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. / Thesis / Master of Applied Science (MASc)

Reinforced concrete block shear walls (RCBSWs) are used as seismic force resisting systems in low- and medium-rise buildings. However, attributed to their nonlinear behavior and composite material nature, accurate prediction of their seismic performance relying only on mechanics is challenging. This study introduces multi-gene genetic programming (MGGP)— a class of bio-inspired artificial intelligence, to uncover the complexity of RCBSW behaviors and develop simplified procedures for predicting the full backbone curve of flexure-dominated fully grouted RCBSWs under cyclic loading. A piecewise linear backbone curve was developed using five secant stiffness expressions associated with cracking, yielding, 80% ultimate, ultimate, and 20% strength degradation (i.e., post-peak stage) derived through controlled MGGP. Based on the experimental results of large-scale cyclically loaded RCBSWs, compiled from previously reported studies, a variable selection procedure was performed to identify the most influential variable subset governing wall behaviors. Utilizing individual wall results, the MGGP stiffness expressions were first trained and tested, and their accuracy was subsequently compared to that of existing models employing various statistical measures. In addition, the predictability of the developed backbone model was assessed at the system-level against experimental results of two two-story buildings available in the literature. The outcomes obtained from this study demonstrate the power of MGGP approach in addressing the complexity of the cyclic behavior of RCBSWs at both component- and system-level—offering an efficient prediction tool that can be adopted by relevant seismic design standards pertaining to RCBSW buildings. / Thesis / Master of Applied Science (MASc)