AbstractSegmental posttensioned precast concrete (SPPC) is a promising approach to accelerated bridge construction (ABC). However, an impediment to the performance-based design of SPPC piers lies in the challenge of defining appropriate damage states and corresponding engineering demand parameter (EDP) limits. To address this, in this study, genetic programming (GP), a form of artificial intelligence, is used to develop drift ratio–based EDP limit models in order to identify the onset of four damage levels that are particularly introduced herein for SPPC piers. In this respect, a finite-element model using ABAQUS is developed and experimentally validated to simulate the response of SPPC piers under seismic loading. The model is then utilized to generate a data set of 316 cases, all within the feasible design space of SPPC piers. This data set is subsequently used to develop GP-based predictive equations that map the relationship between different SPPC pier design parameters (e.g., posttensioning strand ratio, concrete compressive strength, and gravity load level) and EDP (i.e., drift ratio) limits. The performance of the developed equations is evaluated through different measures, including fitness function and performance index. Ultimately, parametric and sensitivity analyses are conducted to demonstrate that the GP-based equations are robust enough to characterize the four damage states for SPPC piers.