Toward automatic generation of 3D steel structures for building information modelling

Laefer, Debra F.; Truong-Hong, Linh

doi:10.1016/j.autcon.2016.11.011

Cited by 102 publications

(68 citation statements)

References 20 publications

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“…The use of automated surveying techniques, such as terrestrial laser scanning [11] and close-range photogrammetry [12] which produce dense point clouds, appears particularly suitable for obtaining the geometry of historic structures [13,14,15]. From such studies, it is evident that the use of terrestrial laser scanning and close-range photogrammetry surveying techniques could result in accurate representation of the geometry of a structure in a relatively reduced cost [16,17,18,19]. Documentation for supporting restoration works as well as monitoring of historic structures are common motivations for the use of these surveying techniques on structures of architectural heritage [20,21,22,23].…”

Section: Introductionmentioning

confidence: 99%

Stability analysis of leaning historic masonry structures

D’Altri

Miranda

Castellazzi

et al. 2018

Automation in Construction

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This paper introduces an automatic, powerful and easy to use procedure for undertaking stability analyses of leaning historic masonry structures, based on an upper bound finite element limit analysis (FELA) approach. The procedure proposed here consists of a comprehensive workflow which involves the automatic point cloud manipulation, the 3D mesh generation of the actual geometry for structural purposes (e.g. FE mesh), and a two-step FELA that reduces drastically optimization variables assuming only active few elements inside a restricted processing zone. To generalize the Heyman's intuition to complex real geometries, the use of a 3D upper bound FELA with a recursive kernel of variables reduction becomes necessary for a precise evaluation of the limit inclination that makes the structure collapse under gravity loads. This outcome permits to estimate the structural health condition of a historic structure by comparing the critical inclination angle with the actual one. To demonstrate the effectiveness of the automated procedure, the southwest leaning tower of the Caerphilly castle (Wales, UK) is investigated and failure mechanisms with collapse inclination angles are evaluated through FELA. The proposed procedure presents a high degree of automation at each operational level and, hence, could be effectively used to assess the stability of historic structures at a national scale and provide useful information to asset owners to classify the structural health condition of leaning historic masonry structures in their care.

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Section: Introductionmentioning

confidence: 99%

Stability analysis of leaning historic masonry structures

D’Altri

Miranda

Castellazzi

et al. 2018

Automation in Construction

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“…Future work can also be built on Step 4 to detect and segment delicate components in bridges with more complex superstructure geometries (e.g., grid‐beams, cross‐beams). The method developed for reconstructing gridded steel structures (Gyetvai et al., ; Laefer and Truong‐Hong, ) can be integrated.…”

Section: Discussionmentioning

confidence: 99%

“…Specially targeted laser-scanning techniques or settings are required for these challenging regions. The method of Laefer and Truong-Hong (2017) can be considered in this endeavor.…”

Section: Discussionmentioning

confidence: 99%

Detection of Structural Components in Point Clouds of Existing RC Bridges

Brilakis

Middleton

2018

Computer aided Civil Eng

113

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The cost and effort of modeling 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 labeled cuboids and cylinders from point clouds. Although these methods work well in synthetic data sets 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 article, 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 oversegments into individually labeled 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 labeled point clusters. Our work provides a solid foundation for future work in generating rich Industry Foundation Classes models from the labeled point clusters.

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“…Perez-Gallardo et al (2017) suggested a semantic model-based system to detect four object classes in an industrial scene using topological information. Laefer & Truong-Hong (2017) leveraged the steel standard library to identify and match the cross-sections of steel frames in point clouds. Recently, Riveiro et al (2016) used specific topological constraints to segment masonry bridge point cloud through normal clustering.…”

Section: Top-down Detectionmentioning

confidence: 99%

Generating bridge geometric digital twins from point clouds

Lu¹,

Brilakis²

2019

Computing in Construction

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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. End-user requirements (EURs)Developing detailed EURs of DTs is outside the scope of this study. This section summarizes the fundamental information that a DT must contain. The end-users of DTs are inspectors, engineers, and the decision makers. The EURs define the information that will be required by the end-users from both their own internal team and from suppliers. The EURs should clearly articulate the information requirements and describe the expected information deliverables. However, the nature of the EURs depends on the complexity of the project, the experience, and the requirements of the end-users. Experienced end-users may develop detailed EURs, whilst others may only set out highlevel requirements, and basic rules. Broadly, a DT includes:EUR 1: Component-level digital representation.

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Toward automatic generation of 3D steel structures for building information modelling

Cited by 102 publications

References 20 publications

Stability analysis of leaning historic masonry structures

Stability analysis of leaning historic masonry structures

Detection of Structural Components in Point Clouds of Existing RC Bridges

Generating bridge geometric digital twins from point clouds

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