Optimizing reinforced concrete beams cost based on AI-based Lagrange functions

This study aims to develop artificial neural networks (ANNs)-based forward Lagrange optimizing ductile doubly reinforced concrete (RC) beams. Cost (CIb) of materials and manufacture were established as an objective function, which is minimized considering constraints according engineer’s needs. Large datasets 100,000 used derive AI-based function for cost (CIb, minimizing RC beams) a input parameters, replacing complex analytical functions. A resilient design capable finding beams beyond human efficiency based on equality inequality constraints, are implanted in Lagrange. Optimal designs not simple especially when multiple constraining conditions be considered. Neither it possible engineers pre-assign can sequentially calculated output conventional design. by was reduced 18%–26% compared probable beam designs. charts with eight outputs (?Mn,Mu,Mcr,?rt_0.003,?rc_0.003,?imme,?long,CIb) nine inputs (L, h, b, fy,f’c, ?rt,?rc,MD,ML) proposed assist beams, presenting CIb the preliminary stage.