Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach

2021 ◽  
Vol 233 ◽  
pp. 111743
Author(s):  
Jesika Rahman ◽  
Khondaker Sakil Ahmed ◽  
Nafiz Imtiaz Khan ◽  
Kamrul Islam ◽  
Sujith Mangalathu
Keyword(s):  
Machine Learning ◽  
Steel Fiber ◽  
Concrete Beams ◽  
Fiber Reinforced Concrete ◽  
Reinforced Concrete Beams ◽  
Data Driven ◽  
Strength Prediction ◽  
Learning Approach ◽  
Steel Fiber Reinforced Concrete ◽  
Machine Learning Approach

Shear strength prediction for steel fiber reinforced concrete beams without stirrups

2016 ◽  
Vol 127 ◽  
pp. 101-116 ◽  
Author(s):  
Fasheng Zhang ◽  
Yining Ding ◽  
Jing Xu ◽  
Yulin Zhang ◽  
Weiqing Zhu ◽  
...  
Keyword(s):  
Shear Strength ◽  
Reinforced Concrete ◽  
Steel Fiber ◽  
Concrete Beams ◽  
Fiber Reinforced Concrete ◽  
Reinforced Concrete Beams ◽  
Strength Prediction ◽  
Fiber Reinforced ◽  
Steel Fiber Reinforced Concrete

scholarly journals Erratum: Ly, H.-B., et al. Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams. Sustainability 2020, 12, 2709

2020 ◽  
Vol 12 (17) ◽  
pp. 7029
Author(s):  
Hai-Bang Ly ◽  
Tien-Thinh Le ◽  
Huong-Lan Thi Vu ◽  
Van Quan Tran ◽  
Lu Minh Le ◽  
...  
Keyword(s):  
Machine Learning ◽  
Reinforced Concrete ◽  
Steel Fiber ◽  
Concrete Beams ◽  
Shear Capacity ◽  
Fiber Reinforced Concrete ◽  
Reinforced Concrete Beams ◽  
Steel Fiber Reinforced Concrete ◽  
Hybrid Machine ◽  
Published Paper

The authors would like to make the following corrections to the published paper [...]

scholarly journals Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams

2020 ◽  
Vol 12 (7) ◽  
pp. 2709 ◽  
Author(s):  
Hai-Bang Ly ◽  
Tien-Thinh Le ◽  
Huong-Lan Thi Vu ◽  
Van Quan Tran ◽  
Lu Minh Le ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sensitivity Analysis ◽  
Reinforced Concrete ◽  
Steel Fiber ◽  
Concrete Beams ◽  
Shear Capacity ◽  
Fiber Reinforced Concrete ◽  
Reinforced Concrete Beams ◽  
Steel Fiber Reinforced Concrete ◽  
Hybrid Machine

Understanding shear behavior is crucial for the design of reinforced concrete beams and sustainability in construction and civil engineering. Although numerous studies have been proposed, predicting such behavior still needs further improvement. This study proposes a soft-computing tool to predict the ultimate shear capacities (USCs) of concrete beams reinforced with steel fiber, one of the most important factors in structural design. Two hybrid machine learning (ML) algorithms were created that combine neural networks (NNs) with two distinct optimization techniques (i.e., the Real-Coded Genetic Algorithm (RCGA) and the Firefly Algorithm (FFA)): the NN-RCGA and the NN-FFA. A database of 463 experimental data was gathered from reliable literature for the development of the models. After the construction, validation, and selection of the best model based on common statistical criteria, a comparison with the empirical equations available in the literature was carried out. Further, a sensitivity analysis was conducted to evaluate the importance of 16 inputs and reveal the dependency of structural parameters on the USC. The results showed that the NN-RCGA (R = 0.9771) was better than the NN-FFA and other analytical models (R = 0.5274–0.9075). The sensitivity analysis results showed that web width, effective depth, and a clear depth ratio were the most important parameters in modeling the shear capacity of steel fiber-reinforced concrete beams.

scholarly journals EXPERIMENTAL STUDIES OF REINFORCED CONCRETE BEAMS STRENGTHENED WITH STEEL FIBER JACKET

2020 ◽  
pp. 34-45
Author(s):  
O. Radaikin ◽  
L. Sharafutdinov
Keyword(s):  
Reinforced Concrete ◽  
Steel Fiber ◽  
Concrete Beams ◽  
Experimental Studies ◽  
Breaking Load ◽  
Fiber Reinforced Concrete ◽  
Reinforced Concrete Beams ◽  
Steel Fiber Reinforced Concrete ◽  
Joint Work ◽  
Loaded Crack

The purpose of the study is to experimentally study the joint work of steel fiber reinforced concrete (SFB) reinforcement jacket and reinforced concrete beams at all stages of loading to further develop a methodology for calculating this method of reinforcing bending elements. The main results of the study consist in assessing the strength, stiffness, fracture toughness, as well as the nature of fracture with a picture of the development of cracks for the examined 4 samples (two with a jacket of reinforcement, two - control - without reinforcement). It has been established that the use of SFB jacket with a thickness of 45 mm and with a fiber content percentage of 2,5% (at a flow rate of 196 kg/m3) increases the breaking load by 20 %, stiffness from 3,4 to 11 times as it is loaded, crack resistance 2,4-2,8 times. The results are compared with computer modeling in ANSYS PC: the discrepancy in the load of crack formation, fracture and deflection values for full-scale samples and a computer model are within 6,3 %, which indicates the reliability of the numerical results and the possibility of using the proposed computer models in further studies on topic of the article.

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