In this paper the coefficient and law of the size effect of RPC were studied through experiments and theoretical analysis. The size-effect coefficients for the compressive strength of RPC are deduced through experimen...In this paper the coefficient and law of the size effect of RPC were studied through experiments and theoretical analysis. The size-effect coefficients for the compressive strength of RPC are deduced through experiments.They indicate that RPC without fiber behaves quite the same as normal or high strength concrete.The size effect on compressive strength is more prominent in RPC containing fiber.Bazant's size effect formula of compressive strength applies to RPC.A formula is given to predict the compressive strength of cubic RPC specimens 100 mm on a side where the fiber dosage ranges from 0-2%.展开更多
针对大坝变形常规统计预报模型在监测信息挖掘时的优势单一性及预报精度欠佳等问题,视大坝变形观测资料为非平稳时间序列,从影响大坝变形的因素出发,将其分为周期性影响因素与随机影响因素,利用多尺度小波分析方法将大坝变形监测序列分...针对大坝变形常规统计预报模型在监测信息挖掘时的优势单一性及预报精度欠佳等问题,视大坝变形观测资料为非平稳时间序列,从影响大坝变形的因素出发,将其分为周期性影响因素与随机影响因素,利用多尺度小波分析方法将大坝变形监测序列分解并重构,结合BP神经网络与自回归积分滑动平均模型(Autoregressive Integrated Moving Average Model,简记ARIMA)对其随机信号与系统信号分项训练预报,并将其预报值相叠加,据此,应用时间序列原理提出了一种基于BP-ARIMA的混凝土坝多尺度变形组合预报模型。工程实例分析表明,所建组合模型较常规模型能够有效挖掘监测信息中所蕴含的有效成分,预报精度显著提升,且计算分析过程简便,为高边坡及水工建筑物中其他监测指标的预报提供了新方法。展开更多
基金Project 50508005 supported by the National Natural Science Foundations of China
文摘In this paper the coefficient and law of the size effect of RPC were studied through experiments and theoretical analysis. The size-effect coefficients for the compressive strength of RPC are deduced through experiments.They indicate that RPC without fiber behaves quite the same as normal or high strength concrete.The size effect on compressive strength is more prominent in RPC containing fiber.Bazant's size effect formula of compressive strength applies to RPC.A formula is given to predict the compressive strength of cubic RPC specimens 100 mm on a side where the fiber dosage ranges from 0-2%.
文摘针对大坝变形常规统计预报模型在监测信息挖掘时的优势单一性及预报精度欠佳等问题,视大坝变形观测资料为非平稳时间序列,从影响大坝变形的因素出发,将其分为周期性影响因素与随机影响因素,利用多尺度小波分析方法将大坝变形监测序列分解并重构,结合BP神经网络与自回归积分滑动平均模型(Autoregressive Integrated Moving Average Model,简记ARIMA)对其随机信号与系统信号分项训练预报,并将其预报值相叠加,据此,应用时间序列原理提出了一种基于BP-ARIMA的混凝土坝多尺度变形组合预报模型。工程实例分析表明,所建组合模型较常规模型能够有效挖掘监测信息中所蕴含的有效成分,预报精度显著提升,且计算分析过程简便,为高边坡及水工建筑物中其他监测指标的预报提供了新方法。
