Yield Strength Of Reinforcement Research Articles

Experimental and Numerical Simulation of Reinforced Concrete-Filled Square GFRP Tubular Columns under Axial Compression.

To investigate the axial compressive behavior of reinforced concrete-filled square glass-fiber-reinforced polymer(GFRP) tubular (RCFSGT) columns, 17 specimens were designed with variations in GFRP tube wall thickness, spiral reinforcement yield strength, and spiral reinforcement ratio. A detailed model was developed using the finite element software ABAQUS, enabling in-depth mechanistic analysis and expanded parameter studies. The results indicate that the failure types of the specimens are all manifested as GFRP square tube cracking, and the core concrete is subjected to crushing or shear failure. The inclusion of a reinforcement cage can significantly enhance the load-bearing capacity and ductility of the specimen. Furthermore, as the yield strength and reinforcement ratio of the spiral reinforcement increase, so does the load-bearing capacity of the specimen. The finite element simulation results align well with the experimental findings. As the wall thickness of the GFRP square tube increases from 2 mm to 6 mm, the load-bearing capacity improves by approximately 19.69%. With the yield strength of the spiral reinforcement rising from 200 MPa to 400 MPa, the specimen's load-bearing capacity shows an increase of approximately 7.55%. However, as its yield strength continues to increase, there is minimal change in the load-bearing capacity. When the stirrup ratio of spiral reinforcement rises from 0.33% to 2.26%, the specimen's load-bearing capacity experiences an increase of approximately 56.90%.

A novel resilient system of self-centering precast reinforced concrete walls with external dampers

The seismic vulnerability of concrete structures has prompted the development of innovative approaches to enhance earthquake resistance. Recent earthquakes have demonstrated the challenges of repairing concrete buildings damaged beyond feasible restoration. In response, research in structural engineering has increasingly focused on damage-resisting reinforced concrete walls, offering improved self-centering behavior and damage mitigation compared to traditional counterparts. Self-centering walls, employing a rocking mechanism, exhibit effective damage minimization and residual deformation reduction. This study introduces a robust approach to precast reinforced concrete walls, comprising a precast reinforced concrete shear wall integrated with an external energy dissipation component. The energy dissipation element, a reinforced concrete column attached externally on both sides of the wall, acts as a new damage-limiting component with enhanced energy dissipation capacity. Utilizing the general-purpose Finite Element (FE) software ABAQUS, a three-dimensional simulation of experimentally investigated self-centering precast reinforced concrete walls was developed. The finite element model was validated and subsequently employed to assess the performance of the proposed system under cyclic loading. Various design parameters of the reinforced concrete energy dissipation element, including cross-sectional area, wall height proportion, concrete strength, primary reinforcement yield strength, and reinforcement ratio, were investigated. Additionally, the suggested system underwent cyclic loading in multiple scenarios simulating subsequent earthquakes. The finite element analysis results indicate that the proposed method, with a well-designed energy dissipation element, ensures minimal damage, a controlled increase in lateral resistance, sufficient energy dissipation capacity, and the required resilience following successive seismic events.

CURVATURE DUCTILITYOF REINFORCED CONCRETE BEAM SECTIONS STIFFENED WITH STEEL PLATES

This paper presents theoretical parametric study of the curvature ductility capacity for reinforced concrete beam sections stiffened with steel plates. The study considers the behavior of concrete and reinforcing steel under different strain rates. A computer program has been written to compute the curvature ductility taking into account the spalling in concrete cover. Strain rate sensitive constitutive models of steel and concrete were used for predicting the moment-curvature relationship of reinforced concrete beams at different rate of straining. The study parameters are the yield strength of main reinforcement, yield strength of transverse reinforcement, compressive strength of concrete, spacing of stirrups and steel plate thickness. The results indicated that higher strain rates improve both the curvature ductility and the moment capacity of reinforced concrete beam sections. Moreover the section curvature ductility increases as the thickness of the stiffening plates decreases.

Enhancement of punching shear behavior of reinforced concrete flat slabs using GFRP grating

The literature review showed insufficient relevant research on the application of Glass-Fiber-Reinforced-Polymers (GFRP) gratings in the structural elements, while GFRP bars, laminate, sheets, and strips, have been extensively explored. This research aims to present a proposal for a new reinforcing system using GFRP gratings to improve the punching shear resistance of RC flat slabs. Results of seven specimens tested experimentally under vertical static loading are displayed, taking into account the influence of the gratings variables. Test results revealed an improvement in the ultimate load ranging between 9.03% and 27.67% for the specimens strengthened by the proposed GFRP grating system. A Nonlinear Finite Element Analysis (NLFEA) was carried out using the ANSYS program with correlational evaluation using load-deflection response and cracking pattern, which resulted in a good convergence of numerical simulations and experimental performance results ranging from 1.0% to 8.0%. Key parameters, namely the concrete compressive strength, steel reinforcement yield strength, main steel reinforcement ratio, secondary steel reinforcement ratio, column dimensions, slab thickness, concrete cover, and GFRP gratings characteristics, were investigated through a parametric study adopting NLFEA by the ANSYS program, where the output results were compared to the recent code provisions.

STATISTICAL EVALUATION OF MECHANICAL CHARACTERISTICS REINFORCEMENT OF CLASS A500С IN COILS

The paper presents the results of astatistical evaluation of the mechanical characteristics of reinforcement of class A500S in coils.The statistical evaluation was performed based on the results of tensile tests on 144 samples of 8 mm diameter rebar of A5000S class made of St3ps steel. The samples were taken from 8 coils of different batches of industrial production, 6 samples fromthe beginning, middle and end of each coil. During the tests, the main mechanical characteristics of A500C reinforcement were determined - yield strength (σТ), ultimate strength (σВ), ratio of ultimate strength to yield strength (σВ/σТ), relative elongation after rupture (δ5), state diagrams were drawn and the elastic modulus of the rebar (Es) was determined. The statistical evaluation was performed for each of the studied parameters (σТ, σВ,σВ/σТ, δ5) first separately for each skein for samples taken from the beginning, middle and end, then for all samples taken from the beginning, middle and end of all skeins, respectively, and finally forthe entire sample of 144 samples.According to the test data of samples from 8 coils, the yield strength of A500C reinforcement varied in the range of 532.80...631.10 MPa, the temporary resistance - 697.30...762.70 MPa, the relative elongation at break - 18.75...30.00 %, the ratioof temporary resistance to yield strength -1.185...1.344.No stable patterns of change in the mechanical characteristics of the reinforcement along the length of the coils were found. The difference between the average values of yield strength, temporary resistance, relative elongation, and the ratio of temporary resistance to yield strength along the length of the coils did not exceed 2 %.The values of the coefficients of variation and the range of the values of the corresponding mechanical characteristics of the samples taken from the beginning, middle and end of the coils were also close to each other.The yield strength of A500C rebar with a diameter of 8 mm in coils in this sample of samples was 0.9999, with a normalised value of 0.95. The coefficient of variation was 0.0328.For the established security of the calculated reinforcement resistance at the level of 0.998, the reliability factor for A500C class rebar with a diameter of 8 mm in coils was 1.109, which is less thanthe value of 1.15 accepted in the current regulatory documents.

Finite element investigation on the post-fire behavior of reinforced composite NSC-HPC slabs

Purpose Even though it is widely used, reinforced concrete (RC) is susceptible to damage from various environmental factors. The hazard of a fire attack is particularly severe because it may cause the whole structure to collapse. Furthermore, repairing and strengthening existing structures with high-performance concrete (HPC) has become essential from both technical and financial points of view. In particular, studying the postfire behavior of HPC with normal strength concrete substrate requires experimental and numerical investigations. Accordingly, this study aims to numerically investigate the post-fire behavior of reinforced composite RC slabs. Design/methodology/approach Consequently, in this study, a numerical analysis was carried out to ascertain the flexural behavior of simply supported RC slabs strengthened with HPC and exposed to a particularly high temperature of 600°C for 2 h. This behavior was investigated and analyzed in the presence of a number of parameters, such as HPC types (fiber-reinforced, 0.5% steel, polypropylene fibers [PPF], hybrid fibers), strengthening side (tension or compression), strengthening layer thickness, slab thickness, boundary conditions, reinforcement ratio and yield strength of reinforcement. Findings The results showed that traction-separation and full-bond models can achieve accuracy compared with experimental results. Also, the fiber type significantly affects the postfire performance of RC slab strengthened with HPC, where the inclusion of hybrid fiber recorded the highest ultimate load. While adding PPF to HPC showed a rapid decrease in the load-deflection curve after reaching the ultimate load. Originality/value The proposed model accurately predicted the thermomechanical behavior of RC slabs strengthened with HPC after being exposed to the fire regarding load-deflection response, crack pattern and failure mode. Moreover, the considered independent parametric variables significantly affect the composite slabs’ behavior.

Estimation of ductility of concrete columns under horizontal seismic load

AbstractThe ductility of concrete columns, which determined the deformability of concrete columns, was an important parameter in evaluating the safety of framework structures under horizontal seismic loads. In this paper, 231‐group test data for evaluating the ductility of concrete columns under seismic loads were collected to establish a reliable database. The effects of investigated parameters including the compressive strength of concrete, the lateral confinement of stirrups, the ratio and yield strength of longitudinal reinforcements, axial compression ratio, shear span ratio, and the ratio between gross area and confined concrete area on the ductility of concrete columns were analyzed. The accuracy of theoretical models for evaluating the ductility of confined concrete relied on the prediction performance for the plastic hinge length of the concrete column. The existing models for the plastic hinge length of concrete columns underestimated the test results, which should be re‐evaluated. The prediction models were developed to evaluate the ductility of concrete columns based on the parametric analysis, which had high prediction performance. In addition, the design ductility limited value of concrete columns under horizontal seismic loads was obtained.

Probabilistic curvature-and-drift limit states predictive models of high-strength bridge columns

This study presents the derivation of closed-form component limit state expressions for high-strength concrete columns reinforced with high-strength steel bars (HSC-HSSB) from an extensive database through the moment–curvature analysis approach. Initially, sensitivity analysis was conducted to investigate the regularity of the parameters (section size, axial-load ratio, longitudinal reinforcement ratio, stirrup reinforcement ratio, concrete compressive strength, and yield strength of longitudinal reinforcement) on the curvature limit state. The approach being convenient, massive samples were obtained in a short time, therefore, 6^7(279936) levels were generated to derive the predictive expressions of curvature and moment limit states by using the logarithm linearity model. Subsequently, based on the cross-section expressions, the drift displacement and shear force limit states predictive expressions were derived and verified by experimental studies, and these limit states were predicted with high precision. Moreover, after considering the uncertainty of material and section size, the curvature and drift displacement of the HSC-HSSB column exhibited a lognormal distribution, and the useful suggested standard deviations (βc) in different limit states were proposed for practical application of fragility curves to assess the vulnerability of bridge columns to earthquakes.

A data-driven approach for predicting interface bond strength between corroded reinforcement and concrete

The deterioration of interface bond strength of reinforcement and concrete in a corrosive environment is the primary cause of premature degradation that affects reinforced concrete structures. Thus, it is crucial to establish a highly reliable bond strength model of corroded reinforced concrete. For this study, we collected data from previous experimental investigations on the interface bond strength between corrosion reinforcement and concrete, and developed a BP-ANN model on the interface bond strength based on a data-driven approach. The model utilized input variables for instance corrosion level, reinforcement diameter, concrete cover to reinforcement diameter ratio, reinforcement diameter to bond length ratio, reinforcement yield strength and concrete compressive strength. The output target was interface bond strength, and the model was trained and tested by using 166 groups of pull-out test datas for corrosion reinforcements and concrete with varying corrosion rates. The outcomes demonstrate that the predictions of the BP-ANN model match well with actual values. Furthermore, the predictive efficiency for the BP-ANN model was verified by comparison of the predicted values with the actual values. A comparative analysis of the applicability and computational error of the BP-ANN model indicates that a data-driven approach to predict bond strength between reinforcement and concrete in an erosion environment has better prediction accuracy than the empirical or semi-empirical bond strength models used in previous studies. Additionally, a sensitivity analysis of the primary influencing factors on bond strength of corroded reinforced concrete is conducted, and it is quantitatively confirmed that the corrosion level was the most significant factor on bond strength, while the influence of reinforcement yield strength is comparatively small.

Experimental and numerical study on structural behavior of reactive powder concrete corbels without stirrups

This paper presents a study that explores the application of reactive powder concrete (RPC), a modern construction material, to corbels without stirrups. The focus of the research is to investigate the shear behaviors of RPC corbels under vertical loading through experimental and numerical methods. Three RPC corbels with different shear span-to-effective depth ratios (av/d) of 0.6, 1.1, and 1.6 were tested. A commercial finite element method (FEM) code was utilized for the numerical analysis, and its accuracy was verified against the experimental results. A parametric study was conducted to evaluate the influence of various factors, including RPC compressive strength, RPC tensile strength, amount of main reinforcement, yield strength of main reinforcement, and stress-strain model of main reinforcement. Additionally, the shear strengths obtained from the tests were compared with the predicted values based on design codes, including ACI 318–14 and NF P18–710. The findings reveal that the av/d ratio significantly affected the load-deflection behaviors and failure modes of the RPC corbel specimens. The numerical model demonstrates good agreement with the experimental results. The RPC compressive strength emerges as the primary factor influencing the peak load of RPC corbels. Comparing various predictions, the Strut-and-Tie models recommended by the NF code exhibit the highest accuracy and safety in estimating the shear strengths of the RPC corbels.

Modeling the load carrying capacity of corroded reinforced concrete compression bending members using explainable machine learning

Corrosion of reinforcement can lead to a decrease in the load carrying capacity of reinforced concrete structures and affect their safety. Therefore, accurate evaluation of the ultimate load carrying capacity is crucial for the maintenance and reinforcement of corroded reinforced concrete structures. In this paper, based on experimental research data of 192 corroded reinforced concrete compression bending members, data-driven analysis was conducted using ANN, SVM, RF, and AdaBoost algorithms to establish the relationship between the influencing factors of the load carrying capacity and their ultimate load carrying capacity. The input variables include the section width of the member, section height of the member, length of member, yield strength of reinforcement, diameter of longitudinal reinforcement, compressive strength of concrete, thickness of concrete cover, hoop diameter, original eccentricity, corrosion rate and the ultimate load carrying capacity is the output variable. Additionally, this study innovatively utilizes the Shapley additive explanations (SHAP) method to enhance the interpretability of the ML models, overcoming the "black box" issue associated with ML methods. Furthermore, the performance of the ML models is compared with theoretical formulas. The results indicate that the ML models exhibit good predictive performance, with higher accuracy than thetheoretical calculation formulas. And the predictive performance of ensemble learning models (RF, AdaBoost) is better than that of single learning models (ANN, SVM). The newly developed hybrid ML model is likely to become a new choice for dealing with the load carrying capacity problem of corroded reinforced concrete compression bending members.

Seismic performance of RC columns under combined cyclic flexural and constant axial loadings

In this study, an experimental test procedure is conducted to investigate seismic performance of reinforced concrete columns under earthquake loading. Earthquake loading is simulated by combination of constant axial load and reversing cyclic load. The main variables of the experimental program are selected to be dimensionless axial load level and the ratio of shear span to effective column depth. Test results are compared with the current suggestions and approximations used for performance-based design and/or evaluation of reinforced concrete (RC) columns under seismic loads. It is observed that section curvature plays an important role to express failure damage limit state under cyclic loading as well as to sudden decrease in load carrying capacity. Besides, two new simple suggestions are made for performance-based design and/or evaluation process of RC columns. As a first step of this suggestions, a relation between damage limit states and curvature ductility ratios of RC columns is defined. This relation is validated with the experimental results of test columns through damage index model based on section curvature. The proposed damage limit states vary according to damage level definition and based on buckling parameter in terms of diameter, buckling length and yield strength of longitudinal reinforcement. The second suggestion is made to relate damage limit state to decrease in shear resistance of columns. To this purpose, a model to express concrete contribution to shear strength due to the proposed damage limits is used. With these two new proposed equations, it is both aimed to obtain a damage limit state in terms of curvature ductility ratio and to control ductile failure mode in a simple yet effective way.

Metaheuristic Optimization of Random Forest for Predicting Punch Shear Strength of FRP-Reinforced Concrete Beams.

Predicting the punching shear strength (PSS) of fiber-reinforced polymer reinforced concrete (FRP-RC) beams is a critical task in the design and assessment of reinforced concrete structures. This study utilized three meta-heuristic optimization algorithms, namely ant lion optimizer (ALO), moth flame optimizer (MFO), and salp swarm algorithm (SSA), to select the optimal hyperparameters of the random forest (RF) model for predicting the punching shear strength (PSS) of FRP-RC beams. Seven features of FRP-RC beams were considered as inputs parameters, including types of column section (TCS), cross-sectional area of the column (CAC), slab's effective depth (SED), span-depth ratio (SDR), compressive strength of concrete (CSC), yield strength of reinforcement (YSR), and reinforcement ratio (RR). The results indicate that the ALO-RF model with a population size of 100 has the best prediction performance among all models, with MAE of 25.0525, MAPE of 6.5696, R2 of 0.9820, and RMSE of 59.9677 in the training phase, and MAE of 52.5601, MAPE of 15.5083, R2 of 0.941, and RMSE of 101.6494 in the testing phase. The slab's effective depth (SED) has the largest contribution to predicting the PSS, which means that adjusting SED can effectively control the PSS. Furthermore, the hybrid machine learning model optimized by metaheuristic algorithms outperforms traditional models in terms of prediction accuracy and error control.

Experimental, numerical, and theoretical study of punching shear capacity of corroded reinforced concrete slab-column joints

Rebars in reinforced concrete (RC) slab-column systems are susceptible to corrosion in unfavorable environments, making slab-column joints (SCJs) more prone to punching shear (PS) failure. In this study, experimental, numerical, and theoretical studies of the PS capacity of corroded RC SCJs were conducted. Four RC SCJ specimens were constructed, and their reinforcement bars were exposed to accelerated corrosion using an electrochemical process. Subsequently, monotonic load tests were performed to determine the PS capacity of the RC SCJs with various corrosion levels. The test results demonstrated that rebar corrosion significantly reduces the PS capacity of the SCJs. Based on this outcome, an extended numerical study is executed to get a broader understanding of the corrosion effect on RC SCJs. A finite element model was devolved using Abaqus software, and the accuracy of the model was validated by the results of the four experimental specimens in the current study. Additionally, experimental results of large-scale specimens from the literature were also used to validate the numerical simulation. A two-level factorial design (TLFD) was used to examine the main and interactive parameters influencing the PS capacity and the ultimate deflection of the corroded RC SCJs. The parameters that were considered are the corrosion ratio, corrosion area, column rectangularity, slab thicknesses, reinforcement ratio, compressive strength of concrete, yield strength of reinforcement, and supporting distance. Based on the TLFD, the numerical parametric study was performed. The results of the numerical study show that corrosion can change the failure mode of the joints from flexural failure to punching shear failure and significantly reduces the PS capacity. The results indicate that increasing the rebar corrosion ratio or the corroded zone size decreases the PS capacity of a corroded SCJ. While increasing the slab thickness reduces the negative effect of rebar corrosion on the PS capacity. In addition, increasing the reinforcement ratio has a slight impact on improving the PS capacity. However, increasing the column rectangularity decreases the PS capacity of a corroded RC SCJ significantly. Based on the results of the experimental and numerical studies, an analytical model was developed to predict the punching shear capacity of the corroded RC SCJ. The reliability of the developed model was verified through the comparison with four different code provisions: ACI 318–19, CSA 23.3–14, BS 8110–97, and Eurocode 2, and three empirical models from the literature.

Moment redistribution capacity of continuous RC beams with High-Strength steel reinforcement

Moment redistribution (MR), a nonlinear behavior occurring in continuous reinforced concrete (RC) beams, is related to the redistributed section's ductility and the stiffness distribution along the whole beam. The use of high-strength steel (HSS) reinforcement could affect the redistributed section’s ductility and can delay or even prevent the formation of plastic hinge after another. The lack of research regarding the influence of those phenomena on MR is one of the reasons that hinder design engineers from using HSS. This paper conducts a broad-range parametric study through finite element (FE) method to check if the design provisions on MR are still applicable to beams with HSS reinforcement of yield strength of more than 600 MPa. The critical sections' yield state (whether the plastic hinge is formed) at the ultimate limit state (ULS) and its influences on MR are investigated. The study demonstrations that the MR capacity decreases with the increase in yield strength of reinforcement, especially when the midspan section cannot yield at ULS due to the use of HSS reinforcement. Besides, the yield state is influenced by the coupling effect of multi-parameters and significantly affects the influence degree of each parameter on MR. A new method considering the yield state is proposed to estimate the MR capacity of the continuous beams. This method's reasonable accuracy is proved. It is recommended to reduce the specified MR by about 6% for every 100 MPa increase in yield strength of reinforcement.

Machine Learning-Based Flexural Capacity Prediction of Corroded RC Beams with an Efficient and User-Friendly Tool

Steel corrosion poses a serious threat to the structural performance of reinforced concrete (RC) structures. Thus, this study evaluates the flexural capacity of RC beams through machine learning (ML)-based techniques with six parameters used as input features: beam width, beam effective depth, concrete compressive strength, reinforcement ratio, reinforcement yield strength, and corrosion level. Four single and ensemble ML models are evaluated; namely, decision tree, support vector machine, adaptive boosting, and gradient boosting. Hyperparameters of each model were optimized using grid search and K-fold cross-validation with root mean squared error used as the performance index. The predictive performance of each model was assessed using four statistical performance metrics. The analysis results demonstrated that the decision tree model exhibited overfitting and limited generalization ability. The adaptive boosting model also had a slight overfitting issue. In addition, the support vector machine reported comparable accuracy to that of adaptive boosting. Conversely, the proposed gradient boosting ensemble model achieved the best performance with strong generalization ability, as indicated by its lowest mean absolute error of 2.78 kN.m, mean absolute percent error of 13.40%, and root mean squared error of 3.56 kN.m, and the highest coefficient of determination of 97.30% on the test dataset. The optimized gradient boosting model has been deployed into a graphical user interface, allowing for practical implementation of the model and enabling fast, efficient, and intelligent prediction of the flexural capacity of corroded RC beams.

Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams

The ability of machine learning (ML) techniques to forecast the shear strength of corroded reinforced concrete beams (CRCBs) is examined in the present study. These ML techniques include artificial neural networks (ANN), adaptive-neuro fuzzy inference systems (ANFIS), decision tree (DT) and extreme gradient boosting (XGBoost). A thorough databank with 140 data points about the shear capacity of CRCBs with various degrees of corrosion was compiled after a review of the literature. The inputs parameters of the implemented models are the width of the beam, the effective depth of the beam, concrete compressive strength (CS), yield strength of reinforcement, percentage of longitudinal reinforcement, percentage of transversal reinforcement (stirrups), yield strength of stirrups, stirrups spacing, shear span-to-depth ratio (a/d), corrosion degree of main reinforcement, and corrosion degree of stirrups. The coefficient of determination of the ANN, ANFIS, DT, and XGBoost models are 0.9811, 0.9866, 0.9799, and 0.9998, respectively. The MAPE of the XGBoost model is 99.39%, 99.16%, and 99.28% lower than ANN, ANFIS, and DT models. According to the results of the sensitivity examination, the shear strength of the CRCBs is most affected by the depth of the beam, stirrups spacing, and the a/d. The graphical displays of the Taylor graph, violin plot, and multi-histogram plot additionally support the XGBoost model's dependability and precision. In addition, this model demonstrated good experimental data fit when compared to other analytical and ML models. Accurate prediction of shear strength using the XGBoost approach confirmed that this approach is capable of handling a wide range of data and can be used as a model to predict shear strength with higher accuracy. The effectiveness of the developed XGBoost model is higher than the existing models in terms of precision, economic considerations, and safety, as indicated by the comparative study.

Research on Seismic Performance of 735 MPa High-Strength Reinforced Concrete Frame Joints

To study the seismic performance of high-strength reinforced concrete frame joints, cyclic loading tests were carried out on six concrete beam-column joints configured with the new Cold-rolled Ribbed Bar (CRB) 735 high-strength steel reinforcement and one joint with Hot-rolled Ribbed Bar (HRB) 400 ordinary steel reinforcement to obtain the effects of the axial compression ratio and the stirrup ratio in the joint core on the seismic performance of the joints. The test results show that the following: (1) Increasing the axial compression ratio can improve the energy dissipation characteristics of the joints configured with CRB735, but cannot effectively improve the ultimate shear capacity of the joints. Larger axial compression ratio will cause earlier cracks in the core area and reduce the ductility of the specimens by 6.1~27.9%. (2) Increasing the stirrup ratio in the joint core will increase the shear capacity of the joint by 5.3~8.1% while also increasing the failure displacement by 2.8~14.9%. (3) When the joints are configured with CRB735 stirrups, the rate of stiffness degradation from core cracking to the yielding stage of the specimens will be effectively delayed. (4) Compared with the joint configured with HRB400, the ultimate loading process of the joints using CRB735 is shorter, the yield load and yield displacement are higher, and the specimens are close to the ultimate state when they reach yield. Finally, the shear capacity of concrete beam-column joints configured with CRB735 can be calculated using the relevant shear capacity equation in GB50010-2010 Code for Design of Concrete Structures, and has a certain safety reserve. In the equation, the yield strength of CRB735 steel reinforcement is recommended to be 735 MPa.

Torsional capacity evaluation of RC beams using an improved bird swarm algorithm optimised 2D convolutional neural network

This study presents the application of deep learning technology in torsional capacity evaluation of reinforced concrete (RC) beams. A data-driven model based on 2D convolutional neural network (CNN) is established, where model inputs contain the beam width, beam height, stirrup width, stirrup height, concrete compressive strength, steel ratio of longitudinal reinforcement, yield strength of longitudinal reinforcement, steel ratio of transverse reinforcement, yield strength of transverse reinforcement and stirrup spacing. To enhance the prediction accuracy of the proposed model, an improved bird swarm algorithm (IBSA) is leveraged to optimise the hyperparameters of CNN in the training phase. A comprehensive dataset, comprising 268 groups of laboratory tests of RC beams collected from published articles, is used for model development and validation. The results show that the proposed 2D CNN with hyperparameter optimisation exhibits high performance in predicting torsional strength of RC beams, which outperforms other machine learning models, building codes and empirical formula in terms of a series of evaluation metrics.

Considerations on the partial safety factor format for reinforced concrete structures accounting for multiple failure modes

The increasing usage of nonlinear analyses for the design of reinforced concrete structures and the necessity of codes of practice to provide a consistent safety format for them is one of the challenges that new generations of codes of practice are facing. Suitable safety formats shall thus account for the peculiarities of nonlinear analysis, such as the possibility of having multiple potential failure modes. In this paper, the applicability of the classical Partial Safety Factor Format (PSFF) for the resistance of reinforced concrete structures (composed of two safety factors: γC for concrete compressive strength and γS for reinforcement yield strength) is investigated accounting for the possibility of multiple failure modes in nonlinear analysis. In addition, the similarities between nonlinear analysis and typical simple cases in the design of structural concrete are shown. Reliability analysis is performed for the design resistance of concrete structures according to PSFF under different design situations (cross-sectional resistance or load-bearing capacity of structural elements and of simple structural systems). The results show that the PSFF applied to material strength variables leads to a satisfactory level of reliability, independently of the development of different failure modes induced by material uncertainties in nonlinear analysis. In addition, it is also observed that the simplification of integrating geometrical and model uncertainties into the partial safety factors for material strength variables can potentially underestimate their influence on the structural reliability in some cases. The case studies shows that occurrence of multiple failure modes can result into significantly different distribution characteristics between the tail and most probable region of the resistance of concrete structures. Attention should also be paid to a proper tail approximation of the probability distribution of the resistance when calibrating safety formats for concrete structures.