Abstract
In construction projects, inspection of structural components mostly relies on classical measurements obtained by measuring tapes, levelling, or total stations. With those methods, only a few points on the structure can be measured, and the resulting inspection may not fully reflect the actual, detailed condition of the complete object. Laser scanning is an emerging remote sensing technology to accurately and quickly capture surfaces of structures in high details. However, because of the complex, massive point cloud data acquired at a construction project, in practice, data processing is still manual work with computer aided programs. To improve upon current workflows, this paper proposes a method to automatically extract point clouds of individual surfaces of structural components of a concrete building, which subsequently can be used to inspect construction quality based on geometric information of the surfaces. The proposed method explores both spatial point cloud information and contextual knowledge of structures (e.g., orientation or shape) derived from building design specifications and practice. For extracting point clouds of surfaces of each structural component, the proposed method consists of 4 consecutive steps for extracting: (1) floors, ceiling slabs, and walls, (2) columns, and (3) primary and (4) secondary beams. Each step consists of two ingredients: (i) rough extracting the candidate points of the component and (ii) fine filtering of the surface points of the components via cell-based and voxel-based region growing segmentation (CRG and VRG) incorporating contextual knowledge of the structural members. Experimental tests on two different types of concrete buildings showed that the proposed method successfully extracts the structural elements, in which the completeness, correctness, and quality from the point-based evaluation are larger than 96.0%, 96.9%, and 92.0%, respectively. Moreover, the evaluation based on a shape similarity showed that the extracted floor, ceiling slab and wall overlap to the ground truth more than 92.5%.
Highlights
In construction projects, defects of structural components are inevi table
For extracting the floors and ceiling slabs, an input point cloud was decomposed into Two dimensional (2D) xy cells in Step 1.1, while Step 1.2 extracted the data points within the patches describing potential surfaces of the floor and ceiling slab (Fig. 11a and b)
The 2D cells in the yz plane, candidate patches for the first step of the cellbased region growing segmentation (CRG) (Fig. 12a), and final resulting points of surfaces of the yz walls from Step 1.3.1 and Step 1.3.3 were respectively shown in Fig. 12b and c
Summary
Defects of structural components are inevi table. The rework costs would be minimized if any defect of the component can be identified at an early phase of the project. In cur rent practice, defect inspection is mostly manual interpretation of geometric data acquired from measuring tapes, levelling, or total sta tions. These methods are time-consuming in acquiring and interpreting geometric data [3,4], and inspection results do not reflect the complete actual condition of a structure because only discrete locations on sur faces of the components are measured. Project managers have difficulties in identifying defects timely and accurately, and mak ing decisions based on objective results. Inspection results in hard copies that are cumbersome to integrate to digital tools, which hampers the efficiency of project management
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