In order to identify the upper and lower bounds of distributed force exciting on an uncertain structure, a comprehensive approach combining genetic algorithm based on Latin hypercube sampling (LHS-GA) and improved L-curve method is built up in this paper. For uncertain parameter expressed by interval form, LHS-GA is presented to seeking the maximum and minimum amplitudes of distributed load in the interval. Aiming at distributed load reconstruction for a specific sampling point in the interval, which is the objective function of LHS-GA, the traditional L-curve method is improved to weaken the ill-posedness problem and obtain accurate distributed force amplitude. Numerical example results indicate the comprehensive method is able to acquire precise bounds of distributed load. Meanwhile, it possesses a strong anti-noise performance.

中文翻译：

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基于LHS-GA和改进L曲线法的不确定结构分布式荷载识别

为识别不确定结构上分布力激励的上下界，本文建立了一种结合基于拉丁超立方抽样的遗传算法（LHS-GA）和改进的L曲线法的综合方法。对于区间形式表示的不确定参数，提出LHS-GA求区间内分布载荷的最大和最小幅值。针对区间内特定采样点的分布载荷重构，即LHS-GA的目标函数，对传统的L曲线方法进行改进，以削弱不适定性问题，获得准确的分布力幅值。数值示例结果表明，综合方法能够获得分布式负载的精确边界。同时，它具有很强的抗噪性能。