A regression model for predicting the shear strength of RC knee joint subjected to opening and closing moment

Tariq, Moiz; Khan, Azam; Shayanfar, Javad; Hanif, Muhammad Usman; Ullah, Asad

doi:10.1016/j.jobe.2021.102727

Cited by 8 publications

(13 citation statements)

References 42 publications

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“…The benefits of GEP have attracted extensive applications in the field of structural engineering over the last decade [16]. Several authors have elegantly employed GEP to develop models for estimating the capacity of various structural components [65]. Recently, a regression-based model has been successfully used for predicting the behavior of RC corner joints, especially where the code formulations are not available [64].…”

Section: Gene Expression Programming Algorithmmentioning

confidence: 99%

“…If the fitness criterion is not satisfied, roulettewheel sampling is employed to select some chromosomes and then mutate them to get new generations. Whereas, if the variables are optimally tuned to the fitness function, the chromosomes are decoded following the best solution to the problem [63,65]. Machine learning (ML) algorithms have been widely used to tackle real-world problems in the last ten years, particularly in civil engineering.…”

Section: Gene Expression Programming Algorithmmentioning

confidence: 99%

“…Machine learning (ML) algorithms have been widely used to tackle real-world problems in the last ten years, particularly in civil engineering. ML algorithms have been successfully used in a variety of real situations, paving the way for several promising opportunities in civil engineering and other domains such as geotechnical, and structural engineering [65][66][67][68][69][70][71][72][73][74].…”

Section: Gene Expression Programming Algorithmmentioning

confidence: 99%

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Gene Expression Programming for Estimating Shear Strength of RC Squat Wall

et al. 2022

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The flanged, barbell, and rectangular squat reinforced concrete (RC) walls are broadly used in low-rise commercial and highway under and overpasses. The shear strength of squat walls is the major design consideration because of their smaller aspect ratio. Most of the current design codes or available published literature provide separate sets of shear capacity equations for flanged, barbell, and rectangular walls. Also, a substantial scatter exists in the predicted shear capacity due to a large discrepancy in the test data. Thus, this study aims to develop a single gene expression programming (GEP) expression that can be used for predicting the shear strength of these three cross-sectional shapes based on a dataset of 646 experiments. A total of thirteen influencing parameters are identified to contrive this efficient empirical compared to several shear capacity equations. Owing to the larger database, the proposed model shows better performance based on the database analysis results and compared with 9 available empirical models.

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Section: Gene Expression Programming Algorithmmentioning

confidence: 99%

Section: Gene Expression Programming Algorithmmentioning

confidence: 99%

Section: Gene Expression Programming Algorithmmentioning

confidence: 99%

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Gene Expression Programming for Estimating Shear Strength of RC Squat Wall

et al. 2022

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“…e proposed model incorporating all the important influencing parameters possesses the highest value of the coefficient of determination (R 2 � 0.79), and the lowest value of both the coefficient of variation (CoV � 12.7) and the average absolute error (AAE � 9.4%), which clearly shows the accuracy and reliability of the model. Apart from the Moiz et al [25] model, all the other models have the coefficient of determination of less than 50%. Similarly, the performance factor of the formulations proposed by the code of practices is less than 50%.…”

Section: Resultsmentioning

confidence: 99%

“…Besides these actions, the corner joint has normally lower axial load than joints in other locations because of a discontinuous column. Due to all these differences, Moiz et al [25] proposed two regression models, one for corner joint opening and the other for the corner joint closing.…”

Section: Introductionmentioning

confidence: 99%

Shear Strength Model for Reinforced Concrete Corner Joints Based on Soft Computing Techniques

Tariq

Khan

Ullah

et al. 2022

Advances in Civil Engineering

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The shear strength of cyclically loaded RC corner joints, resulting in opening and closing moments, has not been extensively studied. In addition, experimental studies of the joint shear strength are time-consuming and expensive. Therefore, to overcome this challenge, two separate gene expression programming (GEP) based empirical models are developed for the shear strength of the corner joints, one under the opening moment and the other under the closing moments. One of the key parameters overlooked in previous studies is the joint shear reinforcement, which has been incorporated in the GEP models. These models are developed by compiling an experimental database of 59 specimens in terms of the concrete compressive strength, the joint aspect ratio, the reinforcement tensile strength, and the reinforcement compressive strength. A detailed statistical study is undertaken that indicates superior accuracy of the proposed models and a high potential for their application in the design practice.

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