Informed successive condition assessments in bridge maintenance

Honfí, Dániel; Björnsson, Ívar; Ivanov, Oskar Larsson; Leander, John

doi:10.1007/s13349-020-00415-2

Cited by 11 publications

(7 citation statements)

References 14 publications

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“…Hence, BIM cannot represent the original geometric deformation information nor support completion, acceptance, operation, and maintenance. Even though the BIM reconstructed based on 2D drawing is consistent with the geometric features of the bridge, the geometric features change as the service time of the bridge in the process of operation and maintenance phase [5]. e gap in the geometric features of the two gradually enlarges.…”

Section: Introductionmentioning

confidence: 97%

Automated Reconstruction of Parametric BIM for Bridge Based on Terrestrial Laser Scanning Data

Qin

Zhou

et al. 2021

Advances in Civil Engineering

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Building information modeling (BIM) in industrialized bridge construction is usually performed based on initial design information. Differences exist between the model of the structure and its actual geometric dimensions and features due to the manufacturing, transportation, hoisting, assembly, and load bearing of the structure. These variations affect the construction project handover and facility management. The solutions available at present entail the use of point clouds to reconstruct BIM. However, these solutions still encounter problems, such as the inability to obtain the actual geometric features of a bridge quickly and accurately. Moreover, the created BIM is nonparametric and cannot be dynamically adjusted. This paper proposes a fully automatic method of reconstructing parameterized BIM by using point clouds to address the abovementioned problems. An algorithm for bridge point cloud segmentation is developed; the algorithm can separate the bridge point cloud from the entire scanning scene and segment the unit structure point cloud. Another algorithm for extracting the geometric features of the bridge point cloud is also proposed; this algorithm is effective for partially missing point clouds. The feasibility of the proposed method is evaluated and verified using theoretical and actual bridge point clouds, respectively. The reconstruction quality of BIM is also evaluated visually and quantitatively, and the results show that the reconstructed BIM is accurate and reliable.

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Section: Introductionmentioning

confidence: 97%

Automated Reconstruction of Parametric BIM for Bridge Based on Terrestrial Laser Scanning Data

Qin

Zhou

et al. 2021

Advances in Civil Engineering

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“…In the paper by Giordano et al [9], this approach is applied to support the cost/benefit analyses of a scour monitoring system for emergency management. The value of information for structural integrity assessment is also the topic of the contribution by Honfi et al [10]. The authors demonstrate the increase of the expected utility that can be obtained through successive condition assessments.…”

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confidence: 94%

SHM for informed management of civil structures and infrastructure

Limongelli

2020

J Civil Struct Health Monit

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“…Therefore, only ambient excitation (i.e., wind and traffic) is used to identify dynamic modal parameters by measuring vibration responses. Moreover, dynamic modal parameters are beneficial for: (1) finite element model updating (Zeng & Kim 2020); (2) vibration-based damage detection (Honfi et al 2020); (3) real-time alarm systems for bridges and buildings (Chen et al 2011). It can be incorporated with maintenance systems to observe variations of modal parameters over time and track abnormality to prevent disastrous failure at an early stage.…”

Section: Introductionmentioning

confidence: 99%

A Two-stage Framework for Automated Operational Modal Identification

zeng¹,

Kim²

2021

Preprint

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Automated operational modal analysis (OMA) is attractive and has been extensively used to replace traditional OMA, which involves much empirical observation and engineers’ judgment. However, the uncertainties on modal parameters and spurious modes are still challenging to estimate under the field conditions. For addressing this challenge, this research proposed an automated modal identification approach. The proposed approach consists of two steps: (1) modal analysis using covariance-driven stochastic subspace algorithm (SSI-cov/ref); (2) automated interpretation of the stabilization diagram. An additional uncertainty criterion is employed to initially remove as many spurious modes as possible. A novel threshold calculation for clustering is proposed with incorporating uncertainty of modal parameters and the weighting factor. An improved self-adaptive clustering with new distance calculation is used to group physical modes, followed by the final step of robust outlier detection to select outlying modes. The proposed automated approach requires minimum human intervention. Two field tests of the footbridge and a post-tensioned concrete bridge are used to verify the proposed approach. A modal tracking was used for continuously measured data for demonstrating the applicability of the approach. Results show the proposed approach has fairly good performance and be suitable for automated OMA and long-term health monitoring.

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Informed successive condition assessments in bridge maintenance

Cited by 11 publications

References 14 publications

Automated Reconstruction of Parametric BIM for Bridge Based on Terrestrial Laser Scanning Data

Automated Reconstruction of Parametric BIM for Bridge Based on Terrestrial Laser Scanning Data

SHM for informed management of civil structures and infrastructure

A Two-stage Framework for Automated Operational Modal Identification

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