大跨度煤棚主体结构设计风荷载的神经网络建模研究及应用

Wind load is the main control loads on the design of large-span coal storage sheds. With the researching accumulation of wind resistance of structures, especially with the accumulation of wind tunnel data, intellectual wind-resistant design using the concept of data mining gained increasing popularity. Based on 701 cases, 4 581 samples of wind tunnel data of cylindrical and spherical roofs, data mining and statistical analyses were carried out, and the generalized regression neural network for the prediction of wind load parameters was established. With 12 480 cases of parametric wind-induced response analyses on single- and double- layer cylindrical and spherical latticed shells, the empirical estimation method of the equivalent static wind load was proposed. Finally, a basic framework of wind-resistant design for the main load-bearing system was built by combining the artificial neural network for aerodynamic wind load and empirical prediction for equivalent static wind load. The effectiveness of the method was proved by the application on the prestressed latticed shell structure of a domestic super-large-span coal storage. It can be concluded that the presented method can provide an efficient estimation of the design wind load which can envelope the wind-induced response analysis results. The method can be used in quick estimating the design wind load in the preliminary design stage.