A Hybrid Mechanics-evolutionary Algorithm-derived Backbone Model for Unbonded Post-tensioned Concrete Block Shear Walls

Abstract

Unbonded post-tensioned concrete block (UPCB) shear walls are an effective seismic force resisting system due to their ability to contain expected damage attributed to their self-centering capabilities. A few design procedures were proposed to predict the in-plane flexural response of UPCB walls, albeit following only basic mechanics and/or extensive iterative methods. Such procedures, however, may not be capable of capturing the complex nonlinear relationships between different parameters that affect UPCB walls’ behavior or are tedious to be adopted for design practice. In addition, the limited datasets used to validate these procedures may render their accuracy and generalizability questionable, further hindering their adoption by practitioners and design standards. To address these issues, an experimentally-validated nonlinear numerical model was adopted in this study and subsequently employed to simulate 95 UPCB walls with different design parameters to compensate for the lack of relevant experimental data in the current literature. Guided by mechanics and using this database, an evolutionary algorithm, multigene genetic programming (MGGP), was adopted to uncover the relationships controlling the response of UPCB walls, and subsequently develop simplified closed-form wall behavior prediction expressions. Specifically, through integrating MGGP and basic mechanics, a penta-linear backbone model was developed to predict the load-displacement backbone for UPCB walls up to 20% strength degradation. Compared to existing predictive procedures, the prediction accuracy of the developed model and its closed-form nature are expected to enable UPCB wall adoption by seismic design standards and code committees.