Part I [1] of this two-part paper presented the formulation of a novel progressive failure model for pultruded fibre reinforced polymer (FRP) composites, allowing for the 3D simulation of quasi-orthotropic FRP plates as a homogenized material, as well as the model calibration based on a set of standardized material characterization tests. Part II presents the application of that (calibrated) model to the simulation of two case studies: (i) transverse compact tensile (CT) tests; and (ii) web-crippling tests for two load configurations, external two-flanges (ETF) and internal two-flanges (ITF). The CT test, which is often used to determine the (tensile) fracture energy of FRP materials, is especially interesting as it allows assessing the quality of the simulations for a combination of in-plane transverse tensile and shear stresses in a geometry with a sharp singularity. The web-crippling test, on the other hand, is often used to determine the strength of FRP shapes under concentrated transverse loads, a real structural problem involving combined in-plane compressive and shear stresses. In this paper these two relatively complex case studies are used to assess the quality of the simulation in the presence of combined in-plane stresses. The numerical results showed an excellent agreement with their CT test counterparts; the simulation of these experiments were also used to demonstrate the need for using a mesh regularization scheme when modelling problems with singularities. The models were also well able to simulate both web-crippling load configurations, only slightly underestimating the maximum load – this was likely due to the slight underestimation of shear strength for combined in-plane shear and moderate transverse compressive stresses, as discussed in Part I [1], and/or non-quasi-orthotropic behaviour of the web-flange junction. Overall, the numerical results showed a good agreement with the experimental data, even for relatively coarse meshes, attesting the feasibility and precision of the proposed damage progression model.