空间不确定性广泛存在于土木、机械、材料、航空航天等诸多工程领域，构建此类参数的高精度不确定性度量模型，是保障工程结构设计安全可靠的重要基础。区间场模型以上下边界而非精确概率分布的形式描述参数的不确定性，可避免传统随机场模型在实际工程应用中因样本数据不足、参数分布范围不匹配等引起的一系列瓶颈问题，在结构空间不确定性度量中展现出很好的应用前景。本项目拟基于现有研究，发展出一种新的度量空间不确定性的区间场模型，解决现有区间场在空间相关性构建及模型表征方面存在的若干关键科学问题，并给出高效的区间有限元方法进行结构空间不确定性分析。主要研究区间场模型的构建、区间场的正交级数展开、区间有限元高效分析三类关键科学问题，并对相关模型和算法进行系统集成与应用验证。本项目的完成将为区间场模型建立重要的理论基础，并有望为未来复杂结构的空间不确定性分析与高可靠性设计提供一条新的研究思路。

Spatial uncertainties are widely encountered in many engineering areas, including civil engineering, mechanical engineering, material science, aeronautics and astronautics, etc. Construction of the uncertainty quantification model for these parameters with high precision acts as an important foundation in ensuring the safety and reliability of engineering structures. Interval field model depicts the uncertainty of a parameter by its upper and lower bounds rather than the precise probability distribution, which can avoid the bottlenecks caused by the lack of sample data and mismatch of parameter distribution in the actual engineering application of traditional random field model, and has shown an appreciable prospect in structural spatial uncertainty quantification. This project intends to develop a new interval field model for spatial uncertainty quantification based on existing researches, aiming to address the crucial scientific issues of the existing interval field in spatial correlation quantification and model representation. Based on this, the interval finite element methods to efficiently perform the structural spatial uncertainty analysis are to be developed. Three key issues including construction of the interval field model, orthogonal series expansion of the interval field, and efficient interval finite element analysis methods will be studied, based on which the system integration and application verification of related models and algorithms will be carried out. The completion of this project will establish an important theoretic foundation for interval field model, and it is expected to provide a new research approach for spatial uncertainty analysis and high reliability design of complex structures in future.