.西安建筑科技大学 信息与控制工程学院，西安 710055
2.西安建筑科技大学 土木工程学院，西安 710055
1.School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
2.School of Civil Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
GAO Panxiang, YU Junqi, NIU Ditao, et al. Research and application on neural network in concrete carbonation
depth prediction. Computer Engineering and Applications, 2014, 50（14）：238-241.
Abstract：For the defects on search speed slow and ease into the local minima of the BP neural network, the model about
each factor and the length of the partial carbonation zone is established, which uses the PSO algorithm to optimize the BP
neural network. Based on the experimental simulation training, the improved model is applied to the part location of a concrete bridge on carbonation depth prediction and the simulation application results show that the network, having faster
convergence rate, output values and expectations is a good match. So the model can predict the length of the partial carbonation zone of the reinforced concrete, which offers scientific guidance to durability design, assessment of concrete
structures and life prediction.
Key words：neural network; concrete; carbonation depth; structural durability
摘 要：针对 BP 神经网络搜索速度慢、易陷入局部极值的缺陷，采用 PSO 算法优化 BP 神经网络后建立各影响因素
与部分碳化区长度的关系模型。将改进后的模型进行实验仿真训练并应用于某混凝土大桥部分位置的碳化深度预
测中，仿真应用结果表明，网络输出值和期望值很好吻合，收敛速度更快。所以该模型能够对混凝土部分碳化区长
度进行预测，为混凝土结构耐久性设计、评估和寿命预测提供科学指导。
关键词：神经网络；混凝土；碳化深度；结构耐久性
文献标志码：A 中图分类号：TP391；TU17 doi：10.3778/j.issn.1002-8331.1207-0450

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