基于一种优化算法的大坝变形性态模型(英文)

大型水库大坝安全监测系统的监测数据可用来建立多种重要的结构安全状态参数下的预测模型。提出了一种基于基因算法的非线性、退化性态模型。以大坝顶部的上、下游位移作为状态参数为例,以倒垂数据作为数据输出,库水位和气温以及前7,15,30和60d的平均值作为外在影响因子。选取坝高58m、坝长190m的Herculane拱坝的332个数据集作为输入-输出参数,时间序列为2000年1月-2007年6月。从这个数据序列获得回归系数,并进行检验确认。同理,可得其他参数下性态预测模型。这些模型能够解释实时监测中的数据,并能迅速辨识某些情况下的大坝安全潜在风险。

Behaviour model for dam displacement derived by an evolutionary algorithm

Data recorded by the behaviour surveillance system of a large dam are used,among other things,to develop some forecast models for various state parameters which are important for the structural safety.In this paper,a nonlinear behaviour model of regression type is derived using a genetic algorithm based method.The upstream-downstream displacement at the top of the dam is selected as an example of state parameter and the measured pendulum deviations are considered as output data.The reservoir water level and air temperature are accepted as external influence factors,but theirs averaged values on different previous time-periods(7,15,30 and 60 days)are well used.Data sets with 332 values for each input /output parameter were prepared starting from the recorded data at Herculane dam,a 58 m tall and 190 m long arch type dam,between January 2000 and June 2007.The data with even numbers of these series were used to obtain the regression coefficients and all data were then considered for validation.The same procedure may be easily adapted to obtain behaviour models for any other state parameters.These models can then be used to interpret the data recorded in operational surveillance and to quickly identify some situations with potential risks for the dam safety.