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| Naji, M. |
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| Motta, Antonella |
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| Mohamed, Tarek |
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| Taccardi, Nicola |
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| Alshaaer, Mazen | Brussels |
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| Casati, R. |
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| Kočí, Jan | Prague |
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| Azam, Siraj |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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Ahmad, Mahmood
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Publications (6/6 displayed)
- 2024Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soilcitations
- 2024Effect of titanium dioxide as nanomaterials on mechanical and durability properties of rubberised concrete by applying RSM modelling and optimizationscitations
- 2024Effect of titanium dioxide as nanomaterials on mechanical and durability properties of rubberised concrete by applying RSM modelling and optimizationscitations
- 2022Gene Expression Programming for Estimating Shear Strength of RC Squat Wallcitations
- 2022Evaluation of Liquefaction-Induced Settlement Using Random Forest and REP Tree Models : Taking Pohang Earthquake as a Case of Illustration
- 2021Evaluation of Low Molecular Weight Cross Linked Chitosan Nanoparticles, to Enhance the Bioavailability of 5-Flourouracilcitations
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article
Gene Expression Programming for Estimating Shear Strength of RC Squat Wall
Abstract
<jats:p>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.</jats:p>