Corroded Reinforced Concrete Research Articles

Numerical Modeling of Uniaxial Corroded Reinforced Concrete Columns Exposed to Fire

Corroded concrete structures remain at risk of fire damage throughout their lifespan. This study explores the fire resistance of reinforced concrete columns, considering the simultaneous impact of corrosion and high temperatures. Thermal–structural models of the corroded concrete columns are developed using SAFIR software (2022). The numerical results are compared with published test data on temperature distributions and axial displacement–time curves. Then, parametric analyses are conducted to investigate the influence of various factors, such as corrosion degree, concrete compressive strength, cover thickness, and fire exposure models, on the fire performance of the concrete columns. The findings reveal that corrosion significantly undermines fire resistance: notably, columns with severe corrosion exhibited a 47% reduction in fire resistance. Conversely, increased concrete strength can bolster the fire resistance of intact columns, particularly when the concrete cover is minimal. Enhancing the cover thickness proves to be an effective strategy to mitigate the thermal degradation of steel reinforcements, thereby extending the columns’ fire resistance by as much as 23%. The study introduces coefficients to quantify the effects of corrosion, fire exposure, material strength, and cover thickness, culminating in a practical formula to calculate the fire endurance of corroded reinforced concrete columns. This formula could complement existing fire safety regulations.

Comprehensive Review on the Flexure Behaviour of Corroded Reinforcement Concrete Beams Under Sustained Loads

This study presents a comprehensive review of the flexural behaviour of corroded Reinforced Concrete (RC) beams subjected to sustained loads. The investigation synthesizes extant literature to elucidate the complex interaction between concurrent stress and steel corrosion in RC members. Emphasis is placed on the critical necessity of conducting corrosion tests under continuous stress conditions to accurately simulate in-situ structural behaviour. The review encompasses previous numerical studies on deteriorated RC beam performance and provides a critical analysis of historical loading regimes designed to mitigate corrosion in loaded RC beams. Notably, the literature reveals conflicting findings regarding the influence of loading on corrosion rates and crack propagation, highlighting areas necessitating further research. The review also considers the effects of creep, shrinkage, and stress, with particular emphasis on long-term deflection characteristics. This comprehensive analysis aims to consolidate current knowledge and identify critical research gaps in understanding the flexural behaviour of corroded RC beams under sustained loads, thereby providing a foundation for future investigations in this domain.

Analysis of Bonding Properties of Corroded Reinforcement Concrete

In order to investigate the degradation of bonding properties between corroded steel bars and concrete, this study employs the half-beam method to conduct bond-slip tests between corroded steel bars and concrete after impressed-current accelerated corrosion of the steel bars in concrete. The effects of steel corrosion rate, steel bar diameter, steel bar strength grade, and concrete strength grade on the bonding properties between concrete and corroded steel bars were analyzed. The influence of different corrosion rates on specimens’ bonding strength and bond-slip curves was determined, and a constitutive relationship for bond-slip between corroded steel bars and concrete was proposed. The results indicate that the ultimate bonding strength of corroded reinforced concrete specimens decreases with increasing corrosion rate. Additionally, an increase in corrosive crack width leads to a linear decrease in bonding strength. Evaluating the decline in adhesive properties through rust expansion crack width in engineering applications is feasible. Furthermore, a bond-slip constitutive relationship between corroded steel bars and concrete was established using relative bond stress and relative slip values, which aligned well with the experimental findings.

Calculation of Characteristic Point Parameters for Restoring Model of Corroded Short-Pier RC Shear Walls

Based on the quasi-static tests of 12 corroded RC (reinforced concrete) shear walls, it was found that reinforcement corrosion has a great influence on the skeleton curve of RC shear walls. With an increase in the degree of corrosion, the bearing capacity of specimens decreases, and the deformation capacity worsens. Increasing the diameter of longitudinal reinforcements can significantly improve the bearing capacity of corroded RC shear walls, while the deformation capacity of corroded specimens can be improved by increasing the lateral distributed reinforcement or the transverse reinforcement in the embedded column. In order to accurately evaluate the seismic performance of corroded RC shear walls, we considered descent segments of four broken-line models to estimate the skeleton curve. After considering the influence of corrosion on the parameters of the characteristic point for the skeleton curve, the calculation formulas of the characteristic point parameters of the skeleton curve for the corroded RC shear wall were determined based on the test data fitting. It was proven that the formula for the characteristic point parameters for the skeleton curve of corroded RC shear walls has good applicability. This study lays a theoretical foundation for the seismic performance evaluation of an RC shear wall structure in a salt fog environment. It provides a theoretical basis for further improving the life-cycle seismic capacity evaluation system for RC structures.

Hybrid Machine learning Techniques-Aided design of corroded reinforced concrete beams

Shear strength (SS) is an essential component in the design of reinforced concrete structural elements, particularly in severe settings where reinforcements can occur and cause a loss in SS. The purpose of this work is to develop a framework for predicting the SS and optimal design of corroded reinforced concrete (CRCo) beams by using a machine learning (ML) approach. The model employed was Gradient Boosting (CGB), optimized using three metaheuristic algorithms. By optimizing the CGB model with the Hunger Games Search (HGS) algorithm, the study achieved the best ML model for predicting the SS of CRCo beams, with an R2 value of 0.996. The proposed model outperformed five other empirical SS models for CRCo beams, and sensitivity, partial dependence analyses were also conducted to explore the impact of various variables on SS. Finally, this work provided guidance on selecting appropriate beam sizes and corrosion rates for optimal CRCo beams design solutions.

Comparative study of models predicting punching shear capacity of strengthened corroded RC slab-column joints

ABSTRACT Degradation in reinforced concrete (RC) members associated with rebar corrosion in old buildings is one major concern that can significantly affect the strength and serviceability of RC structures. Strengthening existing structures using FRP can comprise complex assessment and design processes. This study addresses the critical issue of modeling the behavior associated with steel reinforcement corrosion in RC slab-column joints (SCJs). Three modeling methods, namely artificial neural network (ANN), Eureqa’s symbolic regression algorithm, and analytical modeling, are proposed to predict the punching shear (PS) capacity of corroded RC SCJs by accounting for the changes in material properties due to corrosion and the strengthening provided by different types of FRP. The accuracy of the proposed models is validated through comparisons with experimental results and a verified finite element (FE) model that considers material deterioration and bond degradation. The results demonstrate that the proposed models offer more accurate PS capacity predictions than existing design codes. Among the considered prediction models, the ANN model exhibits superior performance and is recommended for PS capacity prediction. A practical design tool in the form of a graphical user interface (GUI) was created to facilitate the assessment and prediction of the PS behavior of corroded RC SCJs. Engineers can conveniently use this GUI to evaluate and predict the performance of such joints.

A Novel SMFL‐Based Assessment Method for Corrosion Nonuniformity of Rebar and Its Application in Reliability Analysis of Corroded RC Beam

The reliability of corroded reinforced concrete (RC) structures relies on the accurate minimum cross‐sectional area of corroded rebar. In this study, the accurate morphologies and self‐magnetic flux leakage (SMFL) field strengths of twenty‐eight non‐uniformly corroded rebars were obtained using 3D structural light scanning and micromagnetic detection technologies, based on which three indices of the SMFL field variation ratio dH, the corrosion non‐uniformity degree dSn, and the cross‐sectional area ratio K0.25 are proposed. The statistical results show that the probability densities of dSn and K0.25 obey the Weibull distribution and Gamma distribution at the 95% confidence level, respectively, and their distribution parameters are linearly or inversely proportional to dH. The probability density distribution of the minimum cross‐sectional area of corroded rebar can be determined using indices dSn and K0.25, based on which a feasible SMFL‐based reliability assessment method of corroded RC structures is proposed. The case study of a real specific corroded RC beam shows that the reliability assessment error of the SMFL‐based method is only 1.2%, which is much lower than the 20.7% error of the existing method. This SMFL‐based method provides a novel idea that can automatically and accurately assess the effect of rebars’ corrosion non‐uniformity on the reliability of specific in‐serviceRC structures.

A Simplified Approach to Determine Shear Strength for Corroded RC Beams

Corrosion damage, which can be considered as a construction or usage defect during the life of the structure, is an important parameter for the structural elements. Strength loss is observed in reinforced concrete (RC) elements due to corrosion, which is an important parameter affecting the performance of the building. Determining the shear strength of beams with corroded reinforcement is important in terms of strength loss, design, and reinforcement criteria in the structural element. In this context, the data of the corroded RC beam experimental tests carried out in the literature were collected and the ultimate shear strength values of the beams were determined depending on the test parameters. Strength estimation was performed with machine learning regression algorithms XGBoost and AdaBoost. The results obtained were evaluated with R2, RMSE and MAE performance metrics and high estimation success was achieved. The study shows that with these systems, which can perform learning based on experimental data, it is possible to estimate the shear strength values of corroded beams with known production parameters without the need for experimental measurements.

Investigation of Corroded Bond-Slip Effects for Corroded RC Columns

Steel bar corrosion is one of the most common causes of structural performance degradation for reinforced concrete (RC) columns subjected to extreme loads such as earthquakes. Due to the different characteristics of corrosion effects, a comprehensive and efficient finite element (FE) modeling approach is necessary for the performance assessment of corroded RC columns. To this end, this study provides an effective FE modeling approach using a recently developed geometrically nonlinear fiber-based frame element that considers bond-slip, allowing for explicit representation of corrosion-affected bonding. This aspect necessitates the use of an explicit material model for corroded bond-slip and its associated corroded properties. Since the existing models of corroded bonding properties in the literature were developed based on the highly scattered and limited experimental data, this study develops a new probabilistic model for corroded bonding properties based on more experimental data compiled from the literature and quantifies the prevailing uncertainties. To facilitate its use for modeling corroded RC structures, a cyclic corroded bond-slip model in an open-source FE software framework is implemented. In this paper, the FE modeling approach is first used to model two corroded RC columns tested in the literature, with comparison to the conventional modeling approach (i.e., assuming perfect bonding). The influence of corroded bonding on the static and dynamic behaviors of RC columns is further explored deterministically. Furthermore, the impact of uncertainty in corroded bonding properties on the static and dynamic behavior simulations of RC columns is examined. This work is concluded that (1) the FE modeling approach provided for corroded RC columns proves to be effective in capturing corrosion effects; and (2) corroded bonding plays an important role in simulating the behavior of corroded RC columns, and thus cannot be neglected. This is particularly true considering its associated uncertainty and its impact on the performance assessment of RC columns under severe corrosion conditions.

Nonlinear Finite Element Analysis of Corroded Reinforced Concrete Beams

Corrosion in reinforcing bars is a major cause of concern for sustainability of Reinforced Concrete (RC) structures exposed to extreme environmental conditions. Various RC structures, such as bridges and buildings are adversely affected due to the rebar corrosion, reducing its global load carrying capacity and ductility. In order to ensure an earthquake resistant design of any structure, the ductility of structural members is of chief concern. However, corrosion of reinforcing steel in RC members reduces its seismic performance. Thus, for safety and economic viability of any RC structure, an effective maintenance and repair work strategy is necessary. Based on the strong column - weak beam design concept of RC frames, the beams are the first structural members to absorb damage due to earthquake. Thus, to carry out maintenance and repair work, structural health assessment of RC beams considering the effect of reinforcement corrosion is of utmost importance. The present study aims at establishing a relation between the amount of corrosion induced and its effect on the ductility of an RC beam. A 3D Finite Element (FE) model of an RC beam including the effects of corrosion for nonlinear performance assessment has been modelled in ABAQUS. The FE model is validated with past experimental study including the effects of corrosion. Modelling of uniform corrosion in reinforcement bars involves defining the loss of bond between rebars and concrete, inducing spalling stresses on concrete and reduction in the cross-section area of the rebars. Bond-slip model between surface of concrete and reinforcement has been incorporated through cohesive surface. Spalling stress for uniform corrosion is applied in form of outward uniform pressure on the reinforcement and concrete interface. The cracking of cover concrete due to corrosion along with the flexural cracking in concrete due to incremental loading are implemented together in the present study. A comprehensive FE model is developed in order to obtain correct assessment of corroded beams. As observed from the FE model, the failure mechanism of the considered corroded RC beam is observed to be mostly shear failure as the corrosion level increases. Concrete crushing at the center of the RC beam is identified at the failure point of the beam due to corrosion induced damage. Evolution of displacement ductility index with respect to corrosion is determined for an RC beam based on the numerical study. It has been observed that the displacement ductility index of the beam is reduced significantly after considering a corrosion level of about 12%. Hence, the present study is an effort to establish a relation between structural performance of RC beams and level of reinforcement corrosion, which will be useful in proactive maintenance and repair work for corroded RC beams.

Evaluation of residual flexural strength of corroded reinforced concrete beams using convolutional long short-term memory neural networks

The artificial corrosion process is distinct from the natural environment; thus, posing a challenge in the evaluation of the structural performance of reinforced concrete (RC) structures in reality. This study aims at bridging this gap by addressing the effect of the natural corrosion process on the residual flexural strength of beam structures in many aging RC buildings. In particular, the field inspection data on RC beams are collected from various aging RC structures in Ho Chi Minh city. From this surveyed field data, the corrosion rate at the beginning of corrosion propagation is interpreted from the amount of diameter reduction of longitudinal rebar; the corrosion activity index is determined using a selected empirical model; the remaining moment-carrying capacity is calculated by integrating the surveyed data into empirical models. The reliability of the remaining flexural capacity proposed by this study is assessed by comparison with published experimental results. To facilitate automation in the assessment of the service life of RC beams, a robust soft computing model to predict the remaining moment-carrying capacity of corroded reinforced concrete (CRC) beams will be developed. To cope with the spatial and temporal variations of the natural corrosion process, a convolutional long short-term memory neural network (CLNN) is adopted thereof. In essence, the research gap between research outputs from the laboratory accelerated corrosion process to the actual project under the effect of the natural corrosion process is bridged; hence, a reliable and effective framework for evaluating the effect of the natural corrosion process on the moment-carrying capacity of RC beams is provided for engineering practice.

Structural performance deterioration of corroding reinforced concrete columns in marine environments

In aggressive marine environments, rebar corrosion caused by chloride ion ingression in concrete is a critical factor leading to premature failure of in-service reinforced concrete (RC) structures. This paper investigated the structural performance deterioration of marine RC structures affected by rebar corrosion, and proposed new analytical models for predicting the residual bearing capacity of the corroded RC columns. By analyzing the performance deterioration mechanisms of the corroded RC structures, the material degradation models and structural strength reduction models were constructed. Then, the analytical models for estimating the residual bearing capacity of the corroded RC columns under various loading conditions were proposed. From the proposed models, the moment-axial force (M-N) interaction diagrams were plotted for analyzing the influence of corrosion damage locations on the residual bearing capacity. Furthermore, the impact of marine environmental factors on the service life of the corroding RC columns under various loading conditions was investigated. Finally, a numerical example for the corroded RC columns was given to demonstrate the effectiveness of the proposed models. The results showed that the residual bearing capacity of the marine RC columns can be significantly affected by the rebar corrosion level, corrosion damage location, load condition and in-service environment.

Lifetime reliability analysis of concrete columns damaged by reinforcement corrosion

Rebar corrosion caused by chloride ion ingression is a critical factor leading to performance deterioration of in-service reinforced concrete (RC) structures exposed to aggressive environments. This paper presents a new method to evaluate the concrete crack growth, the bearing capacity degradation and the lifetime performance deterioration for the corroded RC columns under small eccentric compression. By analyzing the material degradation mechanism of the corroded RC structures, an effective method is proposed to predict the concrete crack growth and to estimate the residual bearing capacity of the corroding eccentrically loaded RC columns. Then, the concrete crack width and the load-bearing capacity deterioration rate are selected as structural performance indicators to assess the lifetime performance of the corroding RC columns. The stochastic gamma process is adopted for simulating the evolution of structural performance indicators and estimating lifetime failure probability and the remaining useful life of the corroding structures. Finally, a numerical example is given to demonstrate the effectiveness of the proposed methods for the lifetime performance assessment of the corroding columns. The obtained results show that the residual bearing capacity and the lifetime performance of the corroding RC columns can be significantly affected by rebar corrosion level, load condition and in-service environments.

Effect of Corrosion on the Hysteretic Behavior of Steel Reinforcing Bars and Corroded RC Columns

The corrosion of steel reinforcement negatively influences the mechanical performance of reinforced concrete (RC) elements reducing both their strength capacity and ductility. Especially in seismic prone areas, the degradation of the cyclic behavior of corroded RC elements is more intense, limiting the service life of structures and leading to premature failures. In the present paper, in order to study the degraded behavior of the embedded (in concrete) steel reinforcement under cyclic loading, laboratory corrosion experiments were performed on embedded steel reinforcing bars. Thereafter, mechanical tests under dynamic loadings with gradually increased deformations were carried out. From the experimental study, hysteretic models of the non-linear behavior of steel bars were extracted in non-corroded and corroded conditions, in the case of both uniform and pitting corrosion. Based on the hysteretic models of steel bars, an analysis of the cyclic response of (non-corroded and corroded) RC columns was performed using the OpenSees code. The outcomes of the present study indicated the negative consequences of corrosion on the hysteretic behavior of steel reinforcing bars, demonstrating mainly the local reduction in their cross-section (pitting corrosion) combined with the loading history and buckling phenomena as the main factors which deviate the mechanical behavior of steel bars from the classic bilinear model of monotonic loading.

Prediction of shear strength of corrosion reinforced concrete beams using Artificial Neural Network

The shear strength of corroded reinforced concrete (CRC) beams is a critical consideration during the design stages of RC structures. In this study, we propose a machine learning technique for estimating the shear strength of CRC beams across a range of service periods. To do this, we gathered 158 CRC beam shear tests and used Artificial Neural Network (ANN) to create a forecast model for the considered output. Twelve input variables indicate the geometrical and material properties, reinforcing parameters, and the degree of corrosion in the beam, whereas the shear strength is the output considered. The database is designed to employ 70 percent of the data point to train the model and 30 percent to assess the performance. The model makes outstanding predictions, according to the results, with an R2 value of 0.989. In addition, five empirical shear strength models in the literature are utilized to test the suggested ANN model, demonstrating that the new model performs much better. With any given service period, the suggested time-dependent prediction model can offer the shear strength of CRC beams.

Prediction of shear strength of corrosion reinforced concrete beams using Artificial Neural Network

The shear strength of corroded reinforced concrete (CRC) beams is a critical consideration during the design stages of RC structures. In this study, we propose a machine learning technique for estimating the shear strength of CRC beams across a range of service periods. To do this, we gathered 158 CRC beam shear tests and used Artificial Neural Network (ANN) to create a forecast model for the considered output. Twelve input variables indicate the geometrical and material properties, reinforcing parameters, and the degree of corrosion in the beam, whereas the shear strength is the output considered. The database is designed to employ 70 percent of the data point to train the model and 30 percent to assess the performance. The model makes outstanding predictions, according to the results, with an R2 value of 0.989. In addition, five empirical shear strength models in the literature are utilized to test the suggested ANN model, demonstrating that the new model performs much better. With any given service period, the suggested time-dependent prediction model can offer the shear strength of CRC beams.

Prediction of shear strength of corrosion reinforced concrete beams using Artificial Neural Network

The shear strength of corroded reinforced concrete (CRC) beams is a critical consideration during the design stages of RC structures. In this study, we propose a machine learning technique for estimating the shear strength of CRC beams across a range of service periods. To do this, we gathered 158 CRC beam shear tests and used Artificial Neural Network (ANN) to create a forecast model for the considered output. Twelve input variables indicate the geometrical and material properties, reinforcing parameters, and the degree of corrosion in the beam, whereas the shear strength is the output considered. The database is designed to employ 70 percent of the data point to train the model and 30 percent to assess the performance. The model makes outstanding predictions, according to the results, with an R2 value of 0.989. In addition, five empirical shear strength models in the literature are utilized to test the suggested ANN model, demonstrating that the new model performs much better. With any given service period, the suggested time-dependent prediction model can offer the shear strength of CRC beams.

Research on Seismic Resilience of Corroded Reinforced Concrete Structures Designed by China's Code

Research on Seismic Resilience of Corroded Reinforced Concrete Structures Designed by China's Code

A machine learning-based time-dependent shear strength model for corroded reinforced concrete beams

Shear strength of corroded reinforced concrete (CRC) beams is a key concern in the design and/or retrofit processes for an RC structure during its life-cycle. In this paper, we develop a machine learning (ML)-based approach for predicting the residual shear strength of CRC beams at different service times. To achieve this goal, we collected 158 shear tests of CRC beams and adopted one of the most representative ensemble ML algorithms, i.e., the gradient boosting regression tree (GBRT), to establish a predictive model for the shear strength. Six dimensionless variables indicating geometric dimensions, material properties, reinforcing details and the corrosion extent of the beam are set as the inputs, while the shear strength is set as the output. 70% of data are used as training set and the remaining 30% is used to evaluate the model performance. Results indicate that the model produces excellent predictions with an average R2 over 0.9. Moreover, five empirical shear strength models for CRC beams are also used for verifying the proposed GBRT model, which demonstrates that the proposed model has a distinct superior performance. Finally, a mechanical model is used to calculate the corrosion extent of reinforcements and thus a time-dependent shear strength prediction approach for CRC beams is developed based on the ML-based model. The proposed time-dependent prediction model is capable to provide the shear strength of CRC beams with any given service time.

Shear Strengthening of Corroded RC Beams Using UHPC–FRP Composites

Abstract In this paper, ultrahigh-performance concrete (UHPC) combined with fiber-reinforced polymer (FRP) composites is proposed for the shear strengthening of corroded reinforced concrete (RC) be...