Modeling of chloride-induced corrosion in reinforced concrete structures

Zhang, Juhui; Cheung, Moon‐Tong

doi:10.1617/s11527-012-9914-2

Cited by 8 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Establishment Of Prediction Models Based On Bpnn Gmmentioning

confidence: 99%

“…In GM (1, N) and OGM (1, N) modeling, crack width, corrosion time, and corrosion current with a greater correlation were included in the correlation sequences, and GM (1,3) and OGM (1,3) models were established. Te GM (1,3) model predicted the parameters to be G � [a, ρ 2 , • • • , ρ n ] T � [−0.1001, 1.2176e − 6 , 0.0272, 0.0298] T . After repeated trials, when c � 0.05, the ftting and prediction results of the OGM (1, 3) model were ideal, and the calculated prediction parameters are as follows:…”

Section: Establishment Of Prediction Models Based On Bpnn Gmmentioning

confidence: 99%

“…In recent years, theoretical and empirical models have been proposed to estimate the extent of corrosion of steel bars after rust swelling and cracking of concrete structures. Bazant [ 2 ] and Zhang and Cheung [ 3 ] proposed a prediction model for the extent of steel corrosion according to its physical and chemical processes. Isgor and Razaqpur [ 4 ], Zhang et al [ 5 ], Zheng et al [ 6 ], and Xu et al [ 7 ] conducted simulation tests in the laboratory to establish an empirical model of the corrosion rate of steel bars changing with environmental temperature, humidity, and other parameters.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Prediction of Steel Bar Corrosion Based on BP Neural Networks or Multivariable Gray Models

Liu

Xue-wei

2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The corrosion of steel bars in concrete has a significant impact on the durability of constructed structures. Based on the gray relational analysis (GRA) of the accelerated corrosion data and practical engineering data using MATLAB, a back propagation neural network (BPNN) model, a multivariable gray prediction model (GM (1, N)), and an optimization multivariable gray prediction model (OGM (1, N)) of steel corrosion were established by using a sequence of the key affecting factors. By comparing the prediction results of the three models, it is found that the GM (1, N) model has larger fitting and prediction errors for steel corrosion, while the OGM (1, N) model has smaller prediction errors in the accelerated corrosion data; the BPNN model offers more accurate predictions of the practical engineering data. The results show that the BPNN and OGM (1, N) models are all suitable for the prediction of steel bar corrosion in concrete structures.

show abstract

Section: Establishment Of Prediction Models Based On Bpnn Gmmentioning

confidence: 99%

Section: Establishment Of Prediction Models Based On Bpnn Gmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Prediction of Steel Bar Corrosion Based on BP Neural Networks or Multivariable Gray Models

Liu

Xue-wei

2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…e radial corrosion-induced displacement is applied to each hole to simulate the expansion induced by the steel bar corrosion products. is method is the closest to reality and most commonly used [20,21]. e corrosion-induced displacement is fitted by a nonuniform corrosion model whose outer profile of corrosive products is illustrated in Figure 9(b) [20,22,23].…”

Section: Program For Numerical Simulation For Performancementioning

confidence: 99%

“…is research was funded by the National Natural Science Foundation of China (No. 51778215), the Scientific and Technological Development Projects of FSDI [17][18][19][20][21][22][23][24][25], and the Shaanxi Key Scientific and Technological Innovation Team Plan (2015KCT-01).…”

Section: Acknowledgmentsmentioning

confidence: 99%

Performance Degradation of Subsea Shield Tunnel Segment Accounting for Concrete Strength Loss and Steel Bar Corrosion

Guo

Yang

et al. 2021

Shock and Vibration

View full text Add to dashboard Cite

Subsea shield tunnels usually serve in a typical corrosive marine environment. Under the action of chloride penetration and carbonization, tunnel lining segments are often damaged because of concrete strength loss and steel bar corrosion induced concrete cracking during their service life, which seriously degrades the service performance of the tunnels. A systematical experimental and numerical investigation into the performance degradation of subsea shield tunnel segments accounting for concrete strength loss and steel bar corrosion is presented in this paper. The study demonstrates that chloride penetration decreases the peak strength and elastic modulus of the segment concrete by 42% and 46.1%, respectively. The average of the ratio of dissipated energy to the total energy of dry concrete is much smaller than that of water saturated concrete and chlorine solution saturated concrete, and chloride penetration reduces the energy storage capacity of concrete, and the ability to resist damage is weakened. When steel bars corrode for 120 days, the outer cracks continue to extend, and the concrete around the inner steel bars just begin to crack initiation; when corrode for 20 years, the length of the inner cracks gradually exceeds that of the outer cracks, and the inner cracks initiating from different steel bars coalesce with each other and form a continuous failure surface, causing great serious damage to the segment. Due to the difference in concrete strength, for the outer layer, the evolution processes of steel bar corrosion-induced cracks show the characteristics of early initiation, slow propagation, and late coalescence, and those for the inner layer have the characteristics of late initiation, rapid propagation, and early coalescence. During the whole process the propagation speed of the inner and outer cracks appears to be fast first and then slow. Moreover, the study also illustrates that the final state of segment performance degradation after crack coalescence presents the characteristics of whole lamellar exfoliation of the concrete cover.

show abstract

Finite element modeling of concrete cover crack propagation due to non-uniform corrosion of reinforcement

Zhang

Ling

Guan

2017

Construction and Building Materials

View full text Add to dashboard Cite

Modeling of chloride-induced corrosion in reinforced concrete structures

Cited by 8 publications

References 15 publications

The Prediction of Steel Bar Corrosion Based on BP Neural Networks or Multivariable Gray Models

The Prediction of Steel Bar Corrosion Based on BP Neural Networks or Multivariable Gray Models

Performance Degradation of Subsea Shield Tunnel Segment Accounting for Concrete Strength Loss and Steel Bar Corrosion

Finite element modeling of concrete cover crack propagation due to non-uniform corrosion of reinforcement

Contact Info

Product

Resources

About