  This study introduces a novel computational framework for Robust Topology
Optimization (RTO) considering imprecise random field parameters. Unlike the
worst-case approach, the present method provides upper and lower bounds for the
mean and standard deviation of compliance as well as the optimized topological
layouts of a structure for various scenarios. In the proposed approach, the
imprecise random field variables are determined utilizing parameterized p-boxes
with different confidence intervals. The Karhunen-Lo\`eve (K-L) expansion is
extended to provide a spectral description of the imprecise random field. The
linear superposition method in conjunction with a linear combination of
orthogonal functions is employed to obtain explicit mathematical expressions
for the first and second order statistical moments of the structural
compliance. Then, an interval sensitivity analysis is carried out, applying the
Orthogonal Similarity Transformation (OST) method with the boundaries of each
of the intermediate variable searched efficiently at every iteration using a
Combinatorial Approach (CA). Finally, the validity, accuracy, and applicability
of the work are rigorously checked by comparing the outputs of the proposed
approach with those obtained using the particle swarm optimization (PSO) and
Quasi-Monte-Carlo Simulation (QMCS) methods. Three different numerical examples
with imprecise random field loads are presented to show the effectiveness and
feasibility of the study.
