Estimation of the residual bearing capacity of corrosion damaged bridge beams using 3D scanning and finite element analysis

Hain, Alexandra; Zhang, T.; Zaghi, Arash E.

doi:10.1201/9780429279119-519

Cited by 3 publications

(2 citation statements)

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“…The MAPE values are found to be consistent across the training and test datasets, which signifies that there is no obvious overfitting in the neural networks. These error percentages are significantly better than the accuracy known for simplified methods that consider corrosion damage as uniform section loss, which can be up to 100% (Tzortzinis et al, 2021a;Hain et al, 2021). This demonstrates that the neural-network-based models are capable of predicting the results of a rigorous FE analysis and are able to estimate the residual load-carrying capacity of a corroded plate.…”

Section: Results Analysismentioning

confidence: 87%

“…Despite research completed to date, formulaic and FE-based approaches still have several limitations. First, the methods of using formulas and using FE analysis with simplified corrosion damage for predicting residual bearing capacity are not accurate (Tzortzinis et al, 2021a;Hain et al, 2021). Representing complex and irregular corrosion damage with a couple of parameters or with a uniform section loss neglects the fluctuation in corroded regions, which introduces significant errors, sometimes up to 100%, to the results.…”

Section: Introductionmentioning

confidence: 99%

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Application of neural networks to the prediction of the compressive capacity of corroded steel plates

Zhang

Vaccaro²,

Zaghi³

2023

Front. Built Environ.

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The application of artificial neural network approaches has been successful in solving complex civil engineering problems, such as damage detection and structural member capacity prediction. Within the context of the present study, corrosion has become the main factor limiting the safety and load-carrying capacity of aging steel bridge girders. Corrosion damage is often most severe near girder ends in simple-span bridges due to deck joint leakage and the pooling of water and de-icing salts. In addition to empirical methods, Finite Element (FE) analysis is typically used to evaluate the residual bearing capacity of corroded steel girders. However, it is prohibitively challenging and time-consuming to create an accurate FE model of a corroded girder due to the irregular nature of corrosion damage. Resultantly, corrosion damage is often reduced to uniform section loss, which leads to unreliable estimates of a girder’s residual bearing capacity. Researchers have proposed methodologies for modeling irregular corrosion damage, but these approaches require a high level of expertise. A comprehensive method is therefore required to efficiently estimate the residual bearing capacity of a corroded steel girder. This paper proposes the use of neural networks to predict the residual bearing capacity of corroded steel plate models as a first step in estimating the residual bearing capacity of an in-service girder. Neural networks are constructed and trained on a database built from FE analysis performed on steel plate models with realistic representations of corrosion damage. This study assesses the ability of neural networks to estimate the compressive capacity of corroded steel plates since plate girders are one of the most prevalent girder forms in steel bridges. Three types of neural networks are trained to predict the compressive capacity of corroded plate models, including a multilayer perceptron (MLP), a convolutional neural network (CNN), and a hybrid MLP-CNN model. The average mean absolute percentage errors (MAPE) for the three models are 20.65%, 11.46%, and 9.64%, respectively. The results of this study demonstrate the potential of using neural networks to predict the compressive capacity of corroded plates efficiently and accurately, which could facilitate proactive maintenance decision-making for aging bridges.

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Section: Results Analysismentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Application of neural networks to the prediction of the compressive capacity of corroded steel plates

Zhang

Vaccaro²,

Zaghi³

2023

Front. Built Environ.

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Failure mode and load prediction of steel bridge girders through 3D laser scanning and machine learning methods

Tzortzinis,

Wittig,

Filippatos

et al. 2024

ce papers

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Corrosion poses a significant threat to the longevity of steel bridges, impacting overall structural integrity. To effectively assess the structural condition of corroded steel bridges, conventional methods rely on visual inspections or single point measurements. To enhance and modernize this approach, this study introduces a novel framework integrating laser scanning data, computational models, and convolutional neural networks (CNNs). The CNN models are trained on a data set consisting of more than 1400 artificial corrosion scenarios generated by parameterizing real scan data from naturally corroded girders. This innovative method predicts the residual capacity and failure mode of corroded beam ends, achieving a low error rate of up to 3.3%. Unlike established evaluation procedures, the proposed evaluation framework directly utilizes post‐processed laser scanner output, eliminating the need for feature extraction and calculations.

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Structural integrity of aging steel bridges by 3D laser scanning and convolutional neural networks

Tzortzinis,

Filippatos,

Wittig

et al. 2024

Commun Eng

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For steel bridges, corrosion has historically led to bridge failures, resulting in fatalities and injuries. To enhance public safety and prevent such incidents, authorities mandate in-situ evaluation and reporting of corroded members. The current inspection and evaluation protocol is characterized by intense labor, traffic delays, and poor capacity predictions. Here we combine full-scale experimental testing of a decommissioned girder, 3D laser scanning, and convolutional neural networks (CNNs) to introduce a continuous inspection and evaluation framework. Classification and regression CNNs are trained on a databank of 1,421 naturally inspired corrosion scenarios, generated computationally based on point clouds of three corroded girders collected in lab conditions. Results indicate low errors of up to 2.0% and 3.3%, respectively. The methodology is validated on eight real corroded ends and implemented for the evaluation of an in-service bridge. This framework promises significant advancements in assessing aging bridge infrastructure with higher accuracy and efficiency compared to analytical or semi-analytical approaches.

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Estimation of the residual bearing capacity of corrosion damaged bridge beams using 3D scanning and finite element analysis

Cited by 3 publications

References 0 publications

Application of neural networks to the prediction of the compressive capacity of corroded steel plates

Application of neural networks to the prediction of the compressive capacity of corroded steel plates

Failure mode and load prediction of steel bridge girders through 3D laser scanning and machine learning methods

Structural integrity of aging steel bridges by 3D laser scanning and convolutional neural networks

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