scholarly journals Predicting Shear Capacity of FRP-Reinforced Concrete Beams without Stirrups by Artificial Neural Networks, Gene Expression Programming, and Regression Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Ghazi Bahroz Jumaa ◽  
Ali Ramadhan Yousif
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Reinforced Concrete ◽  
Prediction Models ◽  
Concrete Beams ◽  
Gene Expression Programming ◽  
Shear Capacity ◽  
Reinforced Concrete Beams

The shear strength prediction of fiber-reinforced polymer- (FRP-) reinforced concrete beams is one of the most complicated issues in structural engineering applications. Developing accurate and reliable prediction models is necessary and cost saving. This paper proposes three new prediction models, utilizing artificial neural networks (ANNs) and gene expression programming (GEP), as a recently developed artificial intelligent techniques, and nonlinear regression analysis (NLR) as a conventional technique. For this purpose, a large database including 269 shear test results of FRP-reinforced concrete members was collected from the literature. The performance of the proposed models is compared with a large number of available codes and previously proposed equations. The comparative statistical analysis confirmed that the ANNs, GEP, and NLR models, in sequence, showed excellent performance, great efficiency, and high level of accuracy over all other existing models. The ANNs model, and to a lower level the GEP model, showed the superiority in accuracy and efficiency, while the NLR model showed that it is simple, rational, and yet accurate. Additionally, the parametric study indicated that the ANNs model defines accurately the interaction of all parameters on shear capacity prediction and have a great ability to predict the actual response of each parameter in spite of its complexity and fluctuation nature.

scholarly journals The Prediction of Stiffness of Bamboo-Reinforced Concrete Beams Using Experiment Data and Artificial Neural Networks (ANNs)

Crystals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 757 ◽  
Author(s):  
Muhtar ◽  
Amri Gunasti ◽  
Suhardi ◽  
Nursaid ◽  
Irawati ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reinforced Concrete ◽  
Concrete Beams ◽  
Experimental Results ◽  
Reinforced Concrete Beams ◽  
Steel Reinforced Concrete ◽  
Simple Support ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Stiffness is the main parameter of the beam’s resistance to deformation. Based on advanced research, the stiffness of bamboo-reinforced concrete beams (BRC) tends to be lower than the stiffness of steel-reinforced concrete beams (SRC). However, the advantage of bamboo-reinforced concrete beams has enough good ductility according to the fundamental properties of bamboo, which have high tensile strength and high elastic properties. This study aims to predict and validate the stiffness of bamboo-reinforced concrete beams from the experimental results data using artificial neural networks (ANNs). The number of beam test specimens were 25 pieces with a size of 75 mm × 150 mm × 1100 mm. The testing method uses the four-point method with simple support. The results of the analysis showed the similarity between the stiffness of the beam’s experimental results with the artificial neural network (ANN) analysis results. The similarity rate of the two analyses is around 99% and the percentage of errors is not more than 1%, both for bamboo-reinforced concrete beams (BRC) and steel-reinforced concrete beams (SRC).

scholarly journals Shear Capacity of Reinforced Concrete Beams under Monotonic and Cyclic Loads: Experiments and Computational Models

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4092
Author(s):  
Kamil Bacharz ◽  
Barbara Goszczyńska
Keyword(s):  
Reinforced Concrete ◽  
Laboratory Tests ◽  
Computational Models ◽  
Concrete Beams ◽  
Shear Capacity ◽  
Reinforced Concrete Beams ◽  
Image Correlation ◽  
Engineering Practice ◽  
Cyclic Loads ◽  
Loading Modes

The paper reports the results of a comparative analysis of the experimental shear capacity obtained from the tests of reinforced concrete beams with various static schemes, loading modes and programs, and the shear capacity calculated using selected models. Single-span and two-span reinforced concrete beams under monotonic and cyclic loads were considered in the analysis. The computational models were selected based on their application to engineering practice, i.e., the approaches implemented in the European and US provisions. Due to the changing strength characteristics of concrete, the analysis was also focused on concrete contribution in the shear capacity of reinforced concrete beams in the cracked phase and on the angle of inclination of diagonal struts. During the laboratory tests, a modern ARAMIS digital image correlation (DIC) system was used for tracking the formation and development of diagonal cracks.

scholarly journals Statistical and Reliability Study on Shear Strength of Recycled Coarse Aggregate Reinforced Concrete Beams

Materials ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3321
Author(s):  
Hyunjin Ju ◽  
Meirzhan Yerzhanov ◽  
Alina Serik ◽  
Deuckhang Lee ◽  
Jong R. Kim
Keyword(s):  
Reinforced Concrete ◽  
Concrete Beams ◽  
Construction Material ◽  
Safety Margin ◽  
Shear Capacity ◽  
Reinforced Concrete Beams ◽  
Construction And Demolition Waste ◽  
Demolition Waste ◽  
Rc Structures ◽  
Coarse Aggregates

The consumption of structural concrete in the construction industry is rapidly growing, and concrete will remain the main construction material for increasing urbanization all over the world in the near future. Meanwhile, construction and demolition waste from concrete structures is also leading to a significant environmental problem. Therefore, a proper sustainable solution is needed to address this environmental concern. One of the solutions can be using recycled coarse aggregates (RCA) in reinforced concrete (RC) structures. Extensive research has been conducted in this area in recent years. However, the usage of RCA concrete in the industry is still limited due to the absence of structural regulations appropriate to the RCA concrete. This study addresses a safety margin of RCA concrete beams in terms of shear capacity which is comparable to natural coarse aggregates (NCA) concrete beams. To this end, a database for reinforced concrete beams made of recycled coarse aggregates with and without shear reinforcement was established, collecting the shear specimens available from various works in the existing literature. The database was used to statistically identify the strength margin between RCA and NCA concrete beams and to calculate its safety margin based on reliability analysis. Moreover, a comparability study of RCA beams was conducted with its control specimens and with a database for conventional RC beams.

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