This thesis primarily discuss about the development of design analysis framework for high-dimensional problems that includes reliability analysis (RA), sensitivity analysis (SA), and uncertainty quantification (UQ). Monte Carlo Simulation (MCS) is commonly used to perform the analysis, but instead of using MCS solely, a metamodel called Kriging was used to assist the design analysis process. Usually UQ, SA, and RA need numerous function evaluation, however the implementation of metamodel could help alleviate this problem by substituting the function evaluation with the metamodel prediction. For very high-dimensional problem e.g. 20 design variables or more, the use of Kriging is computationally expensive. Therefore, a dimensionality reduction method is further employed to modify the Kriging kernel function resulting in significant speed gain while maintaining the accuracy performance of the ordinary Kriging. Later, the framework was tested on a various functions ranging from 10 dimensions to 100 dimensions in three applications, UQ, SA, and RA. The result shows that when the dimensionality of the function is insufficiently high Kriging with Partial Least Square (KPLS) only slightly worse to ordinary Kriging (OK) in terms of accuracy of the applications. However, when the dimensionality is high and the number of sampling points is low KPLS outperforms OK in terms of accuracy and the execution time of those three applications.