule information. The current study focuses on the development of an automated progress monitoring framework based on an IFC-based BIM and provides an extensive methodology based on a structured task-based approach in accordance with the latest IFC4.x schema in four stages. The first stage creates the appropriate IFC entities, which are then enriched with their values in the second stage. The third stage integrates the actual progress information, which requires regular updating from the construction site. Finally, the fourth stage enables the retrieval of progress information, which is then reported in a user-friendly format along with the estimation of additional progress indicators. The proposed method successfully integrated the progress information into their IFC-based BIM models, demonstrating its practical use for monitoring construction progress. In the end, a web-based application was also developed that made use of progress information stored within the standardized hierarchy of the updated IFC-based BIM to facilitate efficient reporting.

The registration of as-built and as-planned building models is a pre-requisite in automated construction progress monitoring. Due to the numerous challenges associated with the registration process, it is still performed manually. This research study proposes an automated registration method that aligns the as-built point cloud of a building to its as-planned model using its planar features. The proposed method extracts and processes all the plane segments from both the as-built and the as-planned models, then—for both models—groups parallel plane segments into clusters and subsequently determines the directions of these clusters to eventually determine a range of possible rotation matrices. These rotation matrices are then evaluated through a computational framework based on a postulation concerning the matching of plane segments from both models. This framework measures the correspondence between the plane segments through a matching cost algorithm, thus identifying matching plane segments, which ultimately leads to the determination of the transformation parameters to correctly register the as-built point cloud to its as-planned model. The proposed method was validated by applying it to a range of different datasets. The results proved the robustness of the method both in terms of accuracy and efficiency. In addition, the method also proved its correct support for the registration of buildings under construction, which are inherently incomplete, bringing research a step closer to practical and effective construction progress monitoring.

Progress monitoring is an essential part of large construction projects. As manual progress monitoring is time-consuming, the need for automation emerges, especially as, nowadays, BIM for the design of buildings and laser scanning for capturing the as-built situation have become well adopted. However, to be able to compare the as-built model obtained by laser scanning to the BIM design, both models need to use the same reference system, which often is not the case. Transforming the coordinate system of the as-built model into the BIM model is a specialist process that is pre-requisite in automated construction progress monitoring. The research described in this paper is aimed at the automation of this so-called registration process and is based on the dominant planar geometry of most buildings with evident corner points in their structures. After extracting these corner points from both the as-built and the design model, a RANSAC-based pairwise assessment of the points is performed to identify potential matching points in both models using different discriminative geometric invariants. Next, the transformation for the potential matches is evaluated to find all the matching points. In the end, the most accurate transformation parameter is determined from the individual transformation parameters of all the matching corner points. The proposed method was tested and validated with a range of both simulated and real-life datasets. In all the case studies including the simulated and real-life datasets, the registration was successful and accurate. Furthermore, the method allows for the registration of the as-built models of incomplete buildings, which is essential for effective construction progress monitoring. As the method uses the standard IFC schema for data exchange with the BIM, there is no loss of geometrical information caused by data conversions and it supports the complete automation of the progress-monitoring process.

Current methods of construction progress monitoring involve manual data collection and processing, which are time-consuming and labor-intensive, with a dominant human presence entailing several flaws such as missing or inaccurate information. Recent research efforts for automated progress monitoring have largely focused on model-based assessment methods that are dependent on a pre-requisite step known as registration which is still performed manually due to numerous challenges. This study proposes a novel automated coarse registration method that utilizes the BIM model as the as-planned model to align it with the corresponding as-built model using their geometrical features. First, it extracts the corner points in both models using their planar features and then identifies the conjugate corner points based on different geometric invariants. Later, the transformations are determined from those conjugate points and the most accurate transformation parameter is finalized in the end. The proposed method is validated on different datasets.