首页> 外文期刊>Structural and multidisciplinary optimization >Least-cost design of singly and doubly reinforced concrete beam using genetic algorithm optimized artificial neural network based on Levenberg-Marquardt and quasi-Newton backpropagation learning techniques
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Least-cost design of singly and doubly reinforced concrete beam using genetic algorithm optimized artificial neural network based on Levenberg-Marquardt and quasi-Newton backpropagation learning techniques

机译:基于Levenberg-Marquardt和拟牛顿反向传播学习技术的遗传算法优化人工神经网络的单/双钢筋混凝土梁低成本设计

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摘要

In this work, least-cost design of singly and doubly reinforced beams with uniformly distributed and concentrated load was done by incorporating actual self-weight of beam, parabolic stress block, moment-equilibrium and serviceability constraint besides other constraints. Also, this design expertise was incorporated into a genetically optimized artificial neural network based on steepest descent, Levenberg-Marquardt, and quasi-Newton backpropagation learning techniques. The initial solution for the optimization procedure was obtained using limit state design as per IS: 456-2000.
机译:在这项工作中,通过结合梁的实际自重,抛物线应力块,弯矩平衡和可使用性约束以及其他约束,完成了具有均布和集中荷载的单筋和双筋梁的低成本设计。同样,这种设计专业知识被并入了基于最速下降,Levenberg-Marquardt和拟牛顿反向传播学习技术的经过遗传优化的人工神经网络。根据IS:456-2000,使用极限状态设计获得了优化过程的初始解决方案。

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