Detection of Structural Components in Point Clouds of Existing RC Bridges

The cost and effort of modelling existing bridges from point clouds currently outweighs the perceived benefits of the resulting model. There is a pressing need to automate this process. Previous research has achieved the automatic generation of surface primitives combined with rule-based classification to create labelled cuboids and cylinders from point clouds. While these methods work well in synthetic datasets or idealized cases, they encounter huge challenges when dealing with real-world bridge point clouds, which are often unevenly distributed and suffer from occlusions. In addition, real bridge geometries are complicated. In this paper, we propose a novel top-down method to tackle these challenges for detecting slab, pier, pier cap, and girder components in reinforced concrete bridges. This method uses a slicing algorithm to separate the deck assembly from pier assemblies. It then detects and segments pier caps using their surface normal, and girders using oriented bounding boxes and density histograms. Finally, our method merges over-segments into individually labelled point clusters. The results of 10 real-world bridge point cloud experiments indicate that our method achieves very high detection performance. This is the first method of its kind to achieve robust detection performance for the four component types in reinforced concrete bridges and to directly produce labelled point clusters. Our work provides a solid foundation for future work in generating rich Industry Foundation Classes models from the labelled point clusters

Detection of Structural Components in Point Clouds of Existing RC Bridges

The cost and effort for modelling existing bridges from point clouds currently outweighs the perceived benefits of the resulting model. Automating the point cloud-to-Bridge Information Models process can drastically reduce the manual effort and cost involved. Previous research has achieved the automatic generation of surfaces primitives combined with rule-based classification to create labelled construction models from point clouds. These methods work very well in synthetic dataset or idealized cases. However, real bridge point clouds are often incomplete, and contain unevenly distributed points. Also, bridge geometries are complex. They are defined with horizontal alignments, vertical elevations and cross-sections. These characteristics are the reasons behind the performance issues existing methods have in real datasets. We propose to tackle this challenge via a novel top-down method for major bridge component detection in this paper. Our method bypasses the surface generation process altogether. Firstly, this method uses a slicing algorithm to separate deck assembly from pier assemblies. It then detects pier caps using their surface normal, and uses oriented bounding boxes and density histograms to segment the girders. Finally, the method terminates by merging over-segments into individual labelled point clusters. Experimental results indicate an average detection precision of 99.2%, recall of 98.3%, and F1-score of 98.7%. This is the first method to achieve reliable detection performance in real bridge datasets. This sets a solid foundation for researchers attempting to derive rich IFC (Industry Foundation Classes) models from individual point clusters.SeeBridge, Trimbl

Automated production of synthetic point clouds of truss bridges for semantic and instance segmentation using deep learning models

The cost of obtaining large volumes of bridge data with technologies like laser scanners hinders the training of deep learning models. To address this, this paper introduces a new method for creating synthetic point clouds of truss bridges and demonstrates the effectiveness of a deep learning approach for semantic and instance segmentation of these point clouds. The method generates point clouds by specifying the dimensions and components of the bridge, resulting in high variability in the generated dataset. A deep learning model is trained using the generated point clouds, which is an adapted version of JSNet. The accuracy of the results surpasses previous heuristic methods. The proposed methodology has significant implications for the development of automated inspection and monitoring systems for truss bridges. Furthermore, the success of the deep learning approach suggests its potential for semantic and instance segmentation of complex point clouds beyond truss bridges.Agencia Estatal de Investigación | Ref. PID2021-124236OB-C33Agencia Estatal de Investigación | Ref. RYC2021-033560-IUniversidade de Vigo/CISU

Digital twinning of existing bridges from labelled point clusters

The automation of digital twinning for existing bridges from point clouds has yet been solved. Whilst current methods can automatically detect bridge objects in points clouds in the form of labelled point clusters, the fitting of accurate 3D shapes to detected point clusters remains human dependent to a great extent. 95% of the total manual modelling time is spent on customizing shapes and fitting them to right locations. The challenges exhibited in the fitting step are due to the irregular geometries of existing bridges. Existing methods can fit geometric primitives such as cuboids and cylinders to point clusters, assuming bridges are made up of generic shapes. However, the produced geometric digital twins are too ideal to depict the real geometry of bridges. In addition, none of existing methods have evaluated the resulting models in terms of spatial accuracy with quantitative measurements. We tackle these challenges by delivering a slicing-based object fitting method that can generate the geometric digital twin of an existing reinforced concrete bridge from labelled point clusters. The accuracy of the generated models is gauged using distance-based metrics. Experiments on ten bridge point clouds indicate that the method achieves an average modelling distance smaller than that of the manual one (7.05 cm vs. 7.69 cm) (value included all challenging cases), and an average twinning time of 37.8 seconds. Compared to the laborious manual practice, this is much faster to twin bridge concrete elements

Generating bridge geometric digital twins from point clouds

The automation of digital twinning for existing bridges from point clouds remains unresolved. Previous research yielded methods that can generate surface primitives combined with rule-based classification to create labelled cuboids and cylinders. While these methods work well in synthetic datasets or simplified cases, they encounter huge challenges when dealing with real-world point clouds. The proposed framework employs bridge engineering knowledge that mimics the intelligence of human modellers to detect and model reinforced concrete bridge objects in imperfect point clouds. Experiments on ten bridge point clouds indicate the framework can achieve high and reliable performance of geometric digital twin generation of existing bridges.This research is funded by EPSRC, EU Infravation SeeBridge project under Grant No. 31109806.0007 and Trimble Research Fun

Generating bridge geometric digital twins from point clouds

The automation of digital twinning for existing bridges from point clouds remains unsolved. Extensive manual effort is required to extract object point clusters from point clouds followed by fitting them with accurate 3D shapes. Previous research yielded methods that can automatically generate surface primitives combined with rule-based classification to create labelled cuboids and cylinders. While these methods work well in synthetic datasets or simplified cases, they encounter huge challenges when dealing with realworld point clouds. In addition, bridge geometries, defined with curved alignments and varying elevations, are much more complicated than idealized cases. None of the existing methods can handle these difficulties reliably. The proposed framework employs bridge engineering knowledge that mimics the intelligence of human modellers to detect and model reinforced concrete bridge objects in imperfect point clouds. It directly produces labelled 3D objects in Industry Foundation Classes format without generating low-level shape primitives. Experiments on ten bridge point clouds indicate the framework achieves an overall detection F1-score of 98.4%, an average modelling accuracy of 7.05 cm, and an average modelling time of merely 37.8 seconds. This is the first framework of its kind to achieve high and reliable performance of geometric digital twin generation of existing bridges

A benchmark framework of geometric digital twinning for slab and beam-slab bridges

We devise, implement, and benchmark a framework LUKIS to automate the process of geometric digital twinning for existing slab and beam-and-slab bridges. LUKIS follows a top-down strategy to detect and twin bridge concrete elements in point clouds into an established data format Industry Foundation Classes. Existing software packages require modellers to spend many labour hours in generating shapes to fit point cloud sub-parts. Previous methods can generate surface primitives combined with rule-based classification to produce cuboid and cylinder models. While these methods work well in synthetic datasets or simplified cases, they encounter challenges when dealing with real-world point clouds. We tackle this challenge by investigating the entire workflow of geometric digital twinning for bridges and proposing LUKIS to auto-generate bridge objects without needing to generate low-level surface primitives. We implement LUKIS on a single software platform. Experiments demonstrate its ability to rapidly twin geometric bridge concrete elements. Compared to manual operation, LUKIS reduces the overall twinning time by at least 95.4% while the twinning quality (spatial accuracy) is improved. It is the first framework of its kind to achieve the geometric digital twinning for primary concrete elements of bridges on one platform. It lays foundations for researchers to generate semantically enriched digital twins

Instance and semantic segmentation of point clouds of large metallic truss bridges

Several methods have been developed for the semantic segmentation of reinforced concrete bridges, however, there is a gap for truss bridges. Therefore, in this study a state-of-the-art methodology for the instance and semantic segmentation of point clouds of truss bridges for modelling purposes is presented, which, to the best of the authors' knowledge, is the first such methodology. This algorithm segments each truss element and classifies them as a chord, diagonal, vertical post, interior lateral brace, bottom lateral brace, or strut. The algorithm consists of a sequence of methods, including principal component analysis or clustering, that analyse each point and its neighbours in the point cloud. Case studies show that by adjusting only six manually measured parameters, the algorithm can automatically segment a truss bridge point cloud.Agencia Estatal de Investigación | Ref. PID2021-124236OB-C3Agencia Estatal de Investigación | Ref. RYC2021–033560-IUniversidade de Vigo/CISU

Defining a Digital Strategy in a BIM Environment to Manage Existing Reinforced Concrete Bridges in the Context of Italian Regulation

Regulatory activity concerning the management of existing bridges has recently been affected by updates, for instance, in Italy, which calls for a speedy and pragmatic approach based on new technologies such as building information modeling (BIM), when dealing with the survey and risk classification as well as the evaluation and monitoring of structural safety. This paper focuses on the development and integration of a digital solution, based principally on the specific framework developed by the authors, which supports BIM modeling and information management activities, in the structural setting under investigation, through the use of several technologies and tools, namely BIM-authoring, CDE platform and visual programming, in addition to programming in Python. Starting from the organization of a specific BIM object library and the initial data, inserted by means of a custom-made input environment, it was possible to reproduce digital models of bridges in accordance with specific information requirements following the new Level of Information Need setting. The applicability of the proposal is tested on two judiciously chosen real-life cases with different characteristics. Through this implementation, a series of advantages emerge, including expediting traditional procedures for BIM modeling, accessibility and traceability of information— which are constantly updated to support the monitoring of structural safety over time—and the decision-making process related to the bridge management context