| 基于进化神经网络的火灾后混凝土强度评估 |
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| 引用本文： | 赵望达,刘勇求.基于进化神经网络的火灾后混凝土强度评估[J].中南林业科技大学学报,2006,26(2):83-86. |
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| 作者姓名： | 赵望达 刘勇求 |
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| 作者单位： | 中南大学土木建筑学院 湖南长沙410075 |
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| 摘 要： | 高温后混凝土强度的评估是判断受火建筑的结构损伤程度、剩余承载力的重要依据.设计了一个进化神经网络模型,用遗传算法优化RBF网络的连接权和网络结构,并将其应用于火灾后混凝土强度的评估.介绍了混凝土立方试块经受不同火温后进行回弹测试混凝土强度的方法.经实验测试和仿真分析证明,所提出的进化算法用于RBF神经网络优化比基于聚类法的RBF网络具有更高的识别精度.
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| 关 键 词： | 土木建筑 火灾 RBF神经网络 遗传算法 混凝土强度 |
| 文章编号： | 1000-2502(2006)02-0083-04 |
| 修稿时间： | 2005年9月20日 |
Assessment of Concrete Intensity after Fire Based on Evolutionary Neural Network |
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| ZHAO Wang-da,LIU Yong-qiu.Assessment of Concrete Intensity after Fire Based on Evolutionary Neural Network[J].Journal of Central South Forestry University,2006,26(2):83-86. |
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| Authors: | ZHAO Wang-da LIU Yong-qiu |
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| Abstract: | Assessment of concrete intensity is an important measurement to judge the damage level and bearing capacity of construction damaged by fire.An evolutionary neural network model optimized by genetic algorithm was applied to assessing the concrete intensity damaged by fire.And an experimental method,the rebound method,was introduced to test the concrete intensity damaged by fire of different temperature.It has been proved that the evolutionary algorithm has a higher evaluation precision than the clustering arithmetic to RBF neural network optimization by experimental test and emulation analysis. |
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| Keywords: | civil engineering fire radial basis function neural network genetic algorithms concrete intensity |
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