Abstract
Abstract. The application of terrestrial laser scanners (TLSs) on construction sites for automating construction progress monitoring and controlling structural dimension compliance is growing markedly. However, current research in construction management relies on the planned building information model (BIM) to assign the accumulated point clouds to their corresponding structural elements, which may not be reliable in cases where the dimensions of the as-built structure differ from those of the planned model and/or the planned model is not available with sufficient detail. In addition outliers exist in construction site datasets due to data artefacts caused by moving objects, occlusions and dust. In order to overcome the aforementioned limitations, a novel method for robust classification and segmentation of planar and linear features is proposed to reduce the effects of outliers present in the LiDAR data collected from construction sites. First, coplanar and collinear points are classified through a robust principal components analysis procedure. The classified points are then grouped using a robust clustering method. A method is also proposed to robustly extract the points belonging to the flat-slab floors and/or ceilings without performing the aforementioned stages in order to preserve computational efficiency. The applicability of the proposed method is investigated in two scenarios, namely, a laboratory with 30 million points and an actual construction site with over 150 million points. The results obtained by the two experiments validate the suitability of the proposed method for robust segmentation of planar and linear features in contaminated datasets, such as those collected from construction sites.
Highlights
Construction project progress monitoring and deviation control are essential to allow decision makers to identify discrepancies between the planned and the as-built states of a project in order to take timely measures where required (Maalek and Sadeghpour, 2012)
In order to help overcome the aforementioned limitations of current manual practices, automating the monitoring and control processes on construction sites has been proposed in recent years
The points are classified into planes and lines through a robust principal components analysis (PCA), which uses the Det-MCD proposed by Hubert et al (2012) to robustly estimate the covariance matrix
Summary
Construction project progress monitoring and deviation control are essential to allow decision makers to identify discrepancies between the planned and the as-built states of a project in order to take timely measures where required (Maalek and Sadeghpour, 2012). Monitoring is performed manually, a time consuming, error-prone and labour-intensive task on large scale projects (Golparvar-Fard et al 2009). Site supervisory personnel spend 30-50% of their time manually inspecting and controlling the quality of the manually accumulated onsite data (Golparvar-Fard et al 2009). Reduction of this time by means of a novel approach to onsite data collection and analysis suggests that more time can be allocated towards improving vital construction related concerns such as safety, as well as workforce productivity and communications. In order to help overcome the aforementioned limitations of current manual practices, automating the monitoring and control processes on construction sites has been proposed in recent years
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