Norambuena-Contreras, J., Gonzalez-Torre, I., Vivanco, J. F., & Gacitua, W. (2016). Nanomechanical properties of polymeric fibres used in geosynthetics. Polym. Test, 54, 67–77.
Abstract: Geosynthetics are composite materials manufactured using different types of polymeric fibres, usually employed as anti-reflective cracking systems in asphalt pavements. Materials that compose geosynthetics can be damaged due to mechanical and thermal effects produced during the installation process under hot mix asphalts. In this paper, different polymeric fibres extracted from geosynthetics have been evaluated using nanoindentation tests. The main objective was to evaluate the effect of installation process (dynamic compaction and thermal damage) on the mechanical behaviour of individual polymeric fibres at nano-scale. To do this, elastic modulus (E) and hardness (H) of three different polymeric fibres commonly used in geosynthetics (polypropylene, polyester and polyvinyl-alcohol), in two testing directions and under two different states have been studied. Main conclusions of this work are that mechanical properties of geosynthetics individual fibres can change after installation, producing changes in the behaviour of geosynthetics at macro-scale with consequences in the pavement functionality, and that these changes are different depending on the material that composed the fibres. (C) 2016 Elsevier Ltd. All rights reserved.
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Bevilacqua, M., Caamano-Carrillo, C., Arellano-Valle, R. B., & Gomez, C. (2022). A class of random fields with two-piece marginal distributions for modeling point-referenced data with spatial outliers. Test, 31(3), 644–674.
Abstract: In this paper, we propose a new class of non-Gaussian random fields named two-piece random fields. The proposed class allows to generate random fields that have flexible marginal distributions, possibly skewed and/or heavy-tailed and, as a consequence, has a wide range of applications. We study the second-order properties of this class and provide analytical expressions for the bivariate distribution and the associated correlation functions. We exemplify our general construction by studying two examples: two-piece Gaussian and two-piece Tukey-h random fields. An interesting feature of the proposed class is that it offers a specific type of dependence that can be useful when modeling data displaying spatial outliers, a property that has been somewhat ignored from modeling viewpoint in the literature for spatial point referenced data. Since the likelihood function involves analytically intractable integrals, we adopt the weighted pairwise likelihood as a method of estimation. The effectiveness of our methodology is illustrated with simulation experiments as well as with the analysis of a georeferenced dataset of mean temperatures in Middle East.
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