Load-carrying capacity of locally corroded steel plate girder ends using artificial neural network

Tohidi, Sajjad; Sharifi, Yasser

doi:10.1016/j.tws.2015.12.007

Cited by 73 publications

(23 citation statements)

References 41 publications

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“…In 2014, Tohidi and Sharifi [20] demonstrated the versatility of ANN by studying the buckling capacity of steel beams with rectangular web openings that has experienced corrosion in the web. In addition, Tohidi and Sharifi [21] developed an ANN model to estimate the bearing capacity of steel girders with corrosion at the bearing region. The ANN empirical formulas obtained were reported to be accurate in predicting the residual capacity of deteriorated steel beams.…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Formula for the Buckling Load Prediction of I-Section Cellular Steel Beams

Abambres¹,

Rajana

Tsavdaridis

et al. 2018

Computers

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Cellular beams are an attractive option for the steel construction industry due to their versatility in terms of strength, size, and weight. Further benefits are the integration of services thereby reducing ceiling-to-floor depth (thus, building’s height), which has a great economic impact. Moreover, the complex localized and global failures characterizing those members have led several scientists to focus their research on the development of more efficient design guidelines. This paper aims to propose an artificial neural network (ANN)-based formula to precisely compute the critical elastic buckling load of simply supported cellular beams under uniformly distributed vertical loads. The 3645-point dataset used in ANN design was obtained from an extensive parametric finite element analysis performed in ABAQUS. The independent variables adopted as ANN inputs are the following: beam’s length, opening diameter, web-post width, cross-section height, web thickness, flange width, flange thickness, and the distance between the last opening edge and the end support. The proposed model shows a strong potential as an effective design tool. The maximum and average relative errors among the 3645 data points were found to be 3.7% and 0.4%, respectively, whereas the average computing time per data point is smaller than a millisecond for any current personal computer.

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Section: Introductionmentioning

confidence: 99%

Neural Network-Based Formula for the Buckling Load Prediction of I-Section Cellular Steel Beams

Abambres¹,

Rajana

Tsavdaridis

et al. 2018

Computers

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“…One of the most important uncertainties in case of the Széchenyi Chain Bridge is the corrosion grade. In the international literature there are numerous deterministic and stochastic corrosion models available [6][7][8], which have been developed for different steel grades and environmental conditions. Authors considered corrosion using a time-dependent statistical model taken from the international literature using real measurement data as input parameters.…”

Section: Numerical Modeling and Design Aspectsmentioning

confidence: 99%

“…Authors considered corrosion using a time-dependent statistical model taken from the international literature using real measurement data as input parameters. In the current numerical model, the corrosion model developed by Paik and Kim [7] in 2012 is applied, which was originally developed for a marine environment and extended for steel bridges by Tohodi and Sharifi [6] in 2016. In this corrosion model the damage grade is considered by the Weibull-type density function given by equation (3.1):…”

Section: Numerical Modeling and Design Aspectsmentioning

confidence: 99%

“…where: d c is the corrosion depth, α is the shape factor, β is size factor, Y e = Y − Y c ; Y is the age of the bridge, Y c is the age of the corrosion protection. Further details and the considered input parameters are given in [6]. This model can consider the change in the effectiveness of corrosion protection within the lifetime of the structure.…”

Section: Numerical Modeling and Design Aspectsmentioning

confidence: 99%

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Damage assessment of the historical Széchenyi Chain Bridge

Dunai¹,

Kövesdi²

2020

JCAM

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The Széchenyi Chain Bridge is a 170-year-old historical structure located in the downtown of Budapest. The superstructure of the bridge was reconstructed several times in its history and currently the renewal process of the bridge is under consideration. According to the current plans main girders, chain elements and cross-girders will remain the old structure and the deck system will be replaced by a new orthotropic steel deck. The Budapest University of Technology and Economics, Department of Structural Engineering was involved in the design process and in the assessment of the remaining elements’ condition within the last 5 years. During the project authors were faced with numerous specific important and challenging structural problems, modelling specialties, advanced design methods and research interest. The main part of these unusual characteristics come from the layout of the historical structure, long time traffic and corrosion problems. One of the most important questions during the structural analysis is the condition and rotational capacity of the pins between the chain elements. The chain system is more than 100 years old and the rotational capacity of the pins is questionable due to corrosion and friction. This phenomenon significantly influences the static behaviour of the chain elements and the whole suspending system. The current paper presents the numerical and on-site experimental program on the investigation of the rotational capacity of the pins. A second important question was related to the condition of current deck system. Significant corrosion damage was observed on the steel stringers which might cause damage or local collapse of the bridge deck under public transportation loads. Advanced numerical model using probabilistic analysis (FORM) and measurement based corrosion models are applied to make a risk assessment of the deck system's capability to maintain and keep the current traffic on the bridge before the deck will be replaced. Via this bridge inspection and investigation project the authors would like to demonstrate the application of advanced numerical modelling based design techniques and the industrial application of research models for lifetime assessment and risk analysis of historical structures.

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“…Although paint coating is often used to prevent steel structures from being corroded, for steel members located in harsh environments such as coastal areas, or due to deicing salts and chemical plants, corrosion occurs inevitably as the spalling of the coating [9]. This may lead to decrease of cross-section dimensions, change the slenderness ratio, cause stress concentration, and consequently reduce the bearing capacity of the structures [10]. Therefore, corrosion is a major threat to their structural durability and safety performance.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Properties of Corroded High Performance Steel Specimens Based on 3d Scanning

2019

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An experimental study was performed to investigate the impact of corrosion on the mechanical properties of high performance steel (HPS) specimens. Fifteen HPS specimens were designed and subjected to electrochemically accelerated corrosion. The geometric features of the specimens were quantified by using 3D scanning technology. Then, tensile tests were employed to study the mechanical properties of the corroded HPS specimens. The relationship between the mechanical properties of the corroded HPS specimens and corrosion damage was discussed in detail. Meanwhile, a three-stage constitutive model was proposed to evaluate the mechanical behaviors of the degraded HPS specimens. Finally, a finite element model based on 3D scanning technology was developed and verified to investigate the stress distribution and failure of the tensile specimens. Results show that the maximum cross-sectional loss ratio is a suitable parameter for describing the mechanical properties of the degraded HPS specimens. The failure modes of the HPS specimens gradually vary from ductile to brittle as varies from 0% to 50.28%. It is found that the yield or ultimate load decreases linearly as the .increases. The corrosion-induced ductility reduction has an exponential relationship with. A comparison between the numerical results and experimental results shows that the proposed three-stage constitutive model is rational.

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Load-carrying capacity of locally corroded steel plate girder ends using artificial neural network

Cited by 73 publications

References 41 publications

Neural Network-Based Formula for the Buckling Load Prediction of I-Section Cellular Steel Beams

Neural Network-Based Formula for the Buckling Load Prediction of I-Section Cellular Steel Beams

Damage assessment of the historical Széchenyi Chain Bridge

Mechanical Properties of Corroded High Performance Steel Specimens Based on 3d Scanning

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