基于ANFIS的裂缝开度不利荷载组合提取方法

Data Mining Approach to Unfavorable Load Combination of Crack Using ANFIS

水工混凝土结构运行期裂缝的产生和发展主要受水压和温度变化的影响,利用基于自适应神经模糊推理系统(ANFIS)的数据挖掘方法,提取运行期裂缝开度的不利荷载组合。与通常是根据经验和实测资料分析决定水位和温度不利组合的方法相比,避免了人为主观因素的影响。通过对某一工程实例的应用分析表明,该方法分析所得的结论与实际观测的结果完全相符,并能得出显示规则,便于实际应用。

The cause and progression of a crack in operation in important hydraulic concrete structures are affected by hydrostatic pressure and temperature. Different combinations have different influences on the crack. A new data mining approach to the unfavorable load combinations of the crack is presented using adaptive -network based on fuzzy inference system (ANFIS). Compared with the method to extract unfavorable load combinations by experience and measured data, this method can avoid the impact of subject. The calculated results obtained from some engineering illustration show that the conclusion obtained by this method is in good agreement with observed data. The rule of the unfavorable load combination of the crack is generated.