The reliability-based design optimization (RBDO) problem involving discrete design variables and multi-working constraints is tackled with the consideration of the uncertainty factors in the optimal design of crawler crane’s lattice boom. In the optimization model, the uncertainties of material property, geometric parameters and loads are represented by random variables. The geometrical dimensions, performance functions’ reliability indexes and the minimum weight of lattice boom are defined respectively as the random design variables, probabilistic constraints and objective function. Integrating the reliability analysis method based on response surface method (RSM) with multi-island genetic algorithm (MIGA), an efficient reliability-based design optimization process for the lattice boom is developed under the typical constrained working conditions. Several verification cases are presented to demonstrate the effectiveness of optimization results. It is shown that the reliability-based design optimization method can not only satisfy the reliability constraints of the lattice boom, but also ensure the safety and economy of crawler crane boom as well as minimize the structure weight. The developed method is capable to be extended to the reliability-based design optimization of other large and complex construction machinery structures.

在履带起重机的桁架臂优化设计中，考虑了不确定性因素，解决了涉及离散设计变量和多重工作约束的基于可靠性的设计优化（RBDO）问题。在优化模型中，材料特性，几何参数和载荷的不确定性由随机变量表示。几何尺寸，性能函数的可靠性指标和桁架臂的最小重量分别定义为随机设计变量，概率约束和目标函数。将基于响应面法（RSM）的可靠性分析方法与多岛遗传算法（MIGA）集成在一起，在典型的受限工作条件下，开发了一种有效的基于可靠性的晶格臂设计优化过程。提出了几个验证案例，以证明优化结果的有效性。结果表明，基于可靠性的设计优化方法不仅可以满足格构吊臂的可靠性约束，而且可以确保履带吊臂的安全性和经济性，并最大程度地减轻结构重量。该方法可以扩展到其他大型复杂工程机械结构的基于可靠性的设计优化。