Rectangular Reinforced Concrete Columns Research Articles

Plastic hinge behavior of rectangular CFRP-confined RC columns: Meso-scale modelling and formulation

The ultimate rotation capacity, depending on the plastic hinge length, is a vital factor in the seismic design of flexural members. This paper focuses on the investigation of the plastic hinge length and size effect of rectangular carbon fiber-reinforced polymer (CFRP)-confined reinforced concrete (RC) columns. Firstly, considerable scatter and evident bias of the calculation results were found after evaluating existing models for calculating the plastic hinge length of FRP-confined RC columns. Accordingly, a meso-scale numerical approach was developed, with concrete heterogeneity considered, to investigate the influences of the confinement ratio and corner radius on the plastic hinge length and size effect of rectangular CFRP-confined RC columns. Results show that columns having different cross-sectional sizes exhibit similar failure patterns, and the region of lateral crack propagation for columns with lower confinement ratios is larger than that for columns with higher confinement ratios. The actual plastic hinge length is determined based on the yielding zone of reinforcement, the crushing zone of concrete, and the localization zone of curvature, while the localization zone of curvature is employed to determine the real plastic hinge zone. The size effect is found to exist in the plastic hinge length ratio, and it is weakened with the increase of the corner radius, which can be attributed to the fact that columns with higher corner ratios have more effective confinement. Taking into account the size effect, a new model for calculating the ultimate drift ratio of CFRP-confined RC columns was proposed based on an assumption of curvature distribution and presented accurate predictions.

Cyclic performance of non‐ductile reinforced concrete columns retrofitted by partial steel plate jacketing: Experiment and numerical analysis

AbstractThis paper investigates the strengthening of non‐ductile reinforced concrete (RC) columns using partial steel‐plate jacketing to enhance ductile capacity. Quasi‐static cyclic loading tests were conducted on three rectangular RC columns with varying shear‐span‐to‐depth ratios, evaluating the effectiveness of steel‐plate jacketing. Results show improved ductile flexural response, high drift capacity, and maintained gravity‐loading capacity under high lateral displacements. A numerical investigation, adopting a multi‐spring fiber section model, predicted cyclic lateral strength, drift ratio, and failure mode, accurately simulating reinforcement behavior and capturing the shear‐strength enhancement due to steel‐plate jacketing. Numerical results demonstrated good agreement with experimental findings. Based on these outcomes, a simple design procedure for retrofitted concrete columns, applicable to design engineers, has been developed and reported in this paper. This study contributes valuable insights into the practical applications of steel‐plate jacketing for non‐ductile RC column retrofitting, offering a comprehensive approach to predicting their seismic performance.

Shear capacity model of rectangular RC columns based on Bayesian update

Reinforced concrete (RC) columns are function as structural member resisting vertical loading. The combined effects of vertical loading and transverse loading might induce shear failure of RC columns, posing a significant threat to the structure's integrity. While the accuracy of existing models to determine the shear strength of RC column is crucial for design, there is a need for further refinement. In this context, a study introduces a probabilistic shear capacity model based on the truss-arch theory and incorporating the Bayesian theory Monte Carlo Markov Chain (MCMC) method. This model delineates the shear contribution into four distinct components: concrete, stirrup, axial compression force and longitudinal reinforcement longitudinal reinforcement in the middle of the section parallel to the loading direction. A database, comprising 233 RC column specimen sets, facilitates the model's regression analysis. Calibration is achieved against the density distribution and confidence intervals of the posterior parameters. When set against four established models, the new proposal offers a mean value closely aligned with experimental data and showcases reduced variance. To substantiate the shear mechanism of the proposed model, a thorough contribution of each shear component's contribution is made with those in four existing models: Sezen, ACI 318, Priestley, and GB50010. While the Sezen and ACI 318 models recognize the shear contributions of concrete and stirrup, the Priestley and GB50010 models extend to factor in the beneficial impact of the axial compression force component. Comparative analysis underscores the superior predictive capability of the newly proposed model.

Stress and strain relations of RC circular, square and rectangular columns externally wrapped with fiber ropes

This study explores the potential use of low-cost natural fiber reinforced rope polymers (FRRP) to improve the compressive behavior of circular, square, and rectangular reinforced concrete (RC) specimens. A total of 42 specimens were tested under monotonic axial compression in three groups. Groups were formed to differentiate specimens with different cross-sectional shapes such as circular, square, and rectangular. The findings demonstrate that FRRP can effectively boost the compressive behavior of RC columns. Circular specimens with three-layer hemp FRRP exhibited a 200% increase in compressive strength and a 270% improvement in corresponding strain. Cotton FRRP provided a 117% boost in compressive strength and a 233% enhancement in strain. In square specimens, three-layer hemp FRRP resulted in a 110% rise in compressive strength and a 186% increase in strain, while cotton confinement yielded improvements of 95% and 144%, respectively. For the square and rectangular specimens, the improvement in the compressive behavior was reduced compared to the circular specimens because of stress concentrations near corners. Moreover, the study showed that the hemp FRRP confinement outperformed the cotton confinement. The investigation also revealed that the existing analytical models were inadequate in predicting the mechanical properties of RC confined with natural FRRP. Therefore, the study introduces novel equations to predict the compressive strength and corresponding strain for both hemp and cotton confined concrete in various cross-sectional types. These proposed equations exhibit a good level of accuracy in predicting the compressive strength and corresponding strain.

Prediction model and sensitivity analysis of ultimate drift ratio for rectangular reinforced concrete columns failed in flexural-shear based on BP-Garson algorithm

Considering that the deformation capacity of reinforced concrete (RC) columns is crucial in performance-based seismic design, the ultimate drift ratio of RC columns failed in flexural-shear are studied using BP neural network and Garson (BP-Garson) algorithm. Firstly, this study employed data onto tests conducted on 35 rectangular RC columns with flexural-shear failure. Nine influencing factors including axial load ratio, shear span ratio, longitudinal reinforcement configuration, transverse reinforcement configuration, and material strength were selected as variables, a BP neural network was used to establish the relationship between the ultimate drift ratio and these variables. Accordingly, a predictive model for the ultimate drift ratio of RC rectangular columns failed in flexural-shear was constructed. Then, the Garson algorithm for local sensitivity analysis was applied to determine the sensitivity of each influence of the ultimate drift ratio of RC rectangular columns failed in flexural-shear. The results showed that the BP neural network could accurately predict the ultimate drift ratio of RC rectangular columns with flexural-shear failure, of which the primary influencing factors are shear span ratio, axial load ratio, volume transverse reinforcement ratio and longitudinal reinforcement ratio. The BP-Garson algorithm integrates model development with influence analysis, which could improve computational efficiency and serves as a reference for evaluating the sensitivity of seismic performance and its influencing factors in other structural components.

Strengthening Reinforced Concrete Rectangular Columns with Various Slenderness Ratios by Fiber Reinforced Polymer (FRP)

Strengthening Reinforced Concrete Rectangular Columns with Various Slenderness Ratios by Fiber Reinforced Polymer (FRP)

Random Forests Machine Learning Applied to PEER Structural Performance Experimental Columns Database

Columns play a very important role in structural performance and, therefore, this paper contributes to the critical need for failure mode prediction of reinforced concrete (RC) columns by exploring the capabilities of random forest machine learning (ML) based on a well-known experimental column database. Known as the PEER structural performance database, it assembles the results of over 400 cyclic, lateral-load tests of reinforced concrete columns. The database describes tests of spiral or circular hoop-confined columns, rectangular tied columns and columns with or without lap splices of longitudinal reinforcement at the critical sections. The efficiency towards the aforementioned goal of supervised ML methods such as random forests using a randomly assigned test set from the Pacific Earthquake Engineering Research Center (PEER) database is examined here. The overall accuracy score for rectangular RC columns is 94% and for circular RC columns is 86%. The latter performances are influenced by the size of the testing and training sets of data and are independent of the number of decision trees in the forest employed in the random forest algorithm. The performances of random forests in postdicting the failure mode of RC columns prove that ML has great promise in revolutionizing the profession of earthquake engineering.

Failure Mode Identification and Shear Strength Prediction of Rectangular Hollow RC Columns Using Novel Hybrid Machine Learning Models

Failure mode identification and shear strength prediction are critical issues in designing reinforced concrete (RC) structures. Nevertheless, specific guidelines for identifying the failure modes and for accurate predictions of the shear strength of rectangular hollow RC columns are not provided in design codes. This study develops hybrid machine learning (ML) models to accurately identify the failure modes and precisely predict the shear strength of rectangular hollow RC columns. For this purpose, 121 experimental results of such columns are collected from the literature. Eight widely used ML models are employed to identify the failure modes and predict the shear strength of the column. The moth-flame optimization (MFO) algorithm and five-fold cross-validation are utilized to fine-tune the hyperparameters of the ML models. Additionally, seven empirical formulas are adopted to evaluate the performance of regression ML models in predicting the shear strength. The results reveal that the hybrid MFO-extreme gradient boosting (XGB) model outperforms others in both classifying the failure modes (accuracy of 93%) and predicting the shear strength (R2 = 0.996) of hollow RC columns. Additionally, the results indicate that the MFO-XGB model is more accurate than the empirical models for shear strength prediction. Moreover, the effect of input parameters on the failure modes and shear strength is investigated using the Shapley Additive exPlanations method. Finally, an efficient web application is developed for users who want to use the results of this study or update a new dataset.

Machine vision‐based automated earthquake‐induced drift ratio quantification for reinforced concrete columns

SummaryThis paper presents a novel method for estimating the seismic peak interstory drift ratio (IDR) in reinforced concrete (RC) columns after an earthquake using surface crack image analysis. The quantitative representation of the complexity and irregularity of crack images in damaged RC columns is obtained through the consideration of the generalized fractal dimensions. The authors have compiled a comprehensive database consisting of 445 crack maps obtained from cyclic experiments conducted on 110 rectangular RC column specimens exhibiting double‐curvature deformation mode. This database is utilized by the authors to develop and validate the proposed procedure. The research database contains a wide range of structural and geometric features. Five closed‐form equations are developed with the objective of estimating the peak IDR experienced by the RC columns during a seismic event. The predictive equations are derived through the utilization of symbolic regression technique, with the input parameters varying according to the availability of columns characteristic parameters. Results reveal that generalized fractal dimensions, especially D−1, are strong vision‐based indicator of damage in RC columns having correlation coefficients with IDR ranging from 0.82 to 0.92 across the considered plans. The seismic peak IDR obtained through the empirical equations can serve as the input engineering demand parameter (EDP) in the seismic loss estimation frameworks. This allows for the determination of the probability of exceeding damage states for structural and nonstructural components of concrete buildings. Finally, the practical implementation of the methodology is examined by its application to an actual case of a damaged column during the Kermanshah earthquake of magnitude 7.3 that occurred in 2017.

Assessment of plastic hinge length in reinforced concrete columns

Plastic hinge properties are crucial parameters in predicting the nonlinear response of structural elements. Because of the intricate material nonlinearity, precise determination of the plastic hinge length (PHL) has encountered several obstacles. Over the last few decades, there have been various definitions and models put forth to forecast this length, nevertheless, the outcomes displayed significant disparities. Therefore, the paper introduces a comprehensive method for determining PHL using certain criteria, including rebar strain profiles, concrete cover and core peak strains, curvature profiles, and damage observations. Furthermore, a set of four full-scale reinforced concrete (RC) columns measuring 400 mm × 400mm × 3000mm and featuring varying transverse reinforcement configurations were constructed and subjected to testing under a high axial load ratio (ALR). The tested results implied that it is necessary to separate the PHLs based on different criteria. The high axial compression load led to enhancing the PHLs, which were based on rebar compressive yield strains, curvature profiles, concrete cover and core peak strains. In contrast, it has a minor effect on PHL based on tensile yield strains. In addition, the amount of transverse reinforcement had an insignificant effect on all PHLs for tested columns. Hence, a revised equation was proposed to estimate the equivalent PHLs of rectangular RC columns based on tested results and a 114-column database. The proposed equation had better accuracy compared with some other model results in the literature.

Rehabilitation of post‐heated rectangular reinforced concrete columns using different strengthening configuration

AbstractIt is common knowledge that increased temperatures have a negative impact on the axial load‐capacity and stiffness of reinforced concrete (RC) rectangular columns. This is something that is commonly acknowledged. For the purpose of restoring the axial capacity and stiffness of heat‐damaged RC columns, it is absolutely essential to carefully pick a suitable method for strengthening and repairing the columns. This research paper investigates the performance of various strengthening methods, such as the use of carbon fiber‐reinforced polymer (CFRP) jackets with near‐surface mounted (NSM) steel bars and a combination of welded wire mesh (WWM) and CFRP strengthening techniques, for the rehabilitation of RC rectangular columns that have been damaged due to exposure to high temperatures. Specifically, the paper focuses on the performance of these methods in relation to the rehabilitation of RC rectangular columns. There were three groups of tested RC rectangular columns: pre‐heated and unstrengthened, post‐heated and unstrengthened, and post‐heated and strengthened. Over a period of 3 h, 600°C was applied to the heated columns. Both a scheme utilizing a combination of unidirectional CFRP strips and NSM steel bars and another utilizing a combination of unidirectional CFRP strips and WWM were evaluated. Axial compression tests were performed on each column until failure occurred. The experimental test outcomes revealed that exposure to high elevated temperatures led to a decrease in the axial load‐carrying capacity of 38.6% and a decrease of 75.9% in initial stiffness, compared to unheated RC columns. Both strengthening schemes proved effective in restoring and surpassing the initial load‐carrying capacity of undamaged RC rectangular columns by an average of 35%. Notably, the combination of CFRP strips and WWM exhibited superior performance in restoring the initial stiffness of post‐heated RC rectangular columns, representing a novel finding. The strengthening scheme employing WWM and CFRP recovered 61.3% of the lost axial strength caused by high temperature, while the NSM/CFRP scheme restored 34.5% of the lost axial strength. In addition, the utilization of NSM in combination with CFRP strips as well as utilization of the WWM and CFRP systems leads to stiffness enhancement of 52.1% and 123.7%, respectively, when compared to post‐heated and unstrengthened RC columns. However, the study's findings have clearly shown that combination of WWM and CFRP strips is efficient in enhancing the strength of heated and unheated damage RC beams.

Case studies on determining fire resistance of uniaxial bending reinforced concrete columns subjected to 4-side standard fire exposure

This article introduces technical specifications and calculation instructions for fire resistance of uniaxial bending rectangular reinforced concrete (RC) columns under 4-side heating by standard fire. Although a number ofresearch works have been conducted following the Eurocodes to meet the long-term requirement of the country’s development in construction, it is shown that the Russian design standard SP 468.1325800.2019 (SP 468) is also applicable for concrete structures subjected to fire in the Vietnam current context. Tabulated data and simplified calculation methods according to SP 468 are introduced, followed by two practical case studies for demonstration. The calculation results show that by using the simplified calculation method the RC columns’fire-resistant limit can be explicitly determined with a particular safety factor ku, T, and a more economical design solution can be obtained compared to that of the tabulated data method. Additionally, RC columns loaded with small eccentricities exhibit greater fire resistance advantages compared to those with large-eccentricity loading. A number of other useful observations are also discussed in the latter part of this article.

Shear Strength of Circular and Rectangular Reinforced Concrete Columns

Shear Strength of Circular and Rectangular Reinforced Concrete Columns

Comparison of Strength-Assessment Methods for Shear-Critical Reinforced Concrete Rectangular Columns

Comparison of Strength-Assessment Methods for Shear-Critical Reinforced Concrete Rectangular Columns

Investigation of Lateral Load Capacity of Rectangular Reinforced Concrete Columns Strengthened with GFRP Bars Wrapped with CFRP Helices

Investigation of Lateral Load Capacity of Rectangular Reinforced Concrete Columns Strengthened with GFRP Bars Wrapped with CFRP Helices

Assessment of Shear Strength Models of Reinforced Concrete Columns

Shear strength is a crucial parameter in designing Reinforced Concrete (RC) columns considering the effects of lateral loads such as wind or earthquakes. Numerous design codes and published studies have proposed equations for calculating the shear strength of RC columns. However, a discrepancy exists between the calculated models and the experimental results. The aim of this study is to evaluate the calculated models for the shear strength of rectangular RC columns based on 735 data sets, obtained from the literature. Six code-based and empirical models are investigated in this paper. The four code-based models include ACI 318 (2014), CSA (2014), Eurocode 8 (2005), and FEMA 273 (1997), and the two empirical models are proposed by Ascheim & Moehle (1992) [8] and Sezen & Moehle (2004) [9]. The shear strengths of RC columns are calculated for the six models using inputs from the experimental database. Finally, the results are evaluated using statistical indicators, including coefficient of determination and root-mean-squared error. The results reveal that Eurocode 8 (2005) is the best model, followed by Sezen & Moehle (2004) and Canada CSA (2014) since the results of those models are close to the experimental ones and shown to be more conservative than the others.

Parameter identification of hysteresis model of reinforced concrete columns considering shear action

It is necessary to determine the load-displacement hysteresis relationship of the reinforced concrete (RC) columns to accurately predict the nonlinear dynamic response of RC columns during earthquakes. In this paper, 17 rectangular RC columns were tested under horizontal cyclic loading and the hysteresis characteristics of RC columns considering shear action were obtained. Then, the influences of Bouc-Wen-Baber-Noori (BWBN) restoring force model parameters on the hysteresis loops of RC columns were analyzed. Based on the quasi-static test data of rectangular RC columns failed in flexural-shear and shear collected from the PEER-Structural Performance Database and the tests in this paper, a parameter identification method for the BWBN model of RC columns was proposed. After that, the probability distribution and the statistical value of the major influencing parameters of BWBN model for shear-dominated RC columns were calculated. The results show that the BWBN model can accurately predict the hysteresis loops of RC columns under cyclic loading and describe the hysteresis characteristics of shear-dominated RC columns, such as strength, stiffness degradation, and pinch effect. The proposed method can identify BWBN model parameters for shear-dominated RC columns well, and the main distribution range of strength degradation parameter δv, stiffness degradation parameter δη, and pinch effect parameter ζs increase gradually with the failure mode of RC columns from flexural-shear failure to shear failure, indicating that the degradation and pinch effect of RC columns failed in shear is more significant than that of flexural-shear. The presented approach is also appropriate for other components or structures, which lays a foundation for further seismic dynamic response analysis.

Modeling of size effect on compressive behavior of CFRP-confined rectangular RC columns: Influence of corner radius and aspect ratio

Taking into consideration the concrete heterogeneity, a three-dimensional meso-scale approach was established to model the failure of axially loaded rectangular reinforced concrete (RC) columns confined with carbon FRP (CFRP). This meso-scale method was first verified by the available test data. The investigation on the influences of edge sharpness from 0.07 to 0.20 and aspect ratios of 1.0, 1.5 and 2.0 on compressive behavior of CFRP-confined rectangular RC columns with different cross-sectional heights (300 mm, 600 mm, and 900 mm) was performed using the verified numerical model. Similar failure model was observed from the geometrically-similar columns, while a significant size effect was exhibited in the compressive strength. As the structural size increased, the effective confinement area ratio calculated from axial stress distribution decreased, illustrating that the confinement of CFRP jackets on concrete existed a size effect. Moreover, as the edge sharpness increased, the size effect on confinement was weakened, and this weakening effect was reduced with the aspect ratio increasing. Finally, considering the influence of the new arch action model and structural size, a modified model was proposed to predict compressive strengths of rectangular RC columns strengthened with CFRP.

Review of the key parameters of the international calculation guidelines for non-circular RC columns confined with CFRP composites. Comparison with medium and large-scale tests

Several international guides offer recommendations for the calculation and design of reinforcements with fibre reinforced polymer (FRP) composites. To strengthen axially-loaded columns, the guides adopt design stress–strain models based on extensive research into small-scale specimens of plain concrete confined with FRP. However, through this type of testing, it is difficult to accurately predict the behaviour of existing full-scale reinforced concrete (RC) columns confined with FRP composites.This paper presents a review of the formulation proposed by four international guides. The study compiles the available test results on medium and large-scale rectangular RC columns confined with carbon FRP composites subjected to axial compression concentric load.The predictions of the axial strength and the ultimate strain of the FRP-confined concrete are compared to the experimental results. The minimum confinement ratio (CR) recommended in the guides to guarantee sufficient confinement level is also analysed, alongside the estimation of the effective strain in the CFRP jacket at failure. The results obtained have proven uneven.The best predictions of the concrete strength improvement have been obtained with the ACI and fib guides. In general, to comply with the minimum CR proposed by the guides fails to prevent a softening stress–strain response, frequent in the rectangular tests analysed. The more restrictive CR criteria (from the CNR and fib guide) have shown themselves to be more suitable.The ultimate strain of the confined concrete is underestimated by the guides and the errors obtained are much higher than in the prediction of the strength enhancement ratio.Despite the widespread dispersion of the results, the mean value of strain efficiency factor obtained in the tests is similar to the value of 0.55 recommended by ACI. This parameter is not easy to determine for rectangular columns, but its estimation is essential for the calculation.

Machine Learning Models for Predicting Shear Strength and Identifying Failure Modes of Rectangular RC Columns

The determination of shear strength and the identification of potential failure modes are the crucial steps in designing and evaluating the structural performance of reinforced concrete (RC) columns. However, the current design codes and guidelines do not clearly provide a detailed procedure for governing failure types of RC columns. This study predicted the shear strength and identified the failure modes of rectangular RC columns using various Machine Learning (ML) models. Six ML models, including Multivariate Adaptive Regression Splines (MARSs), Naïve Bayes (NBs), K-nearest Neighbors (KNNs), Decision Tree (DT), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were developed to calculate the shear strength and to classify the failure modes of rectangular RC columns. A total of 541 experimental data samples were collected from literature and utilized for developing the ML models. The results reveal that the ANN and KNNs models outperformed other ML models in predicting the shear strength of rectangular RC columns with the R2 value larger than 0.99. Additionally, the KNNs model achieved the highest accuracy, mostly 100%, for identifying the failure modes of rectangular RC columns. Based on the superior performance of the ANN and KNNs models, a graphical user interface was also developed to rapidly predict the shear strength and failure modes of rectangular RC columns.