Towards Automatic Segmentation and Recognition of Multiple Precast Concrete Elements in Outdoor Laser Scan Data

Abstract

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.

A Decade of Modern Bridge Monitoring Using Terrestrial Laser Scanning: Review and Future Directions

“…In this effort, the point cloud of the prefabricated components was captured using TLS, and the results were compared with the as-designed geometry condition provided in BIM. In this regard, similar efforts have been conducted by Kim et al [206], Wang et al [207], and Maalek et al [208] on integrated BIM and laser scanning for real-time dimensional quality assurance [209,210] and decision making systems [211][212][213][214].…”

Remote Indoor Construction Progress Monitoring Using Extended Reality

“…The generated point clouds are co-registered with the model by using an adapted iterative-closest point (ICP) algorithm, and the as-planned model is transformed in to a point cloud by simulating the points using the recognized positions of the laser scanner [44]. Recently, researchers have developed systems that can detect different component categories using a supervised classification based on features obtained from the as-built point cloud; after that, an object can be detected if the classification category is identical to that in the model [3,20,21].…”

Automatic modeling of prefabricated components with laser‐scanned data for virtual trial assembly

“…However, the LSD for these supporting objects are regarded as the noise data that should be removed. To automatically remove the noise data, a recognition approach proposed by Liu, Li, Feng, Liu, and Wu (2019) was used in this study. The as‐designed model of the scanned PC was first transformed into a polygon representation.…”