Mechanics-Guided Genetic Programming Expression for Shear-Strength Prediction of Squat Reinforced Concrete Walls with Boundary Elements
Publication: Journal of Structural Engineering
Volume 146, Issue 11
Abstract
Squat reinforced concrete shear walls with boundary elements (SRCSW-BE) are used in special structures (e.g., nuclear facilities) to resist lateral seismic loads. However, several studies have demonstrated the inaccuracy of the relevant current shear strength prediction expressions (e.g., ASCE/SEI 43-05). Specifically, expressions originally developed based on empirical or experimentally calibrated analytical models (using different datasets) showed discrepancies when their predictions were compared with experimental results from other datasets. This situation is mainly attributed to the complex shear behavior and failure mechanisms of SRCSW-BE in addition to the wide ranges of their interdependent design characteristics. To address this issue, the current study utilizes genetic programming (GP), a form of artificial intelligence, to develop an elegant shear strength prediction expression using a dataset of 254 SRCSW-BE. Guided by mechanics, the key factors governing wall shear strength were first identified, and the GP-based expression was subsequently developed, trained, validated, and tested. The accuracy of the developed GP-based expression was assessed through different performance evaluation measures. The analyses showed that the developed expression can provide better predictions with significantly higher accuracy compared to other shear strength prediction expressions available in relevant design standards and literature. Further robustness assessment also demonstrated the conformity of the GP-based expression with known underlying behavior mechanics of SRCSW-BE, which, along with its elegant form, makes the developed expression adoption-ready by relevant design standards (e.g., ACI 318 and CSA A23.3). Overall, the current study is expected to demonstrate the ability of GP-based approaches in addressing other complex behaviors of structural components/systems and tackling relevant challenges pertaining to the latter’s behavior predictions.
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Data Availability Statement
The dataset analyzed in the study is available in Gulec and Whittaker (2009). Other data are available in the respective references cited throughout the manuscript.
Acknowledgments
The financial support for the study was provided through the Natural Science and Engineering Research Council (NSERC) of Canada CaNRisk-CREATE program, as well as the INTERFACE Institute and the INViSiONLab, both of McMaster University. The authors would like also to acknowledge support from the NSERC, Grant No. RGPIN-2020-06611.
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Journal of Structural Engineering
Volume 146 • Issue 11 • November 2020
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©2020 American Society of Civil Engineers.
History
Received: Sep 22, 2019
Accepted: Mar 2, 2020
Published online: Aug 17, 2020
Published in print: Nov 1, 2020
Discussion open until: Jan 17, 2021
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