| 基于进化神经网络的超声回弹综合法火灾后混凝土强度的评定 |
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| 引用本文： | 赵望达,刘勇求.基于进化神经网络的超声回弹综合法火灾后混凝土强度的评定[J].铁道科学与工程学报,2006,3(3):31-34. |
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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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| 基金项目： | 湖南省科技厅项目(02SSY3027) |
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| 摘 要： | 基于火灾高温后混凝土强度的评定是判断火灾后建筑结构损伤程度、剩余承载力的重要依据,设计了一个进化神经网络模型,用遗传进化算法优化RBF网络的连接权和网络结构,并将其应用于火灾后混凝土抗压强度的评定,给出了混凝土强度测试的实验方法。研究结果表明,所提出的进化神经网络比回归计算方法具有更高的识别精度和较强的实用性。
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| 关 键 词： | 火灾 超声回弹综合法 RBF神经网络 遗传算法 混凝土抗压强度 |
| 文章编号： | 1672-7029(2006)03-0031-04 |
| 修稿时间： | 2005年12月15 |
Assessment of concrete strength by ultrasonic and rebound combined method after fire based on evolutionary neural network |
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| ZHAO Wang-da,LIU Yong-qiu.Assessment of concrete strength by ultrasonic and rebound combined method after fire based on evolutionary neural network[J].Journal of Railway Science and Engineering,2006,3(3):31-34. |
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| Authors: | ZHAO Wang-da LIU Yong-qiu |
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| Abstract: | Assessment of compressive strength of concrete is an important issue of damage degree and bearing capacity of construction damaged by fire.An evolutionary neural network model optimized by genetic algorithm was applied to assessing compressive strength of concrete by ultrasonic and rebound combined method after fire.An experimental method was given for compressive strength of concrete test by ultrasonic and rebound combined method.It is proved that evolutionary algorithm is more useful and has higher evaluation precision than that of regression calculation by experimental test and emulation analysis. |
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| Keywords: | fire ultrasonic and rebound combined method radial basis function neural network genetic algorithm compressive strength of concrete |
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