为进行结构抗风可靠度分析，需对作用在结构上平稳随机脉动风场进行模拟。 采用谱表达法描述脉动风场往往涉及成千上万个随机变量，会造成 “维数灾难”问题。 基于此，本文提出一类高维概率空间选点法保障模拟的精度和效率，主要由 3 个步骤组 成: 高维概率空间的分解、正交子空间选点和随机配对得到高维概率空间离散代表点及 赋得概率。采用本文的方法模拟了某 30 层框架剪力墙结构上沿高度变化的水平风场， 研究表明: 本文的方法在二阶统计意义上具有很高的模拟精度和效率，可用少量的代表 性样本构建完备的概率集合，进一步结合概率密度演化法可对结构风振响应进行精细化 概率评价。

To perform wind-resistant reliability analysis of structures，it is necessary to simulate the stationary stochastic fluctuating wind field． Simulating the stochastic fluctuating wind fields through spectral representation method ( SＲM) often involves thousands of random variables，leading to the“curse of dimensionality”． On this basis， this paper proposes a new method for points selection in high-dimensional random-variate space to ensure the accuracy and efficiency of SＲM． The new method mainly consists of three steps: the decomposition of high-dimensional random-variate space，the points selection in the orthogonal subspaces，and the random pairing to obtain the representative points and assigned probabilities in high-dimensional random-variate space． The proposed method is used to investigate the horizontal wind field acting along the height of a 30-story frame-shear wall structure． The results show that the proposed method is of high accuracy and efficiency for the simulation in the second-order statistical sense and a complete probability set can be constructed by only using a small number of representative sample processes． Besides，the proposed method can be further combined with the probability density evolution method to elaborately evaluate the probabilistic response of wind-induced vibration of structures．