Jul 2026· International Journal of Automation Technology· Vol 20, pp. 254-265· 0 citations· 20 references
TL;DR
This study proposes a heuristic method for constructing lightweight parametric models of steel girder bridges from terrestrial laser scanning (TLS) point clouds that enables component-wise segmentation and reconstruction without relying on design drawings or prior structural models.
Abstract
Component-based inspection and maintenance of steel girder bridges increasingly benefit from three-dimensional as-is structural models that explicitly represent individual members and inspection elements. However, most existing bridges lack digital structural models, and generating such models from scanned point clouds remains a nontrivial task due to occlusions, missing data, and the absence of design drawings. This study proposes a heuristic method for constructing lightweight parametric models of steel girder bridges from terrestrial laser scanning (TLS) point clouds. The proposed method integrates image-based neighborhood search, point-based dimensionality analysis using principal component analysis, and line-based and plane-based RANSAC to extract and reconstruct major structural components, including deck slabs, main girders, and cross beams. By exploiting domain knowledge and the geometric characteristics of steel girder bridge superstructures, the method enables component-wise segmentation and reconstruction without relying on design drawings or prior structural models. The proposed approach was validated using real-world TLS data of an existing steel girder bridge, demonstrating stable extraction and reconstruction of major structural components. The resulting parametric models explicitly represent inspection elements and have the potential to facilitate the spatial association of inspection results, photographs, and maintenance records, thereby supporting practical bridge maintenance workflows. The applicability and limitations of the proposed method are also discussed.
The results demonstrate the potential of hybrid AI and geometric approaches to improve the efficiency, repeatability, and reliability of Scan-to-BIM processes for historical masonry bridge heritage and show that the geometric quality of the HBIM model depends primarily on the density, spatial distribution and completeness of the structural points, rather than on their total number.
V. Alfio, Massimiliano Pepe, Donato Palumbo et al.· Applied Sciences· 0 citations
Abstract. This paper presents a comprehensive methodology for the automated semantic segmentation and 3D reconstruction of industrial building elements, including roof panels, floor, rafters, purlins, and columns, from unstructured point clouds. The proposed approach integrates orientation-based filtering, projection onto characteristic planes, morphological analysis, and optimization-based I-profile fitting to generate accurate 3D models. The workflow begins with point cloud preprocessing, where the data are aligned with the building axes and cleaned of outliers, followed by subdivision into two subsets based on local surface orientation. Binary projections are then processed to extract element contours, while roof slopes and panel inclinations are automatically estimated to guide the reconstruction of rafters and purlins. The method was validated on a real-case study of 930 m² industrial warehouse scanned with a mobile laser scanner, resulting in a raw dataset of seven million points. The segmentation achieved F1-scores above 0.90 for floors, roof panels, rafters, and columns, and 0.75 for purlins. Profile fitting yielded an average width error of 3.8%, confirming the robustness and reliability of the reconstruction across diverse structural components.
P. González-Cabaleiro, M. Albadri, Antonio Fernández et al.· The International Archives o...· 0 citations
Spatial shape inspection of modern suspension bridges is relatively well-developed, whereas research on historic suspension bridges remains scarce. Historic bridges usually lack original drawings and maintenance records. Their slender members intersect, vegetation surrounds the structure, and no design geometry is available for comparison. This study develops a point-cloud-based spatial shape inspection method for historic suspension bridges. The method covers point-cloud acquisition and processing, multi-module semantic segmentation, spatial shape anomaly screening, and historical shape reconstruction. It was tested on the Longjiang Bridge in Yunnan, China. On all labeled points, the segmentation achieved an overall accuracy of 98.35%, a macro-F1 score of 96.09%, and an mIoU of 92.78%, outperforming a rule-based configuration by 40.79 percentage points. No continuous profile anomaly was found within the effectively observed span, and a controlled 40 mm local displacement activated the screening criterion. The present main-cable sag was 3.12 m; a 0.3% effective cable-length change gave a historical sag of 2.57 m, and a support range of ±50 mm widened the admissible interval to 2.21–2.92 m. The proposed method can serve as a preliminary step in the health inspection of historic suspension bridges and provides methodological support for building digital archives of historic bridges.
Aiming at addressing the problems of complex geometric features, high inter-class similarity, and insufficient single-scale information in semantic segmentation of point clouds for ancient building interior components, this paper takes the Ba Wang Academy of Shenyang Jianzhu University as the research object and proposes a Geometric Feature Multi-scale Network (GFMN). First, a point cloud dataset containing five types of components—windows, beams, walls, roofs, and columns—was collected and constructed using a FARO Focus3D X330 terrestrial laser scanner (FARO Technologies, Lake Mary, Florida, USA). Second, 46-dimensional handcrafted geometric descriptors were extracted for each discrete point from four aspects: basic point attributes, local geometric features, density and scale features, and multi-scale fusion. On this basis, features were grouped according to semantics and fed into independent encoding branches, where a gated adaptive fusion mechanism was employed to dynamically adjust the contribution of each branch, and optimization was performed in combination with a prototype classification head and a joint loss function. Experimental results show that the proposed method achieved an overall accuracy of 93.17% on the test set, significantly outperforming state-of-the-art methods such as PointNet, PointNet++, Point Transformer, and Point Cloud Transformer. This study provides an effective solution for high-precision semantic segmentation of ancient building interior components.
Terrestrial laser scanning (TLS) point clouds are increasingly used for monitoring steel grid structures, and accurate instance segmentation is central to their processing. In complex environments, segmenting junction regions is challenging owing to multi-member geometry and incomplete sampling. Existing approaches frequently depend on prior information such as design drawings or Building Information Modeling (BIM), which limits generality and offers few solutions when model priors are unavailable. A hierarchical spherical coordinate segmentation with dual-sphere center refinement method (HSC-DCR) is proposed for geometry-driven junction region instance segmentation. The method uses radial connectivity. A segmentation origin is first located via a grid search driven by directional convergence evaluation, and a spherical coordinate system is then established for initial angular domain clustering. Subsequently, topological correction is guided by multi-dimensional indicators, and instance refinement is achieved through dual-sphere center optimization. The process is geometry-driven and independent of design models. Experiments on a laser-scanned stadium point cloud, covering 22 junction-region types and 521 instances, achieve an F1-score of 0.948 and mIoU of 85.5% under an instance-matched evaluation protocol. The results show that HSC-DCR can reliably obtain node and member instances from TLS point clouds without relying on drawings or BIM, supporting TLS-based monitoring of steel grid structures.
Hairun Chen, Alex Hay-Man Ng, Bo Hu et al.· Remote Sensing· 0 citations
With the development of intelligent unmanned systems, it is important for indoor mobile mapping and structural perception to reconstruct building structures in a timely manner from sequential LiDAR point clouds. However, many existing reconstruction methods rely on complete or accumulated point clouds, making them less suitable for partial observations, occlusions, and continuous updates. This paper proposes an incremental geometric reconstruction framework for building structures based on LiDAR point clouds. The method combines temporal state inheritance and orthogonal projection to transform 3D point-cloud processing into 2D plane-based contour updating. A transmissive relationship-based hole detection strategy is introduced to preserve real openings such as doors and windows while completing partially unobserved regions. Simulation and real-world experiments show that the proposed method can recover major planar building structures. In the simulation scene, the proposed method achieves a CD-L1 of 0.088 m, a CD-L2 of 0.007 m2, and an F1-score of 0.901, with an average single-frame processing time of 1.02 s. The experimental results indicate that the proposed method provides a compact and interpretable plane-based structural representation for near-real-time incremental reconstruction of building structures from sequential LiDAR point clouds.
Xian Cao, Changyu Qian, Hanqiang Deng et al.· ISPRS International Journal...· 0 citations
