Search

In this work, a multi-scale model established from the concept of periodic homogenization is utilized to predict the cyclic and time-dependent response of thermoplastic-based woven composites. The macroscopic behaviour of the composite is determined from finite element simulations of the representative unit cell of the periodic microstructure, where the local non-linear constitutive laws of the components are directly integrated, namely, the matrix and the yarns. The thermoplastic matrix is described by a phenomenological multi-mechanisms constitutive model accounting for viscoelasticity, viscoplasticity and ductile damage. For the yarns, a hybrid micromechanical–phenomenological constitutive model accounting for anisotropic damage and anelasticity induced by the presence of a diffuse micro-crack network is utilized. The capabilities of the overall multi-scale model are validated by comparing the numerical predictions with experimental data. Further illustrative examples are also provided, where the composite undergoes time-dependent deformations under uni-axial and non-proportional multi-axial loading paths. The multi-scale model is also employed to analyze the influence of the local deformation processes on the macroscopic response of the composite. This paper is part of the COPERSIM project managed by IRT Jules Verne (French Institute in Research and Technology in Advanced Manufacturing Technologies for Composite, Metallic and Hybrid Structures). The authors wish to associate the industrial and academic partners of this project, respectively, Arts et Metiers Institute of Technology, Solvay, Plastic Omnium, PSA and Renault.

Quantitative risk assessments of debris flows and other hydrogeological hazards require the analyst to predict damage potentials. A common way to do so is by use of proportional loss functions. In this paper, we analyze a uniquely rich dataset of 132 buildings that were damaged in one of five large debris flow events in Switzerland. Using the double generalized linear model, we estimate proportional loss functions that may be used for various prediction purposes including hazard mapping, landscape planning, and insurance pricing. Unlike earlier analyses, we control for confounding effects of building characteristics, site specifics, and process intensities as well as for overdispersion in the data. Our results suggest that process intensity parameters are the most meaningful predictors of proportional loss sizes. Cross-validation tests suggest that the mean absolute prediction errors of our models are in the range of 11%, underpinning the accurateness of the approach.