差异演化算法用于单桩承载力指数曲线模型优化
Application of differential evolution to optimize exponential curve model of ultimate bearing capacity of single pile
-
摘要: 预测挤扩支盘桩单桩极限承载力的指数曲线模型,其函数表达式是一个复杂非线性方程,采用传统的优化方法对模型参数进行回归处理往往因为计算复杂和人为因素的影响,使得预测结果带有较大的误差。为此,利用混沌(Chaos)的遍历性产生初始群体,并自适应地调整缩放因子和选取差异演化模式,对差异演化(DE)算法进行改进,提出自适应加速差异演化(AADE)算法,并将其用于指数曲线模型参数和理论极限承载力优化中。结合工程实例,对挤扩支盘桩静载荷试验实测数据进行拟合计算与分析,结果表明:与其他方法相比,AADE算法能够更好地拟合实测数据和有效地预测单桩极限承载力,且AADE算法具有求解速度快、计算精度高、算法控制参数设置简便、通用性强等优点。Abstract: The expression of exponential curve model which is used to forecast the ultimate bearing capacity of single squeezed branch pile is a complicate and nonlinear function. The predicted results of traditional optimization methods for the parameter regression of the exponential curve model are often obtained with a great deviation because of computational complexity and artificial factors. Therefore, the adaptive accelerating differential evolution (AADE) improved by chaos initialization and adaptive adjustment of the scale factor and differential evolution strategy generation by generation is proposed, and then it is used to solve the nonlinear optimization of the model parameters and theoretical ultimate bearing capacity. Fitting calculation and analysis of measured data which is from the static load tests on squeezed branch piles is displayed. The results show that: compared with other methods, the AADE fits the measured data better and predicts the single pile ultimate bearing capacity effectively, and furthermore, it has many good properties such as fast computing speed, high accuracy, easy control variables setting, high universality, etc.