scholarly journals Cyclic Flexural Behavior of Reinforced Concrete Beams

2018 ◽  
Vol 38 ◽  
pp. 03022
Author(s):  
Prafulla B Malla ◽  
Hong Zhou ◽  
Yi Che
Keyword(s):  
Reinforced Concrete ◽  
Flexural Strength ◽  
Size Effect ◽  
Concrete Beams ◽  
Experimental Tests ◽  
Apparent Size ◽  
Reinforced Concrete Beams ◽  
Flexural Behavior ◽  
Displacement Ductility ◽  
Beam Depth

The present study aims at investigating the cyclic flexural behavior of reinforced concrete beams with varying depths. Five reinforced concrete beams with beam depth ranging from 250 mm to 750 mm were tested under reversed cyclic loading and the influence of beam depth on the flexural strength and ductility of reinforced concrete beams was investigated. In addition, OpenSees was used to model the test specimens and the analytical results were compared with the experimental reuslts. It is shown that there is no apparent size effect on the normalized ultimate flexural strength of the tested beams, while for the displacement ductility factor, a significant size effect is observed. Load-deflection hysteric curves of test specimens obtained by the fiber-based element of OpenSees with Concrete03 and Hysteric models are in good agreement with those from experimental tests.

Flexural Behavior of High Strength Reinforced Concrete Beams by Replacement Ratio of Recycled Coarse Aggregate

2013 ◽  
Vol 680 ◽  
pp. 230-233 ◽  
Author(s):  
Yong Taeg Lee ◽  
Seung Hun Kim ◽  
Jong Hyeon Kim ◽  
Sang Ki Baek ◽  
Young Sang Cho ◽  
...  
Keyword(s):  
Reinforced Concrete ◽  
Flexural Strength ◽  
Concrete Beams ◽  
High Strength ◽  
Reinforced Concrete Beams ◽  
Flexural Behavior ◽  
Recycled Aggregates ◽  
Current Test ◽  
Potential Applications ◽  
Flexural Tests

Recently, many structures which were built about 30 years ago are watched by reconstruction. Demolished concrete is occurred in the process and these quantity increase about 10% more than the preceding year. Fortunately, recycled aggregates are produced from demolished concrete, whereas the recycled aggregates are not used often because there are not many researches which have been verified by experts or researchers about strength when reinforced concrete is made with recycled aggregates. In this paper, high strength reinforced concrete is valued with potential applications and check change of strength when it made by recycled aggregates. For this, flexural tests of 4 high strength reinforced concrete beams with recycled aggregates were performed, and the high strength reinforced concrete beams were tested within the limits such as compressive strength, flexural strength, ductility, strain, and curvature. The current test data were examined in terms of flexural strength, along with the data from previously tested reinforced concrete beams with recycled aggregates.

An Experimental Study on Flexural Behavior of Reinforced Concrete Beams with GFRP Bars and Recycled Coarse Aggregate

2013 ◽  
Vol 319 ◽  
pp. 440-443
Author(s):  
Seung Hun Kim ◽  
Yong Taeg Lee ◽  
Tae Soo Kim ◽  
Seong Uk Hong
Keyword(s):  
Reinforced Concrete ◽  
Flexural Strength ◽  
Concrete Beams ◽  
Reinforced Concrete Beams ◽  
Flexural Behavior ◽  
Flexural Performance ◽  
Coarse Aggregates ◽  
Glass Fiber Reinforced ◽  
Load Carrying ◽  
Cracking Pattern

This study evaluates the flexural performance of reinforced concrete beams with GFRP(Glass Fiber Reinforced Polymer) bars and RCA(Recycled Coarse Aggregates). A total of four specimens with various replacement ratios of RCA (0%, 30%, 50%, and 100%) were tested. An investigation was performed on the influence of RCA with various replacement ratios on load-carrying capacity, post cracking stiffness, cracking pattern, and ductility. The test results showed that replacement ratios of RCA had not a bad effect on concrete compressive strength or flexural strength of beams. They were compared with the design flexural strength and the nominal moment predictions of ACI Code.

scholarly journals Efficiency of Hybrid Algorithms for Estimating the Shear Strength of Deep Reinforced Concrete Beams

2022 ◽  
Author(s):  
Mohammad Sadegh Barkhordari ◽  
De-Cheng Feng ◽  
Mohsen Tehranizadeh
Keyword(s):  
Shear Strength ◽  
Reinforced Concrete ◽  
Concrete Beams ◽  
Search Algorithm ◽  
Experimental Tests ◽  
Reinforced Concrete Beams ◽  
Rc Beams ◽  
High Rise ◽  
Beam Depth ◽  
Artificial Neural Network Ann

Earthquakes occurred in recent years have highlighted the need to examine the strength of reinforced concrete (RC) members. RC beams are one of the elements of reinforced concrete structures. Due to the dramatic increase in the population and the number of medium/high-rise buildings, in recent years, the beams of buildings have been mainly designed and executed in the type of deep beams. In this study, the artificial neural network (ANN) with optimization algorithms, including particle swarm optimization (PSO), Archimedes optimization algorithm (AOA), and sparrow search algorithm (SSA), are used to determine the shear strength of reinforced concrete deep (RCD) beams. 271 samples from experimental tests are employed to develop algorithms. The results of this study, design codes equations, and previous research are compared. Comparison between the results shows that the PSO-ANN algorithm is more accurate than previous methods. Finally, SHApley Additive exPlanations (SHAP) method is utilized to explain the predictions. SHAP reveals that the beam span and the ratio of the beam span to beam depth have the highest impact in predicting shear strength.

scholarly journals Optimum Design of Flexural Strength and Stiffness for Reinforced Concrete Beams Using Machine Learning

2021 ◽  
Vol 11 (18) ◽  
pp. 8762
Author(s):  
Nazim Abdul Nariman ◽  
Khader Hamdia ◽  
Ayad Mohammad Ramadan ◽  
Hamed Sadaghian
Keyword(s):  
Machine Learning ◽  
Reinforced Concrete ◽  
Flexural Strength ◽  
Concrete Beams ◽  
Design Method ◽  
Experimental Tests ◽  
Reinforced Concrete Beams ◽  
Optimization Approach ◽  
Steel Bars ◽  
Strength And Stiffness

In this paper, an optimization approach was presented for the flexural strength and stiffness design of reinforced concrete beams. Surrogate modeling based on machine learning was applied to predict the responses of the structural system in three-point flexure tests. Three design input variables, the area of steel bars in the compression zone, the area of steel bars in the tension zone, and the area of steel bars in the shear zone, were adopted for the dataset and arranged by the Box-Behnken design method. The dataset was composed of thirteen specimens of reinforced concrete beams. The specimens were tested under three-points flexure loading at the age of 28 days and both the failure load and the maximum deflection values were recorded. Compression and tension tests were conducted to obtain the concrete data for the analysis and numerical modeling. Afterward, finite element modeling was performed for all the specimens using the ATENA program to verify the experimental tests. Subsequently, the surrogate models for the flexural strength and the stiffness were constructed. Finally, optimization was conducted, supporting the factorial method for the predicted responses. The adopted approach proved to be an excellent tool to optimize the design of reinforced concrete beams for flexure and stiffness. In addition, experimental and numerical results were in very good agreement in terms of both the failure type and the cracking pattern.

Sign in / Sign up

Export Citation Format

Share Document