Strength evaluation of deteriorated girder ends. II: Numerical study on corroded I-beams

Tzortzinis, Georgios; Knickle, Brendan T.; Bardow, Alexander; Breña, Sergio F.; Gerasimidis, Simos

doi:10.1016/j.tws.2020.107216

Cited by 7 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Liu et al [25] analyzed the degradation of the shear capacity of I-girders with local corrosion at the end by the finite element method. Tzortzinis et al [26][27][28][29] studied the bearing capacity of the natural corrosion beams of a decommissioned bridge. Saad-Eldeen et al [30,31] evaluated the ultimate strength of corroded ship steel box girders based on experiments and numerical simulations.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on the Overall Stability of Corroded H-Shaped Steel Beams

Luo¹,

Fu²,

Zhang³

et al. 2022

Buildings

View full text Add to dashboard Cite

The degradation of the overall stability of corroded rolled H-shaped steel beams under bending conditions has not been extensively studied. In the present study, monotonic tensile tests and overall stability tests were conducted on seven rolled H-shaped steel beams that were subjected to electrochemical corrosion in order to discuss the influence of corrosion on the material’s mechanical properties and the overall stability of steel beams under bending conditions. The test results have indicated that the strength, the elastic modulus, and the elongation of the steel declined with an increase in the corrosion rate of the steel beams, and an obvious plastic deterioration phenomenon was observed. In addition, all of the steel beams with different degrees of corrosion were subjected to overall flexural–torsional buckling failure. The stiffness and the overall stability ultimate bearing capacity of the corroded steel beams decreased with the increase in the corrosion rate, and the overall stability of the test beams with a high design corrosion rate degraded significantly. Furthermore, by using the finite element numerical simulation analysis software ABAQUS, a double-reduction corrosion model of the sectional dimensions and the material’s mechanical properties was established. The overall stability ultimate bearing capacities of the steel beams that were subjected to three-point bending and the corresponding load–lateral displacement curves were analyzed. In addition, the finite element numerical simulation results were compared with the test results for verification. Subsequently, the influence of the initial bending on the overall stability ultimate bearing capacity of the steel beams was analyzed by virtue of the verified finite element model. This study will provide a test basis for the evaluation of the bearing capacity of existing rolled H-shaped steel members, as well as an experimental basis and finite element model reference for the follow-up study on the degradation of the mechanical properties of the corroded rolled steel members.

show abstract

Section: Introductionmentioning

confidence: 99%

Experimental Study on the Overall Stability of Corroded H-Shaped Steel Beams

Luo¹,

Fu²,

Zhang³

et al. 2022

Buildings

View full text Add to dashboard Cite

show abstract

“…Researchers proposed a formula to calculate the ultimate bearing capacity based on a residual thickness ratio and a damage height percentage parameter. Tzortzinis et al (2021b) proposed modification to existing equations for assessing the bearing strength of rolled girders with corroded webs focusing on three aspects: the initial imperfection amplitude, the average web thickness, and the ratio of bearing length over section depth.…”

Section: Introductionmentioning

confidence: 99%

“…The ultimate capacity and failure modes of the girder models were compared. In order to replicate the thickness reduction distribution, Tzortzinis et al (2021b) divided the corroded region into multiple areas. The thickness was considered uniform in each area.…”

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.

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

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.

show abstract

Strength evaluation of deteriorated girder ends. II: Numerical study on corroded I-beams

Cited by 7 publications

References 7 publications

Experimental Study on the Overall Stability of Corroded H-Shaped Steel Beams

Experimental Study on the Overall Stability of Corroded H-Shaped Steel Beams

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

Contact Info

Product

Resources

About