通过分析7座在役混凝土大坝不同部位混凝土碳化深度检测结果,对比了Lesache de Fontenay模型、Smol⁃czyk模型、中国建筑科学研究院模型的混凝土碳化深度计算结果,得出Smolczyk模型预测在役混凝土结构碳化深度与实测值契合度较高,并根据...通过分析7座在役混凝土大坝不同部位混凝土碳化深度检测结果,对比了Lesache de Fontenay模型、Smol⁃czyk模型、中国建筑科学研究院模型的混凝土碳化深度计算结果,得出Smolczyk模型预测在役混凝土结构碳化深度与实测值契合度较高,并根据工程检测结果对Smolczyk模型进行了修正,建立并验证了基于混凝土强度检测结果的在役混凝土大坝碳化深度预测模型,根据碳化深度预测模型推导出了碳化寿命预测模型。展开更多
Firstly,neural network based on improved particle swarm optimization (PSO)algorithm is introduced in this paper. Based on the data collected from projects in typical areas,the concrete carbonation depth is assessed wi...Firstly,neural network based on improved particle swarm optimization (PSO)algorithm is introduced in this paper. Based on the data collected from projects in typical areas,the concrete carbonation depth is assessed with consideration of various factors such as unit cement consumption (C),unit water consumption (W),binder material content (B),water binder ratio (W/B ),concrete strength (MPa),rapid carbonization days (D),fly ash consumption of unit volume concrete(FA),fly ash percentage of total cementitious materials (FA%),expansion agent consumption of unit volume concrete(EA),expansion agent percentage of total cementitious materials (FA%).Gaining the data from project-experiment,a model is presented to calculate and forecast carbonation depth using neural network based on improved PSO algorithm. The calculation results indicate that this algorithm accord with the prediction carbonation depth of concrete accuracy requirements and has a better convergence and generalization,worth being popularized.展开更多
文摘通过分析7座在役混凝土大坝不同部位混凝土碳化深度检测结果,对比了Lesache de Fontenay模型、Smol⁃czyk模型、中国建筑科学研究院模型的混凝土碳化深度计算结果,得出Smolczyk模型预测在役混凝土结构碳化深度与实测值契合度较高,并根据工程检测结果对Smolczyk模型进行了修正,建立并验证了基于混凝土强度检测结果的在役混凝土大坝碳化深度预测模型,根据碳化深度预测模型推导出了碳化寿命预测模型。
文摘Firstly,neural network based on improved particle swarm optimization (PSO)algorithm is introduced in this paper. Based on the data collected from projects in typical areas,the concrete carbonation depth is assessed with consideration of various factors such as unit cement consumption (C),unit water consumption (W),binder material content (B),water binder ratio (W/B ),concrete strength (MPa),rapid carbonization days (D),fly ash consumption of unit volume concrete(FA),fly ash percentage of total cementitious materials (FA%),expansion agent consumption of unit volume concrete(EA),expansion agent percentage of total cementitious materials (FA%).Gaining the data from project-experiment,a model is presented to calculate and forecast carbonation depth using neural network based on improved PSO algorithm. The calculation results indicate that this algorithm accord with the prediction carbonation depth of concrete accuracy requirements and has a better convergence and generalization,worth being popularized.
