Virtual trial assembly (VTA) simulates the preassembly by verifying the correspondence of as‐built and as‐designed prefabricated component (PC) models in virtual reality. Notably, obtaining accurate as‐built PC models ensures the success of VTA. To accomplish it, current methods require matching the scanned PC data with the as‐designed model and manually modifying the model to approximate the actual dimensions, which is inefficient and leads to subjective errors. Therefore, it is desirable to develop an automatic modeling approach to obtain reliable as‐built PC models for further improving the efficiency of VTA. However, to the best of our knowledge, few studies have been done on the automatic modeling of as‐built PC models in VTA procedure. To this end, based on the prior knowledge of as‐designed model, this study proposes an approach to automatically reconstruct the primary structure via plane fitting and structural feature point calculation for geometric modeling. Then, two adaptive methods for inner boundary detection and round‐hole estimation are developed for the reconstruction of detailed structure. Finally, as‐built model visualization is realized based on the data extraction and modeling development of a building information modeling software. To verify the feasibility of the proposed approach, a validation experiment was conducted on a full‐scale precast concrete stair using two scanning resolutions (1/5 and 1/8). Experiment results show that the estimation of the dimensions consists of multifaceted corners that are stable and not affected by scanning resolution. Low scan resolution (1/8) may increase estimation errors of the edge, which has a single‐sided turning point. The experiment results also confirm the effectiveness of the proposed approach for the automatic model reconstruction of as‐built PCs.

To date, to improve construction quality and efficiency and reduce environmental pollution, the use of precast concrete elements (PCEs) has become popular in civil engineering. As PCEs are manufactured in a batch manner and possess complicated shapes, traditional manual inspection methods cannot meet today’s requirements in terms of production rate of PCEs. The manual inspection of PCEs needs to be conducted one by one after the production, resulting in the excessive storage of finished PCEs in the storage yards. Therefore, many studies have proposed the use of terrestrial laser scanners (TLSs) for the quality inspection of PCEs. However, all these studies focus on the data of a single PCE or a single surface of PCE, which is acquired from a unique or predefined scanning angle. It is thus still inefficient and impractical in reality, where hundred types of PCEs with different properties may exist. Taking this cue, this study proposes to scan multiple PCEs simultaneously to improve the inspection efficiency by using TLSs. In particular, a segmentation and recognition approach is proposed to automatically extract and identify the different types of PCEs in a large amount of outdoor laser scan data. For the data segmentation, 3D data is first converted into 2D images. Image processing is then combined with radially bounded nearest neighbor graph (RBNN) algorithm to speed up the laser scan data segmentation. For the PCE recognition, based on the as-designed models of PCEs in building information modeling (BIM), the proposed method uses a coarse matching and a fine matching to recognize the type of each PCE data. To the best of our knowledge, no research work has been conducted on the automatic recognition of PCEs from a million or even ten million of the outdoor laser scan points, which contain many different types of PCEs. To verify the feasibility of the proposed method, experimental studies have been conducted on the PCE outdoor laser scan data, considering the shape, type, and amount of PCEs. In total, 22 PCEs including 12 different types are involved in this paper. Experiment results confirm the effectiveness and efficiency of the proposed approach for automatic segmentation and recognition of different PCEs.