• Steel and Composite Structures
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• v.11 no.3
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• pp.183-205
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• 2011

This paper presents the results of an investigation into the post-buckling behaviour and ultimate strength of imperfect corroded steel plates used in ship and other marine-related structures. A series of elastic-plastic large deflection finite element analyses is performed on randomly corroded steel plates. The effects of general corrosion on both sides of the plates are introduced into the finite element models using a random thickness surface model. The effects on plate compressive strength as a result of parametric variation of the corroded surface geometry are evaluated. A proposal on the effective thickness is concluded in order to estimate the ultimate strength and explore the post-buckling behaviour of randomly corroded steel plates under uniaxial compression.

• Steel and Composite Structures
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• v.18 no.6
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• pp.1479-1492
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• 2015

This study evaluated the localized stress condition of the real corroded deck surface of an orthotropic steel bridge because severe corrosion damage on the deck surface and fatigue cracking were reported. Thus, a three-dimensional finite element (FE) analysis model was created based on measurements of the corroded orthotropic steel deck surface to examine the stress level dependence on the corrosion condition. Based on the FE analysis results, it could be confirmed that a high stress concentration and irregular stress distribution can develop on the deck surface. The stress level was also increased by approximately 1.3-1.5 times as a result of the irregular corroded surface. It was concluded that this stress concentration could increase the possibility of fatigue cracking in the deck surface because of the surface roughness of the orthotropic steel bridge deck.

• Smart Structures and Systems
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• v.32 no.1
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• pp.23-35
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• 2023

Steel bolted joint is an important part of steel structure, and its damage directly affects the bearing capacity and durability of steel structure. Currently, the existing research mainly focuses on the identification of corroded bolts and corroded bolts respectively, and there are few studies on multiple states. A detection framework of corroded and loosened bolts is proposed in this study, and the innovations can be summarized as follows: (i) Vision Transformer (ViT) is introduced to replace the third and fourth C3 module of you-only-look-once version 5s (YOLOv5s) algorithm, which increases the attention weights of feature channels and the feature extraction capability. (ii) Three states of the steel bolts are considered, including corroded bolt, bolt missing and clean bolt. (iii) Line segment detector (LSD) is introduced for bolt rotation angle calculation, which realizes bolt looseness detection. The improved YOLOv5s model was validated on the dataset, and the mean average precision (mAP) was increased from 0.902 to 0.952. In terms of a lab-scale joint, the performance of the LSD algorithm and the Hough transform was compared from different perspective angles. The error value of bolt loosening angle of the LSD algorithm is controlled within 1.09%, less than 8.91% of the Hough transform. Furthermore, the proposed framework was applied to fullscale joints of a steel bridge in China. Synthetic images of loosened bolts were successfully identified and the multiple states were well detected. Therefore, the proposed framework can be alternative of monitoring steel bolted joints for management department.

• Journal of the Korea Concrete Institute
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• v.11 no.6
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• pp.129-135
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• 1999

Reinforced concrete structures are constructed under the basic assumption of perfect bonding between steel and concrete. The corrosion of steel in the reinforced concrete beams results in the excessive cracks and gradual deterioration of concrete. This paper are concerned about the slip characteristics of reinforced concrete between steel and concrete. The accelerated test by external power supply was conducted with the three corrosion rates in the laboratory. As a result, it was obtained as follows: (1) the yield strength of steel was reduced according to corrosion states. (2) the equivalent steel area should be considered for detailed analysis. (3) According to the use of corroded steel or not, slip amounts between concrete and steel in test beams increased as the corrosion rate increased. These results can be explained from the bond loss between concrete and steel in test beams.

• Steel and Composite Structures
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• v.35 no.5
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• pp.611-625
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• 2020

Experimental investigation and finite element analysis of corroded cold-formed steel (CFS) columns are presented. 11 tensile coupon specimens and 6 stub columns of corroded CFS that had a channel section of C160x60x20 were subjected to monotonic tensile tests and axial compression tests, respectively. The degradation laws of the mechanical properties of the tensile coupon specimens and stub columns were analysed. An appropriate finite element model for the corroded CFS columns was proposed and the influence of local corrosion on the stability performance of the columns was studied by finite element analysis. Finally, the axial capacity of the experimental results was compared with the predictions obtained from the existing design specifications. The results indicated that with an increasing average thickness loss ratio, the ultimate strength, elastic modulus and yield strength decreased for the tensile coupon specimens. Local buckling deformation was not noticeable until the load reached about 90% of the ultimate load for the non-corroded columns, while local buckling deformation was observed when the load was only 40% of the ultimate load for the corroded columns. The maximum reduction of the ultimate load and critical buckling load was 57% and 81.7%, respectively, compared to those values for the non-corroded columns. The ultimate load of the columns with web thickness reduced by 2 mm was 53% lower than that of the non-corroded columns, which indicates that web corrosion most significantly affects the bearing capacity of the columns with localized corrosion. The results predicted using the design specifications of MOHURD were more accurate than those predicted using the design specifications of AISI.

• Computers and Concrete
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• v.29 no.3
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• pp.145-159
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• 2022

In this study, the extreme learning machine and deep learning models were devised to estimate the bond strength of corroded reinforcement in concrete. The six inputs and one output were used in this study. The compressive strength, concrete cover, bond length, steel type, diameter of steel bar, and corrosion level were selected as the input variables. The results of bond strength were used as the output variable. Moreover, the Analysis of variance (Anova) was used to find the effect of input variables on the bond strength of corroded reinforcement in concrete. The prediction results were compared to the experimental results and each other. The extreme learning machine and the deep learning models estimated the bond strength by 99.81% and 99.99% accuracy, respectively. This study found that the deep learning model can be estimated the bond strength of corroded reinforcement with higher accuracy than the extreme learning machine model. The Anova results found that the corrosion level was found to be the input variable that most affects the bond strength of corroded reinforcement in concrete.

• Steel and Composite Structures
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• v.24 no.5
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• pp.635-641
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• 2017

Safety service improvement and development of efficient maintenance strategies for corroded steel structures are undeniably essential. Therefore, understanding the influence of damage caused by corrosion on the remaining load-carrying capacities such as tensile strength is required. In this study, artificial neural network (ANN) approach is proposed in order to produce a simple, accurate, and inexpensive method developed by using tensile test results, material properties and finite element method (FEM) results to train the ANN model. Initially in reproducing corroded model process, FEM was used to obtain tensile strength of artificial corroded plates, for which surface is developed by a spatial autocorrelation model. By using the corroded surface data and material properties as input data, with tensile strength as the output data, the ANN model could be trained. The accuracy of the ANN result was then verified by using leave-one-out cross-validation (LOOCV). As a result, it was confirmed that the accuracy of the ANN approach and the final output equation was developed for predicting tensile strength without tensile test results and FEM in further work. Though previous studies have been conducted, the accuracy results are still lower than the proposed ANN approach. Hence, the proposed ANN model now enables us to have a simple, rapid, and inexpensive method to predict residual tensile strength more accurately due to corrosion in steel structures.

• Steel and Composite Structures
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• v.10 no.6
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• pp.475-487
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• 2010

Most of steel structures in various industries are subjected to corrosion due to environmental exposure. Corrosion damage is a serious problem for these structures which may reduce their carrying capacity. These aging structures require maintenance and in many cases, replacement. The goal of this research is to consider the effects of corrosion by developing a model that estimates corrosion loss as a function of exposure time. The model is formulated based on average measured thickness data collected from three severely corroded I-beams (nearly 30 years old). Since corrosion is a time-dependent parameter. Analyses were performed to calculate the lateral buckling capacity of steel beam in terms of exposure time. Minimum curves have been developed for assessment of the remaining lateral buckling capacity of ordinary I-beams based on the loss of thicknesses in terms of exposure time. These minimum curves can be used by practicing engineers for better estimates on the service life of corrosion damaged steel beams.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.2
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• pp.40-48
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• 2012

Steel corrosion in concrete is a main cause of deterioration and early failure of concrete structures. A novel integration of electromagnetic heat induction and infrared (IR) thermography is proposed for nondestructive detection of steel corrosion in concrete, by taking advantage of the difference in thermal characteristics of corroded and non-corroded steel. This paper focuses on experimental investigation of the concept. An inductive heater is developed to remotely heat the embedded steel from concrete surface, which is integrated with an IR camera. Concrete samples with different cover depths are prepared. Each sample is embedded with a single rebar in the middle, resulting an identical cover depth from the front and the back surfaces, which enable heat induction from one surface and IR imaging from the other simultaneously. The impressed current (IC) method is adopted to induce accelerated corrosion on the rebar. IR video images are recorded during the entire heating and cooling periods. The test results demonstrate a clear difference in thermal characteristics between corroded and non-corroded samples. The corroded sample shows higher rates of heating and cooling than those of the non-corroded sample. This study demonstrates a potential for nondestructive detection of rebar corrosion in concrete.

• Steel and Composite Structures
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• v.46 no.3
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• pp.385-401
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• 2023

Steel is becoming increasingly popular due to its high strength, excellent ductility, great assembly performance, and recyclability. In reality, steel structures serving for a long time in atmospheric, industrial, and marine environments inevitably suffer from corrosion, which significantly decreases the durability and the service life with the exposure time. For the mechanical properties of corroded steel, experimental studies are mainly conducted. The existing numerical analyses only evaluate the mechanical properties based on corroded morphology at the isolated time-in-point, ignoring that this morphology varies continuously with corrosion time. To solve this problem, the relationships between pit depth expectation, standard deviation, and corrosion time are initially constructed based on a large amount of wet-dry cyclic accelerated test data. Successively, based on that, an in-situ pitting evolution method for evaluating the residual tensile strength of corroded steel is proposed. To verify the method, 20 repeated simulations of mass loss rates and mechanical properties are adopted against the test results. Then, numerical analyses are conducted on 135 models of corrosion pits with different aspect ratios and uneven corrosion degree on two corroded surfaces. Results show that the power function with exponents of 1.483 and 1.091 can well describe the increase in pit depth expectation and standard deviation with corrosion time, respectively. The effect of the commonly used pit aspect ratios of 0.10-0.25 on yield strength and ultimate strength is negligible. Besides, pit number ratio α equating to 0.6 is the critical value for the strength degradation. When α is less than 0.6, the pit number increases with α, accelerating the degradation of strength. Otherwise, the strength degradation is weakened. In addition, a power function model is adopted to characterize the degradation of yield strength and ultimate strength with corrosion time, which is revised by initial steel plate thickness.