Predicting the Shear Resistance of Steel Fiber Reinforced Concrete Structures using Random Forest-based Model

Author(s):  
Oladimeji Benedict Olalusi ◽  
Mariam Akinlolu ◽  
Theo C. Haupt
2020 ◽  
Vol 10 (19) ◽  
pp. 6955
Author(s):  
Oladimeji B. Olalusi ◽  
Panagiotis Spyridis

Shear failure is a brittle and undesirable mode of failure in reinforced concrete structures. Many of the existing shear design equations for steel fiber reinforced concrete (SFRC) beams include significant uncertainty due to the failure in accurately predicting the true shear capacity. Given these, adequate quantification and description of model uncertainties considering the systematic variation in the model prediction and measured shear capacity is crucial for reliability-based investigation. Reliability analysis must account for model uncertainties in order to predict the probability of failure under prescribed limit states. This study focuses on the quantification and description of model uncertainty related to the current shear resistance predictive models for SFRC beams without shear reinforcement. The German (DAfStB) model displayed the lowest bias and dispersion, whereas the fib Model 2010 and the Bernat et al., model displayed the highest bias and dispersion. The inconsistencies observed in the resistance model uncertainties at the variation of shear span to effective depth ratio are a major cause for concern, and differentiation with respect to this parameter is advised. Finally, in line with the EN 1990 semi-probabilistic approach for reliability-based design, the global partial safety factors related to model uncertainties in the shear resistance prediction of SFRC beams are proposed.


2020 ◽  
Vol 12 (4) ◽  
Author(s):  
Klara Talantova

The article discusses the terms and definitions that are increasingly appearing in the open press and on the Internet, confusing well-established concepts. The purpose of considering terminology issues is an attempt to draw the attention of the community of builders – scientists and practitioners to the inadmissibility of inaccuracy and incorrect choice of terms and definitions in the technical, scientific and even regulatory literature. The article discusses the transformation (changes) of terminology in the field of dense concretes, reinforced concrete structures, as well as structures based on a building composite – steel fiber reinforced concrete. In particular, concrete is a composite, reinforced concrete is not a collective name for reinforced concrete structures and products, but material, moreover, is composite. At the same time, steel fiber concrete is a hardened concrete. This approach is an important reason for restraining widespread use in the practice of building structures based on steel fiber reinforced concrete. These terms are often used in the open press, depriving the discussed topics of physical meaning, and, as a result, the impossibility of obtaining the expected result. At the same time, the open press provides information on composite materials, the peculiarities of creating and managing their properties. The article presents the established generally accepted terms and definitions that correspond to the physical nature of concrete, reinforced concrete structures, composite materials and structures based on composite – steel fiber reinforced concrete. The definition of composite materials, the principles of their creation, as well as the features of the composite – steel fiber reinforced concrete and methods of forming its structure are presented. A method is shown for obtaining the properties of steel fiber reinforced concrete in accordance with the stress-strain state of the developed structural element and creating structures based on it with properties specified in accordance with the conditions of their operation and having technical and economic indicators that exceed those of standard structures – analogues.


2021 ◽  
Vol 5 (7 (113)) ◽  
pp. 59-65
Author(s):  
Nadia Moneem Al-Abdaly ◽  
Salwa R. Al-Taai ◽  
Hamza Imran ◽  
Majed Ibrahim

Because of the incorporation of discontinuous fibers, steel fiber-reinforced concrete (SFRC) outperforms regular concrete. However, due to its complexity and limited available data, the development of SFRC strength prediction techniques is still in its infancy when compared to that of standard concrete. In this paper, the compressive strength of steel fiber-reinforced concrete was predicted from different variables using the Random forest model. Case studies of 133 samples were used for this aim. To design and validate the models, we generated training and testing datasets. The proposed models were developed using ten important material parameters for steel fiber-reinforced concrete characterization. To minimize training and testing split bias, the approach used in this study was validated using the 10-fold Cross-Validation procedure. To determine the optimal hyperparameters for the Random Forest algorithm, the Grid Search Cross-Validation approach was utilized. The root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE) between measured and estimated values were used to validate and compare the models. The prediction performance with RMSE=5.66, R2=0.88 and MAE=3.80 for the Random forest model. Compared with the traditional linear regression model, the outcomes showed that the Random forest model is able to produce enhanced predictive results of the compressive strength of steel fiber-reinforced concrete. The findings show that hyperparameter tuning with grid search and cross-validation is an efficient way to find the optimal parameters for the RF method. Also, RF produces good results and gives an alternate way for anticipating the compressive strength of SFRC


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