A COLLABORATIVE COMPUTATIONAL FRAMEWORK FORAUTOMATED OPTIMIZATION, ARCHITECTURALPLANNING, AND CONSTRUCTION OF REINFORCEDCONCRETE FRAMES THROUGH COLLABORATIVEAUTOMATION

This paper introduces a collaborative computational framework that optimizes and automates theperformance-based seismic design (PBSD) process for reinforced concrete (RC) frames, taking intoaccount architectural constraints. The framework integrates architectural design with performancebasedengineering through artificial intelligence (AI), promoting collaboration among architects,engineers, and construction professionals, and leading to cost reduction. The research emphasizes theintegration of an AI-driven platform for seismic design optimization, ensuring compliance witharchitectural limitations. The framework aims to reduce structural weight and improve architecturalspace and lighting quality, thereby enhancing cost-effectiveness and sustainability. The methodology’seffectiveness is demonstrated through a case study of a ۴-story shear frame, showing improvements inefficiency, resilience, sustainability, structural performance, and natural light optimization in seismicproneregions. The validation highlights enhancements in a data flow, stakeholder synergy, seismicdesign efficiency, and sustainability.

Keywords:

Structural Optimization , Performance-Based Seismic Design , Collaborative Design , Artificial Intelligence , Sustainable Design