Development of prediction models for shear strength of SFRCB using a machine learning approach

Sarveghadi, Masoud; Gandomi, Amir H.; Bolandi, Hamed; Alavi, Amir H.

doi:10.1007/s00521-015-1997-6

Cited by 60 publications

(49 citation statements)

References 26 publications

(38 reference statements)

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“…The reason for this improvement is that our proposed model uses the available information from the literature in an optimal way. Moreover, comparing the predictions with our model to the model by Greenough and Nehdi [24] and Sarveghadi et al [22], which were also based on soft computing methods, shows that using a larger database and evaluating a large number of ANN features results in a significant better fit of the experimental data.…”

Section: Comparison Between Ann-based and Existing Methodsmentioning

confidence: 62%

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ANN-Based Shear Capacity of Steel Fiber-Reinforced Concrete Beams without Stirrups

Abambres

Lantsoght

2019

Fibers

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Comparing experimental results of the shear capacity of steel fiber-reinforced concrete (SFRC) beams without stirrups to the capacity predicted using current design equations and other available formulations shows that predicting the shear capacity of SFRC beams without mild steel shear reinforcement is still difficult. The reason for this difficulty is the complex mechanics of the problem, where the steel fibers affect the different shear-carrying mechanisms. Since this problem is still not fully understood, we propose the use of artificial intelligence (AI) to derive an expression based on the available experimental data. We used a database of 430 datapoints obtained from the literature. The outcome is an artificial neural network-based expression to predict the shear capacity of SFRC beams without shear reinforcement. For this purpose, many thousands of artificial neural network (ANN) models were generated, based on 475 distinct combinations of 15 typical ANN features. The proposed “optimal” model results in maximum and mean relative errors of 0.0% for the 430 datapoints. The proposed model results in a better prediction (mean Vtest/VANN = 1.00 with a coefficient of variation 1 × 10−15) as compared to the existing code expressions and other available empirical expressions, with the model by Kwak et al. giving a mean value of Vtest/Vpred = 1.01 and a coefficient of variation of 27%. Until mechanics-based models are available for predicting the shear capacity of SFRC beams without shear reinforcement, the proposed model thus offers an attractive solution for estimating the shear capacity of SFRC beams without shear reinforcement. With this approach, designers who may be reluctant to use SFRC because of the large uncertainties and poor predictions of experiments, may feel more confident using the material for structural design.

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Section: Comparison Between Ann-based and Existing Methodsmentioning

confidence: 62%

“…Several the expressions in Table 1 have been developed with methods of AI. The equation by Sarveghadi et al [22] is derived from a more general expression developed with the use of artificial neural networks. The expression by Greenough and Nehdi [24] results from genetic programming.…”

Section: Rdmentioning

confidence: 99%

ANN-Based Shear Capacity of Steel Fiber-Reinforced Concrete Beams without Stirrups

Abambres

Lantsoght

2019

Fibers

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“…The appearance of any factor in such empirical equations could demonstrate the importance of that factor in predicting the USC. In a series of works by Sarveghadi et al [171], Kwak et al [101], Ashour et al [57], and Ahmadi et al [13], factors such as the reinforcement ratio, fiber factor, shear span, effective depth, concrete compressive strength, and web width are considered. Several additions apart from the above factors have also been observed in the works of Arslan et al [15], Imam et al [89], and Greenough and Nehdi [83].…”

Section: Importance Of Selection Of the Input Factorsmentioning

confidence: 99%

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

et al. 2020

Sustainability

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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.

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“…This paper presents a unique database of 487 experiments. Smaller databases have been reported or discussed in the literature previously [23][24][25][26][27][28][29], but the current effort has resulted in the recompilation of a significantly larger number of datapoints. Moreover, the full database is available as a dataset in the public domain for other researchers [30], which is a step forward as well.…”

Section: Introductionmentioning

confidence: 99%

Database of Shear Experiments on Steel Fiber Reinforced Concrete Beams without Stirrups

Lantsoght¹

2019

Preprint

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Adding steel fibers to concrete improves the capacity in tension-driven failure modes. An example is the shear capacity in steel fiber reinforced concrete (SFRC) beams with longitudinal reinforcement and without shear reinforcement. Since no mechanical models exist that can fully describe the behavior of SFRC beams without shear reinforcement failing in shear, a number of empirical equations have been suggested in the past. This paper compiles the existing empirical equations and code provisions for the prediction of the shear capacity of SFRC beams failing in shear as well as a database of 487 experiments reported in the literature. The experimental shear capacities from the database are then compared to the prediction equations. This comparison shows a large scatter on the ratio of experimental to predicted values. The practice of defining the tensile strength of SFRC based on different experiments internationally makes the comparison difficult. For design purposes, the code prediction methods based on the Eurocode shear expression provide reasonable results (with coefficients of variation on the ratio of tested to predicted results of 27% - 29%). None of the currently available methods properly describe the behavior of SFRC beams failing in shear. As such, this work shows the need for studies that address the different shear-carrying mechanisms in SFRC and its crack kinematics.

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Development of prediction models for shear strength of SFRCB using a machine learning approach

Cited by 60 publications

References 26 publications

ANN-Based Shear Capacity of Steel Fiber-Reinforced Concrete Beams without Stirrups

ANN-Based Shear Capacity of Steel Fiber-Reinforced Concrete Beams without Stirrups

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

Database of Shear Experiments on Steel Fiber Reinforced Concrete Beams without Stirrups

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