Reinforced concrete frames are usually sized by repeated manual cycles of analysis and revision, a practice that leaves many members either marginally checked or carrying more material than the loading requires. This paper proposes and documents a workflow that connects the STAAD.Pro structural analysis and design package to the XGBoost learning algorithm, so that routine member level results become a dataset from which design decisions can be drawn. Two models are described: a classifier that judges whether a member is safe, and a regressor that estimates the section area needed to bring a member into a target utilization band. A cost aware selection layer then picks an adjusted section from a practical catalogue, and every recommendation is re-analysed in STAAD.Pro so that only verified savings are counted. The study reviews evolutionary, swarm based, BIM based, standalone learning and surrogate approaches, and identifies limited integration of a commercial analysis package with boosted tree learning as the gap addressed. Methodological safeguards include asymmetric treatment of unsafe misses, grouped validation by building model, and separate feature sets with and without the utilization ratio. A prototype web application, STAAD.OPT, accepts a CSV file of member demands and returns a verdict and adjusted parameters for each member. An evaluation protocol with acceptance criteria fixed in advance is presented

Ankita Bhawthankar, Dr. Madhavi Shamkuwar, "Cost and Material Optimization of Reinforced Concrete Buildings An Integrated Workflow of STAAD.Pro Analysis and XGBoost Learning" International Journal of Advance Research in Science, Management, Engineering and Technology, V1(4): Page(72-82) September - October 2026. ISSN: 3139-1141. www.ijarsmet.com. Published by Scientific and Academic Research Publishing.