A rapid assessment methodology for bridges damaged by truck strikes

Stull, Christopher J.; Earls, Christopher J.

doi:10.12989/scs.2009.9.3.223

Cited by 10 publications

(4 citation statements)

References 8 publications

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“…The former technique refers to fitting simple primitives extracted from the segmented points while the latter indicates a technique of extruding a segmented shape along a path to obtain the 3D CAD model visualization. Examples of these techniques have been developed in existing research works conducted by Walsh, Borello, Guldur and Hajjar [66], Barazzetti [51], Xiong, Adan, Akinci and Huber [67], Stull and Earls [74],Laefer [75], and Yang et al [76]. In these research works, efforts have been made to develop a high-level algorithm or introduce a detailed scanning plan to auto-identify specific sections of a structure, especially bridge structures.…”

Section: Three-dimensional Model Generationmentioning

confidence: 99%

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

et al. 2020

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Over the last decade, particular interest in using state-of-the-art emerging technologies for inspection, assessment, and management of civil infrastructures has remarkably increased. Advanced technologies, such as laser scanners, have become a suitable alternative for labor intensive, expensive, and unsafe traditional inspection and maintenance methods, which encourage the increasing use of this technology in construction industry, especially in bridges. This paper aims to provide a thorough mixed scientometric and state-of-the-art review on the application of terrestrial laser scanners (TLS) in bridge engineering and explore investigations and recommendations of researchers in this area. Following the review, more than 1500 research publications were collected, investigated and analyzed through a two-fold literature search published within the last decade from 2010 to 2020. Research trends, consisting of dominated sub-fields, co-occurrence of keywords, network of researchers and their institutions, along with the interaction of research networks, were quantitatively analyzed. Moreover, based on the collected papers, application of TLS in bridge engineering and asset management was reviewed according to four categories including (1) generation of 3D model, (2) quality inspection, (3) structural assessment, and (4) bridge information modeling (BrIM). Finally, the paper identifies the current research gaps, future directions obtained from the quantitative analysis, and in-depth discussions of the collected papers in this area.

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Section: Three-dimensional Model Generationmentioning

confidence: 99%

“…One of the first applications of laser scanning technology in bridge assessment was presented by Stull and Earls [74]. In this study, a damaged composite bridge was modelled based on TLS information and the reserve capacity of the bridge structure was estimated reaching that of the extracted finite element model (FEM).…”

Section: Geometric Modellingmentioning

confidence: 99%

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

et al. 2020

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“…Moreover, they provided an example where TLS has served as an alternative means of evaluating static load deflections of a bridge which could not have reasonably had strain gauges attached to its underside without shutting down a major thoroughfare. Stull and Earls [34] used collected TLS information to estimate the capacity of a damaged bridge structure by extracting the finite element model. Another effort by Miśkiewicz et al [31] described a thorough interdisciplinary bridge assessment using a combination of TLS technology, Ground Penetration Radar (GPR), and Finite Element (FE) computation to evaluate the factors that contribute to the incidence of premature pavement cracks.…”

Section: Introductionmentioning

confidence: 99%

Application of TLS Method in Digitization of Bridge Infrastructures: A Path to BrIM Development

et al. 2022

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Over the past years, bridge inspection practices and condition assessments were predicated upon long-established manual and paper-based data collection methods which were generally unsafe, time-consuming, imprecise, and labor-intensive, influenced by the experience of the trained inspectors involved. In recent years, the ability to turn an actual civil infrastructure asset into a detailed and precise digital model using state-of-the-art emerging technologies such as laser scanners has become in demand among structural engineers and managers, especially bridge asset managers. Although advanced remote technologies such as Terrestrial Laser Scanning (TLS) are recently established to overcome these challenges, the research on this subject is still lacking a comprehensive methodology for a reliable TLS-based bridge inspection and a well-detailed Bridge Information Model (BrIM) development. In this regard, the application of BrIM as a shared platform including a geometrical 3D CAD model connected to non-geometrical data can benefit asset managers, and significantly improve bridge management systems. Therefore, this research aims not only to provide a practical methodology for TLS-derived BrIM but also to serve a novel sliced-based approach for bridge geometric Computer-Aided Design (CAD) model extraction. This methodology was further verified and demonstrated via a case study on a cable-stayed bridge called Werrington Bridge, located in New South Wales (NSW), Australia. In this case, the process of extracting a precise 3D CAD model from TLS data using the sliced-based method and a workflow to connect non-geometrical information and develop a BrIM are elaborated. The findings of this research confirm the reliability of using TLS and the sliced-based method, as approaches with millimeter-level geometric accuracy, for bridge inspection subjected to precise 3D model extraction, as well as bridge asset management and BrIM development.

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“…The predicted damage locations and severities were found to compare well with the imposed damages on the structure. Also many other research efforts attempted to apply ANNs to identify damage in structural engineering (Rosales et al 2009, Stull and Earls 2009, Inglessis et al 2002, Ramadas et al 2008, Lam and Ng 2008, Zapico et al 2003, Ni et al 2006, Lu and Tu 2004.…”

Section: Introductionmentioning

confidence: 99%

Structural damage detection of steel bridge girder using artificial neural networks and finite element models

Hakim¹,

Razak²

2013

Steel & Composite structures

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Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8% error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification.

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A rapid assessment methodology for bridges damaged by truck strikes

Cited by 10 publications

References 8 publications

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

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

Application of TLS Method in Digitization of Bridge Infrastructures: A Path to BrIM Development

Structural damage detection of steel bridge girder using artificial neural networks and finite element models

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