Neural Network Model for Optimization of Cold-Formed Steel Beams
Neural Network Model for Optimization of Cold-Formed Steel Beams
复制标题
冷弯型钢梁优化的神经网络模型
DOI：
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发表时间：
1997
期刊：
影响因子：
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通讯作者：
Asim Karim
中科院分区：
文献类型：
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作者：
H. Adeli;Asim Karim
冷弯型钢的一个重要优点是结构钢设计师可以获得更大的横截面形状和尺寸灵活性。然而，缺乏标准的优化形状使得选择最经济的形状非常困难，如果不是不可能的话。这项任务因其设计规则的复杂性和高度非线性性质而进一步复杂化。本文提出了冷弯薄壁钢梁优化设计的通用数学公式和计算模型。非线性优化问题的解决，通过适应最近发展的鲁棒神经动力学模型。设计的基础可以是美国钢铁协会（AIS）、许用应力设计（ASD）或荷载和阻力系数设计（LRFD）规范。计算模型已被应用到三种不同的常用类型的横截面形状：帽子，I-和Z-形状。的鲁棒性和通用性的方法已被证明通过应用到三个不同的例子。该研究为冷弯型材结构的自动优化设计奠定了数学基础。结果将是更经济地使用冷成型钢。
An important advantage of cold-formed steel is the greater flexibility of cross-sectional shapes and sizes available to the structural steel designer. However, the lack of standard optimized shapes makes the selection of the most economical shape very difficult if not impossible. The task is further complicated by the complex and highly nonlinear nature of the rules that govern their design. A general mathematical formulation and computational model is presented for optimization of cold-formed steel beams. The nonlinear optimization problem is solved by adapting the robust neural dynamics model developed recently. The basis of the design can be American Iron and Steel Institute (AIS), allowable stress design (ASD), or load and resistance factor design (LRFD) specifications. The computational model has been applied to three different commonly used types of cross-sectional shapes: hat-, I-, and Z-shapes. The robustness and generality of the approach have been demonstrated by application to three different examples. This research lays the mathematical foundation for automated optimum design of structures make of cold-formed shapes. The result would be more economical use of cold-formed steel.