Using 3D laser scanning for estimating the capacity of corroded steel bridge girders: Experiments, computations and analytical solutions

Tzortzinis, Georgios; Ai, Chengbo; Breña, Sergio F.; Gerasimidis, Simos

doi:10.1016/j.engstruct.2022.114407

Cited by 27 publications

(9 citation statements)

References 26 publications

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“…Although innovative techniques such as 3D laser scanning (J. Liu et al, 2023;Tzortzinis et al, 2022), dynamic Bayesian networks (DBNs) (Zhu et al, 2019), and Bayesian updating methods combined with Markov chain Monte Carlo (MCMC) techniques (M.-C. Chen et al, 2019) have been introduced for quality inspection and crack prediction; they predominantly focus on postproduction evaluations and monitoring, overlooking risk prevention and control during the production phase. This oversight underscores the urgent need for systematic identification, assessment, and management of quality risks in the production of prefabricated steel structural components, which is crucial for enhancing quality management and ensuring safety in construction and engineering projects.…”

Section: Literature Reviewmentioning

confidence: 99%

Quality Risk Assessment of Prefabricated Steel Structural Components during Production Using Fuzzy Bayesian Networks: A Case Study in China

Zhong,

Peng

2024

Preprint

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This study aims to address quality issues in the production of prefabricated steel structural components for buildings by investigating challenges in quality risk assessment. It identifies key factors contributing to quality problems and establishes an evaluation index system. Traditional methods encounter limitations in handling uncertainty and conducting quantitative analysis. Therefore, the fuzzy Bayesian network (FBN) theory is utilized to perform probabilistic analysis on quality risks during the production phase. This research achieves a more accurate and dynamic risk assessment by integrating the strengths of fuzzy logic and Bayesian networks (BNs) and by utilizing expert knowledge, the similarity aggregation method (SAM), and the noisy-OR gate model. The study reveals that factors such as the "low professional level of designers," "poor production refinement," and "poor storage conditions for finished products" have a significant impact on quality risks. This research offers a novel risk assessment tool for steel structural component production, effectively assisting enterprises in identifying potential risks, formulating risk reduction strategies, and enhancing production quality.

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Section: Literature Reviewmentioning

confidence: 99%

Quality Risk Assessment of Prefabricated Steel Structural Components during Production Using Fuzzy Bayesian Networks: A Case Study in China

Zhong,

Peng

2024

Preprint

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“…There is a noticeable gap in identifying and assessing potential risks during the production process. Although innovative techniques such as 3D laser scanning [27], dynamic Bayesian networks (DBNs) [28], and Bayesian updating methods combined with Markov chain Monte Carlo (MCMC) techniques [29] have been introduced for quality inspection and crack prediction, they predominantly focus on postproduction evaluations and monitoring, overlooking risk prevention and control during the production phase. This oversight underscores the urgent need for systematic identification, assessment, and management of quality risks in the production of prefabricated steel structural components, which is crucial for enhancing quality management and ensuring safety in construction and engineering projects.…”

Section: Literature Reviewmentioning

confidence: 99%

Quality Risk Assessment of Prefabricated Steel Structural Components during Production Using Fuzzy Bayesian Networks

Zhong,

Peng

2024

Buildings

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This study aims to address quality issues in the production of prefabricated steel structural components for buildings by investigating challenges in quality risk assessment. It identifies key factors contributing to quality problems and establishes an evaluation index system. Traditional methods encounter limitations in handling uncertainty and conducting quantitative analysis. Therefore, the fuzzy Bayesian network (FBN) theory is utilized to perform a probabilistic analysis of quality risks during the production phase. This research achieves a more accurate and dynamic risk assessment by integrating the strengths of fuzzy logic and Bayesian networks (BNs) and by utilizing expert knowledge, the similarity aggregation method (SAM), and the noisy-OR gate model. The study reveals that factors such as the “low professional level of designers”, “poor production refinement”, and “poor storage conditions for finished products” have a significant impact on quality risks. This study offers a scientific risk assessment tool designed to address the quality control challenges commonly experienced in the manufacturing of steel structural components. Identifying the critical risk factors that influence quality empowers actual production enterprises to develop risk management strategies and improvement measures in a more focused manner, thereby facilitating more effective resource allocation and risk prevention and control. Consequently, this approach has a significant impact on enhancing the overall production level and quality within the industry.

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“…Second, each knockdown factor is only valid for a unique structure/loading combination and is therefore difficult to generalize to other kinds of structures and applications. It has been shown that knowing accurately a structure’s initial geometry enables the accurate prediction of the buckling event [8,9]. However, in many applications, measuring the shape of a structure can be expensive and in some cases it is impossible.…”

Section: Introductionmentioning

confidence: 99%

Experimentally probing the stability of thin-shell structures under pure bending

Royer

Pellegrino

2023

Phil. Trans. R. Soc. A.

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This paper studies the stability of space structures consisting of longitudinal, open-section thin-shells transversely connected by thin rods subjected to a pure bending moment. Localization of deformation, which plays a paramount role in the nonlinear post-buckling regime of these structures and is extremely sensitive to imperfections, is investigated through probing experiments. As the structures are bent, a probe locally displaces the edge of the thin shells, creating local dimple imperfections. The range of moments for which the early buckling of the structures can be triggered by this perturbation is determined, as well as the energy barrier separating the pre-buckling and post-buckling states. The stability of the local buckling mode is then illustrated by a stability landscape, and probing is extended to the entire structure to reveal alternate buckling modes disconnected from the structure’s fundamental path. These results can be used to formulate efficient buckling criteria and pave the way to operating these structures close to their buckling limits, and even in their post-buckling regime, therefore significantly reducing their mass. This article is part of the theme issue ‘Probing and dynamics of shock sensitive shells’.

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Using 3D laser scanning for estimating the capacity of corroded steel bridge girders: Experiments, computations and analytical solutions

Cited by 27 publications

References 26 publications

Quality Risk Assessment of Prefabricated Steel Structural Components during Production Using Fuzzy Bayesian Networks: A Case Study in China

Quality Risk Assessment of Prefabricated Steel Structural Components during Production Using Fuzzy Bayesian Networks: A Case Study in China

Quality Risk Assessment of Prefabricated Steel Structural Components during Production Using Fuzzy Bayesian Networks

Experimentally probing the stability of thin-shell structures under pure bending

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