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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
引用本文:苏宁,彭士涛,孙瑛,洪宁宁,冯仁德,余波,杨帆. 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[J]. 建筑结构学报, 2019, 40(7): 34-41. DOI: 10.14006/j.jzjgxb.2018.C197
作者姓名:苏宁  彭士涛  孙瑛  洪宁宁  冯仁德  余波  杨帆
作者单位:交通运输部天津水运工程科学研究所,天津300456;哈尔滨工业大学结构灾变与控制教育部重点实验室,黑龙江哈尔滨150090;交通运输部天津水运工程科学研究所,天津,300456;哈尔滨工业大学结构灾变与控制教育部重点实验室,黑龙江哈尔滨,150090;中国电力工程顾问集团西南电力设计院有限公司,四川成都,610021
基金项目:国家重点研发计划(2016YFE0204800),中央级公益性科研院所基本科研业务费专项(TKS160111,TKS1190204)。
摘    要:风荷载是大跨度煤棚结构设计中的主要控制荷载。随着结构抗风研究尤其是风洞试验数据的积累,结合数据挖掘进行结构智能化抗风设计是一种趋势。基于701组工况4581个柱面及球面屋盖风洞试验样本进行数据挖掘和统计分析,建立了脉动风荷载参数的广义回归神经网络预测模型;通过对12480个工况的单、双层柱面及球面网壳结构进行参数化风振响应分析,总结了等效静风荷载的经验表达式;建立了基于人工神经网络预测气动风荷载的主体结构等效静风荷载的抗风设计基本框架,并通过国内某超大跨度干煤棚张弦结构进行了有效性验证。结果表明:采用本文提出的风荷载数据库预测与等效静风荷载方法效率较高,且能够在一定程度包络风振响应分析结果,可用于结构初步设计阶段对主体结构设计风荷载快速预估。

关 键 词:大跨度煤棚  等效静风荷载  人工神经网络  风振响应

Research and application on artificial neural network modeling of design wind load on large-span coal storage sheds
SU Ning,PENG Shitao,SUN Ying,HONG Ningning,FENG Rende,YU Bo,YANG Fan. Research and application on artificial neural network modeling of design wind load on large-span coal storage sheds[J]. Journal of Building Structures, 2019, 40(7): 34-41. DOI: 10.14006/j.jzjgxb.2018.C197
Authors:SU Ning  PENG Shitao  SUN Ying  HONG Ningning  FENG Rende  YU Bo  YANG Fan
Affiliation:1. Tianjin Research Institute for Water Transport Engineering of China Ministry of Transport,Tianjin 300456, China; 2. Key Lab of Structures Dynamic Behavior and Control of theMinistry of Education, Harbin Institute of Technology,  Harbin 150090, China;3. Southwest Electric Power Design Institute Co., Ltd, China Power Engineering Consulting Group, Chengdu 610021, China;
Abstract: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, 4581 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 12480 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.
Keywords:large-span coal storage shed  equivalent static wind load  artificial neural network  wind-induced response  
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