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The physical and mechanical performance of concrete is inherently dependent on aggregate morphology including the general shape at coarse-scale, the local roundness at medium-scale and the surface texture at fine-scale. The authors developed a multiscale computational method for highly efficient generation of realistic 3D aggregates using micro X-ray Computed Tomography (μXCT) images, the spherical harmonic (SH) analysis and a random-field reconstruction algorithm. In this method, the real aggregate surface segmented from CT images is first decomposed and mapped by the SH function to radius fields at different scales, whose statistical data are then extracted to reconstruct new aggregates through a spectral representation method and an innovative folding algorithm. The proposed method is verified by comparison of four morphology indices (sphericity, convexity, roundness and roughness) of the real aggregate and the new ones, and its flexibility in reconstructing new sets of random aggregates with specified morphology indices at selected scales is also demonstrated. As an application, eight sets of Fuller-grade random aggregates with target morphology indices are constructed and packed into a cube container to generate digital concrete specimens. The aggregate sphericity and convexity are found to be linearly correlated with the maximum aggregate volume fraction that can be packed into the cube. The developed method can be also applied to other granular materials, such as pharmaceutical particles, colloids, ceramics, soils and coal.