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We develop a Bayesian Inference (BI) of a non-linear multiscale model and material parameters using experimental composite coupons tests as observation data. In particular we consider non-aligned Short Fibers Reinforced Polymer (SFRP) as a composite material system and Mean-Field Homogenization (MFH) as a multiscale model. Although MFH is computationally efficient, when considering non-aligned inclusions, the evaluation cost of a non-linear response for a given set of model and material parameters remains too prohibitive to be coupled with the sampling process required by the BI. Therefore, a Neural-Networktype (NNW) is first trained using the MFH model, and is then used as a surrogate model during the BI process, making the identification process affordable.

We present a stochastic approach combining Bayesian Inference (BI) with homogenization theories in order to identify, on the one hand, the parameters inherent to the model assumptions and, on the other hand, the composite material constituents behaviors, including their variability. In particular, we characterize the model parameters of a Mean-Field Homogenization (MFH) model and the elastic matrix behavior, including the inherent dispersion in its Young's modulus, of non-aligned Short Fibers Reinforced Polymer (SFRP) composites. The inference is achieved by considering as observations experimental tests conducted at the SFRP composite coupons level. The inferred model and material law parameters can in turn be used in Mean-Field Homogenization (MFH)-based multi-scale simulations and can predict the confidence range of the composite material responses.

significantly affected by Cm. From the predictive error analysis it is seen that the optimal parameters set to capture the orientation state of the specimen is: (i) for the FT model, Ci = 0.005, alpha-RPR = 0.7 and (ii) for the iARD model, Ci = 0.005, Cm = 0.2, and alpha-RPR = 0.7.

A material based on recycled rubber has been developed to use as a protective coating on road barriers with the aim of improving motorcyclists' security against crash impacts. This material is based on grounded rubber from used tires added by extrusion using low-density polyethylene as adhesive. Compression tests have been performed for different densities of the recycled material to fully describe the mechanical characteristics under high strain rates (in the rank 0.057–5.7 s−1), and a constitutive model composed of a hyperelastic Mooney Rivlin part and a viscoelastic part based on the generalized Maxwell model has been taken to characterize this behavior. Hyperelastic parameters have been obtained by means of the least-squares fitting technique, and particle swarm optimization (PSO) has been used to obtain viscoelastic parameters. The PSO algorithm is shown to be a good optimization method, simple, versatile, and consisting of few parameters that accelerate to the optimal solution. Therefore, this article presents a new and efficient approach to obtaining the parameters for the viscoelastic model. The behavior of the experimental material confirms the theoretically obtained results, so the procedure presented in the article is validated successfully.