Bar Development Length Research Articles

Flexural bond behavior and development length of ribbed CFRP bars in UHPFRC

Ribbed carbon fiber reinforced polymer (CFRP) bars in ultra-high-performance fiber reinforced concrete (UHPFRC) are a high-efficiency combination for its sustainable substitute and high strength to weight ratio. This paper aims to investigate the bond stress and development length of ribbed CFRP bar embedded in UHPFRC using the hinged beam test. A total of 12 groups of hinged beam specimens were tested with different parameters, such as bar type (ribbed CFRP, sand coated CFRP, ribbed glass fiber reinforced polymer (GFRP), and steel bar), bonded length (4db, 10db, 20 db, and 40 db), fibers (with and without fibers in ultra-high-performance concrete (UHPC)), and presence of shear reinforcement for assessing the bonding behavior and development length of each bar. Tensile failure occurred in the steel, ribbed GFRP, and ribbed CFRP bars with bonded lengths of 10db, 20db, and 40db, respectively, and pullout failure occurred in sand coated CFRP bars regardless of bonded length. The ultimate load for the bonded length of 10 db was in order of ribbed CFRP, ribbed GFRP, steel, and sand coated CFRP bars. Compared with the bonded length, the bond stress of ribbed CFRP bars is larger than that of the ribbed GFRP and sand coated CFRP bar and these trends became more remarkable as the bonded length increased. There was no difference in the bond stress with presence of the reinforcement cage, and the bond stress increased by 20 % when the fibers were incorporated. The code-based equation for development length of ribbed CFRP bar was found to be underestimated, and ribbed CFRP, sand coated CFRP, ribbed GFRP bar of development length was proposed as 24db, 20db, and 107db.

Bond and anchorage performance of beam flexural bars in beam-column joints using slag-based geopolymer concrete and their effect on seismic performance

An available study on the bond and anchorage performance of beam flexural bars in beam-column joints using slag-based geopolymer concrete (GC) is limited. The applicability of current design codes to beam flexural bars design in GC beam-column joints is unknown. In this study, to investigate the bond and anchorage performance of beam rebars at the GC beam-column joints and their effect on seismic performance, cyclic loading tests were performed on 2 reinforced GC interior beam-column joints and 6 reinforced GC exterior beam-column joints. The test results showed that the increase in column depth-to-beam rebar diameter ratio (hc/db) from 21 to 25 improved the bond performance of the beam flexural bars in the reinforced GC interior beam-column joints, which significantly improved the energy dissipation capacity. The limits of hc/db larger than 20 in GB50010-2010 (except seismic grade I that requires hc/db ≥ 25) and ACI 318-19 were not safe for reinforced GC interior beam-column joints. The limits of hc/db in Eurocode 8 and NZS3101 for low-strength concrete (fc′ ≤ 30 MPa) were applicable. In reinforced GC exterior beam-column joints, the bond and anchorage performance of 90˚ hooked bars was superior to that of the corresponding headed bars. Regardless of the anchorage details, the bond and anchorage performance of the reinforced GC exterior beam-column joints increased with the increase of the development length of beam flexural bars. In reinforced GC exterior beam-column joint design, the bar development length requirement of GB50010-2010 was applicable to both 90˚ hooked bars and headed bars. Eurocode 8, NZS3101, and ACI 318-19 were applicable to the development length of 90˚ hooked bars.

Reinforcing bar development length modeling using integrative support vector regression model with response surface method: New approach

Due to the non-uniformity of bond stress distribution, the full bar development length should be tested to validate the development length of the reinforcing bar embedded in concretes. The current study proposed the design of a hybrid artificial intelligence (AI) model using integration of support vector regression (SVR) coupled with response surface method (RSM) for prediction of reinforcement bar development length. Two nonlinear calibrating processes are conducted, RSM is used to connect the input data on the hardening dataset in first stage while SVR is used for determining the nonlinear relation between the hardening dataset and the output development bar stress. 534 pull-out test observations on short unit bar length are used for the modeling. Several physical dimensional properties are incorporated as input attributes for the hybrid predictive model. Stand-alone AI models, empirical formulations, and design codes are used for validation. Parametric analysis is performed to investigate the sensitivity of RSM-SVR model toward as input variables. Results evidenced the capability of the proposed RSM-RVM model to approximate the non-linear relations between physical information and bar development length. The RSM-SVR model proved to be a reliable intelligence model for bar materials design. Additionally, the model was highly consistent in the calibration of the existing design codes for more reliability. In quantitative terms, RSM-SVR model attained minimum root mean square error (RMSE = 25.60 MPa), compared with for example ACI 318-14 modeling design (306.56 MPa) or stand-alone SVR model (90.95 MPa), over the testing phase. The bar development length formulation using RSM-SVR model showed the nonlinear relationship with respect to the influence of the input variables such as transvers bars in development region, reinforcing yield stress, and concrete compressive strength in normal domain.

Evaluation of structural performance for beam-column joints with high-strength materials under cyclic loading using PIV technique

Current design codes require long bar development length and high joint hoop ratio in reinforced concrete (RC) exterior beam-column joints using high-strength materials, which reduces constructability. This study investigated the seismic behavior of RC exterior beam-column joints with insufficient joint hoops and using high-strength beam flexural bars (Grade 600 MPa) and/or high-strength concrete. Six half-scale exterior beam-column joints were tested under cyclic load reversals. The test parameters included the yield strength and diameter of beam flexural bars and the concrete strength. As an innovative measurement method, particle image velocimetry (PIV) technique was implemented to accurately measure the joint shear deformation, beam flexural deformation, plastic hinge length, surface strain fields, and crack patterns in the beam-column joint specimens. The PIV method predicted the location of beam cracks accurately. The test results showed that the use of high-strength bars increased bond-slip due to higher bond requirement, which decreased the energy dissipation capacity by 16% compared to the joint specimen using Grade 400 MPa bars. Further, the joint shear deformation and the beam crack width increased, while the plastic hinge length decreased. The combined use of high-strength concrete and high-strength bars improved the structural performance in terms of the energy dissipation capacity and initial stiffness.

Development of ANN-based models to predict the bond strength of GFRP bars and concrete beams

The use of glass fiber-reinforced polymer (GFRP) has gained increasing attention over the past decades, aiming at replacing traditional steel rebar in concrete structures, especially in corrosion or magnetic conditions. Understanding the working mechanism between the reinforcements and concrete is crucial in many practical applications, in which the corresponding bond strength is considered as a critical element. In this study, a database including 159 experimental beam results gathered from the available literature was used for the development of an artificial neural network (ANN) model in an effort to predict the bond strength between GFRP bars and concrete. Two ANN models using BFGS quasi-Newton backpropagation and conjugate gradient backpropagation with Polak-Ribiére algorithms were constructed and evaluated in terms of bond strength prediction accuracy. The considered database consisted of five input parameters, including the bar diameter, concrete compressive strength, minimum cover to bar diameter ratio, bar development length to bar diameter ratio, the ratio of the area of transverse reinforcement to the product of transverse reinforcement spacing, the number of developed bars and bar diameter. The evaluation of the models was conducted and compared using well-known statistical measurements, namely the correlation coefficient (R), root mean square error (RMSE), and absolute mean error (MAE). The results demonstrated that both ANN models could accurately predict the bond strength between GFRP bars and concrete, paving the way for engineers to possess a useful alternative design solution for reinforced concrete structures

Development of ANN-based models to predict the bond strength of GFRP bars and concrete beams

The use of glass fiber-reinforced polymer (GFRP) has gained increasing attention over the past decades, aiming at replacing traditional steel rebar in concrete structures, especially in corrosion or magnetic conditions. Understanding the working mechanism between the reinforcements and concrete is crucial in many practical applications, in which the corresponding bond strength is considered as a critical element. In this study, a database including 159 experimental beam results gathered from the available literature was used for the development of an artificial neural network (ANN) model in an effort to predict the bond strength between GFRP bars and concrete. Two ANN models using BFGS quasi-Newton backpropagation and conjugate gradient backpropagation with Polak-Ribiére algorithms were constructed and evaluated in terms of bond strength prediction accuracy. The considered database consisted of five input parameters, including the bar diameter, concrete compressive strength, minimum cover to bar diameter ratio, bar development length to bar diameter ratio, the ratio of the area of transverse reinforcement to the product of transverse reinforcement spacing, the number of developed bars and bar diameter. The evaluation of the models was conducted and compared using well-known statistical measurements, namely the correlation coefficient (R), root mean square error (RMSE), and absolute mean error (MAE). The results demonstrated that both ANN models could accurately predict the bond strength between GFRP bars and concrete, paving the way for engineers to possess a useful alternative design solution for reinforced concrete structures

Effect of Impact Load on Splice Length of Reinforcing Bars

Impact loading damage of reinforced concrete (RC) members deteriorates bond strength of reinforcing bars. To understand the effect of strain rate on the bond strength of reinforcing bars in RC beams under impact load, drop hammer test was performed on twenty-four simply supported RC beams with lap spliced bars at the mid-span. The test parameters were reinforcing bar diameter, splice length, drop height, and hammer mass. The dynamic responses including the impact load history, mid-span deflection history, crack distribution, and strain history of reinforcing bar were evaluated. Although the designed bar development length was 31–69% of the requirement of current design codes under static load, the tensile strength of bar splices was greater than the dynamic yield strength when subjected to large impact energy under impact load. On the basis of the test results, existing design equations for the bar development length under static load were modified to consider the impact loading effect on the bond strength. Factors related to the strain rate effect of materials, impact damage, and impact energy loss were proposed. The prediction of the proposed method agreed well with the tensile strength of bar splices under impact load.

Static and dynamic loading tests for precast concrete moment frames under progressive collapse

Precast Concrete (PC) building structures are weak to progressive collapse due to discontinuity of reinforcement at beam-column joints. In the present study, four static loading tests and two dynamic loading tests were performed to investigate the progressive collapse resistance of conventional PC moment frames. The test parameters were the connection detail, bar development length, beam section size, and rebar ratio. The structural performance, such as the load-carrying capacity, deformation capacity, failure mode, and rebar strain, were evaluated. The test results showed that the progressive collapse resistance was significantly affected by the connection detail and beam rebar ratio. Based on the static loading test results, dynamic loading with multi-stage load conditions was applied to two PC specimens. The progressive collapse resistance of the specimens under static and dynamic loadings was compared, and the effect of dynamic loading was discussed.

Effects of tensile reinforcing steel ratio and Near-Surface-Mounted bar development length on the structural behavior of strengthened RC beams

Abstract The use of near-surface mounted (NSM) Glass fiber reinforced polymer (GFRP) bars is one of the most popular and effective techniques for strengthening reinforced concrete (RC) beams. This paper presents an experimental research program to study the flexural strengthening of RC beams comparing two areas of bottom tensile reinforcing steel and three development lengths of NSM GFRP bars. The beam test results indicated that the beam flexural strength increased up to 110% and 58% for the cases of low and high tensile reinforcing steel ratios, respectively. The effect of the tensile reinforcing steel area on the critical value of the development length of NSM GFRP bars was also investigated. It was found that decreasing the axial stiffness ratio, reduced the strengthening efficiency and the critical development length of the NSM GFRP bars. Finally, a 3D Finite Element (FE) model using ANSYS was constructed and was validated using the experimental results. Good agreement was seen between experimental and FE model results.

Nonlinear modeling for bar bond stress using dynamical self-adjusted harmony search optimization

Design code provisions for reinforced concrete are often based on empirical relations resulting from simple statistical treatments of experimental data. Hence, they may provide inaccurate results for predicting complex structural behavior. In the present study, novel nonlinear regression for prediction of the reinforcing bar development length is developed using dynamical self-adjusted harmony search optimization. The nonlinear mathematical relations are regressed using 534 results of simple pullout tests on short unit bar lengths. A novel bi-nonlinear expression is proposed, and its predictive capability outperformed that of design code formulas such as the ACI 318-14, ACI 408R-03, and Eurocode 2 along with other existing empirical models. A parametric study was conducted to explore the sensitivity of the proposed models to influential input parameters. It was found that the new model offers a powerful predictive tool for reinforcing bar bond strength which differs from that of existing models that assume unrealistic uniform bond stress along the rebar. This flexible and data-intensive model could be further scrutinized for consideration in future design code revisions and enhancements.

Predicting reinforcing bar development length using polynomial chaos expansions

The bond stress of a reinforcing bar in a cementitious matrix varies along the bar length and is difficult to quantify. Thus, design code provisions refer to the concept of bar development length and rely on statistical analysis of rebar-pull-out test results. In the present study, a novel data-driven predictive model based on Polynomial Chaos Expansions (PCE) was developed to predict the reinforcing bar development length using 534 experimental results of simple pull-out tests on short unit bar lengths. The predictive capability of PCE was compared to that of other data-driven models, namely the Response Surface Method (RSM) and Artificial Neural Networks (ANN). Moreover, predictions of the PCE, RSM and ANN were further compared with calculations of three commonly used design code formulas (i.e., ACI 318-14, ACI 408R-03, and Eurocode 2) and predictions of two existing empirical models (i.e. Model Code 2010 and Hwang et al. model). A parametric study was conducted to explore the sensitivity of the proposed model to influential input parameters. It was found that the Polynomial Chaos Expansions model offers a powerful predictive tool for reinforcing bar bond strength. The model was able to capture trends that differ from that of existing models that assume unrealistic uniform bond stress along the rebar. This flexible and data intensive model for predicting rebar bond stress and full embedment length could offer an intelligent platform for accommodating new bar materials, new test data, and calibrating existing design provisions to keep design codes relevant.

Effect of impact loading on bar development length in CCT node

ABSTRACTCurrent design codes prescribe the development length of a reinforcing bar based on test results under static loading. In the present study, the development length of the reinforcement bar for reinforced concrete beams under impact loading was evaluated. Three-point impact loading test was performed on 16 partially bonded reinforced concrete beams to investigate the effect of loading rate on the bar development length. The test parameters were drop heights of the hammer, rebar diameter, and bar development length. The structural performance including the impact load, mid-span deflection, crack distribution, and rebar strain was evaluated. Although the bar development length was shorter than the design requirement under static loading, beam rebars were yielded under impact loading. For verification, a nonlinear numerical analysis result addressing the strain rate effect of concrete and reinforcing bars and the confinement effect of stirrups was compared with the test result. Further, considering the strain rate effect and nonuniform bond stress distribution model, a new design method was proposed to predict the bar development length under impact loading.

Bond strength prediction of steel bars in low strength concrete by using ANN

This paper presents Artificial Neural Network (ANN) models for evaluating bond strength of deformed, plain and cold formed bars in low strength concrete. The ANN models were implemented using the experimental database developed by conducting experiments in three different universities on total of 138 pullout and 108 splitting specimens under monotonic loading. The key parameters examined in the experiments are low strength concrete, bar development length, concrete cover, rebar type (deformed, cold-formed, plain) and diameter. These deficient parameters are typically found in non-engineered reinforced concrete structures of developing countries. To develop ANN bond model for each bar type, four inputs (the low strength concrete, development length, concrete cover and bar diameter) are used for training the neurons in the network. Multi-Layer-Perceptron was trained according to a back-propagation algorithm. The ANN bond model for deformed bar consists of a single hidden layer and the 9 neurons. For Tor bar and plain bars the ANN models consist of 5 and 6 neurons and a single hidden layer, respectively. The developed ANN models are capable of predicting bond strength for both pull and splitting bond failure modes. The developed ANN models have higher coefficient of determination in training, validation and testing with good prediction and generalization capacity. The comparison of experimental bond strength values with the outcomes of ANN models showed good agreement. Moreover, the ANN model predictions by varying different parameters are also presented for all bar types.

Experimental Investigation of Bond Characteristics of Deformed and Plain Bars in Low Strength Concrete

The use of inferior quality materials, inadequate detailing and poor construction practices are responsible for most of the brittle failure modes of non-engineered reinforced concrete structures. Bond failures in non-engineered reinforced concrete elements due to short anchorages or low concrete cover result in large slip deformations prevent the development of plastic deformations and reduce energy dissipation capacity. Until now, little work has been carried out that can lead to the development of bond-slip relationships for low strength non-engineered reinforced concrete structures. To address this, experiments have been carried out on pullout and splitting specimens under monotonic loading to investigate bond characteristics of typically used steel bars in non-engineered reinforced concrete structures. Various deficient parameters are considered in the experiments in order to develop multi-parameter bond strength relations for low strength concrete ≤15MPa. The key parameters examined in the experiments are low strength concrete, bar development length, concrete cover, re-bar types (deformed and plain) and re-bar diameter. This paper presents the experimental details and results which are further processed to develop bond strength equations for different bar types in low strength concrete. These equations can be used to define the bond-slip relation for conducting seismic vulnerability assessment of non-engineered structures.

Nonuniform Bond Stress Distribution Model for Evaluation of Bar Development Length

Nonuniform Bond Stress Distribution Model for Evaluation of Bar Development Length

Design and construction of UHPC-filled duct connections for precast bridge columns in high seismic zones

One of the column connection types that has been recently emphasised for accelerated bridge construction (ABC) in high seismic zones is grout-filled duct connections. Experimental results have shown that the seismic behaviour of reinforced concrete columns connected to precast cap beams using this connection type is emulative of conventional columns. However, an alternative ABC connection is required to connect precast columns to shallow cap beams and footings due to insufficient anchorage length in those elements. Ultra-high-performance concrete (UHPC) was proposed in this study to be used instead of conventional grout in the connection to reduce the anchorage length. First, fourteen large-scale pullout tests were performed to investigate the bond strength of UHPC-filled duct connections. The test results showed that the incorporation of UHPC instead of normal grout as duct filler can reduce the bar embedment length by 50% or more. Second, design equations were developed based on the pullout test data to compute the bar development length in these connections. Subsequently, two half-scale precast bridge columns utilising this connection type at the column base were tested to verify the design equations. Full column plastic moment was developed in both specimens under cyclic loading with no UHPC-filled duct connection failure such as bar pullout even under 12% drift ratio cycles. Finally, a guideline was proposed based on the pullout and column test data for the design and construction of the UHPC-filled duct connections to facilitate field deployment.

Artificial neural network and genetic programming for predicting the bond strength of GFRP bars in concrete

The bond strength between GFRP bars and concrete is one of the most important aspects in reinforced concrete structures and is generally affected by several factors. In this study, experimental data of 159 notched, hinged, splice and inverted hinged beam specimens from an existing database in the literature were used to develop artificial neural network (ANN) and genetic programming (GP). The data used in modeling are arranged in a format of seven input parameters that cover the bar position, bar surface, bar diameter (d b), concrete compressive strength (f c), minimum cover to bar diameter ratio (C/d b), bar development length to bar diameter ratio (l/d b) and the ratio of the area of transverse reinforcement to the product of transverse reinforcement spacing, the number of developed bar and bar diameter (A tr/snd b). The MAE of testing data was found to be less than 1.06 and 0.76 MPa for the proposed ANN and GP models, respectively. Moreover, the study concluded that the proposed ANN and GP models predict the bond strength of GFRP bars in concrete better than the multi-linear regression model and existing building code equations. A parametric analysis was also conducted using the developed ANN and GP models to establish the trend of the main influencing variables on the bond capacity. Many of the assumptions made by the bond design methods are predicted by the developed models; however, few are inconsistent with the developed models’ predictions.

SD500 철근용 충전식 슬리브 철근이음에 대한 해석 및 실험적 연구

Splice sleeves for HD25 and HD32 rebars with yield strength 500 MPa were studied experimentally and analytically. The shapes of sleeve was examined with nonlinear finite element analyses. A total of 18 specimens were tested with test variables of rebar types, sleeve lengths, mortar compressive strengths, and rebar development lengths. Three identical specimens per each variable were tested in order to prevent any test errors. After tests, numerical studies with a nonlinear finite element method were conducted to evaluate the test results. Experimental studies with 18 specimens showed that the sleeves of this study satisfies the code requirement. It was found that the strength of mortar and the bar development length within the sleeves did not affect to the load-carrying capacity of sleeves.

Lateral confining action of mortar-filled sleeve reinforcement splice

Of the various methods of splicing reinforcing bar in reinforced concrete structure, mortar-filled sleeve reinforcement splice offers diverse benefits, not only in terms of structural performance but also for the construction process. Consequently, after the mortar-filled sleeve splices have been developed in recent years, research and development on these splices has been actively carried out, in order to evaluate its macro structural performance, such as its strength and stiffness, with the aim of enabling this system to be applied to construction in the field as early as possible. However, to make a proper evaluation on the overall structural performance of the mortar-filled sleeve reinforcing bar splice, it is of critical importance to understand the lateral confining action of the sleeve, which is known to affect the bond strength between the embedded bar and mortar in the sleeve. Accordingly, in this study, an experiment of monotonic loading and cyclic loading was conducted with a full-sized mortar-filled sleeve splice attaching strain gauges on the sleeve surface with experimental variables such as development length of bar, etc. Based on the test results, the effect of the lateral confining action of the sleeve was analyzed and considered in terms of the bond strength between the bar and mortar in this splice.

Bond Characteristics of High-Strength Lightweight Concrete

Future construction of concrete floating platforms for offshore oil exploration and development off the East Coast of Canada may lead to a substantial increase in the use of high strength lightweight (HSLW) concrete in Canada. While HSLW concrete has been extensively used in other areas of the world such as Norway, its use to date in Canada has been limited. HSLW concrete with its improved durability and lightweight characteristics is a very much sought after material in the construction of concrete floating platforms. However, the efficient use of HSLW concrete in Canada is limited by the following two restrictions in the Canadian concrete design code A23.3 (2): first, in calculating the bar development length the maximum permissible value for the compressive strength of the concrete is limited to 64 MPa, secondly for lightweight concrete the minimum development length must be increased by 30%. The objective of this research was to determine the bond strength characteristics of 25 mm and 35 mm deformed reinforcement bars embedded in 80 MPa HSLW concrete and to assess whether or not the code restrictions are justified. -- The experiment consisted of performing a total of 72 pullout and push-in test to evaluate the bond behavior under both monotonic and cyclic loading. The effect of tension and compression along with various rates of loading were investigated for the monotonic tests as well as changing the rate of loading for the cyclic tests. Each of the specimens was confined with 10 mm stirrups, representing a well-confined member that is typical in an offshore floating platform. The concrete used in the experiment had an average compressive strength of 83.1 MPa. The results were evaluated and compared to the work by other researchers on high strength normal weight (HSNW) concrete as well as to the Australian, American and Canadian design codes. -- The results indicated that HSLW concrete behaves very similar to HSNW concrete and the maximum bond stress for HSLW concrete is greater than that of normal strength lightweight concrete. The bond stress versus displacement curve indicates a sharp nearly linear ascending portion of the curve followed by a steep descending portion indicating very brittle behavior, which is characteristic to high strength concrete. The cyclic tests indicated that cyclic loading does not have a significant effect on the bond strength provided that the maximum cyclic displacement is less than the peak load displacement in the monotonic test. A comparison of the test results to the various code equations indicates that the current codes are too conservative for HSLW concrete. In particular it is proposed that the concrete density modification factor for lightweight concrete in the Canadian design code be reduced from 1.30 to 1.10 for the case of HSLW concrete. Finally, it was determined that an expression based on the cubic root of the concrete compressive strength rather than the square root better describes the bond behavior of HSLW concrete.