Implications of spatial variability characterization in discrete particle models

Characterization of the inherent spatial variability of the mechanical properties of advanced composite materials as well as of standard concretes is among the primary concerns of the presented paper. In the context of discrete particle models the random field concept is frequently adopted in order to account for the fluctuations of material characteristics on scales independent of the geometrical characterization of the mesostructure as represented by particular particle configurations mimicking the aggregate placement. The specific goal of this paper is to introduce spatial variability into structural reliability by providing a sample reduction strategy for the discrete framework. Presented simulations of a classical experiment utilize the well-established Lattice Discrete Particle Model (LDPM) and demonstrate the general potential of the proposed strategy towards current state-of-the-art in stochastic mechanics and some relating open problems.

[1]  Roman Wendner,et al.  Characterization of concrete failure behavior: a comprehensive experimental database for the calibration and validation of concrete models , 2014 .

[2]  G. Cusatis,et al.  Lattice Discrete Particle Model (LDPM) for failure behavior of concrete. I: Theory , 2011 .

[3]  Mark G. Stewart,et al.  Spatial variability of pitting corrosion and its influence on structural fragility and reliability of RC beams in flexure , 2004 .

[4]  Christian Meyer,et al.  Fracture Mechanics of ASR in Concretes with Waste Glass Particles of Different Sizes , 2000 .

[5]  Ton Vrouwenvelder,et al.  The JCSS probabilistic model code , 1997 .

[6]  Robert E. Melchers,et al.  Structural Reliability: Analysis and Prediction , 1987 .

[7]  Gianluca Cusatis,et al.  Lattice Discrete Particle Modeling (LDPM) of Alkali Silica Reaction (ASR) deterioration of concrete structures , 2013 .

[8]  Alfred Strauss,et al.  Stochastic fracture‐mechanical parameters for the performance‐based design of concrete structures , 2014 .

[9]  Sanjay R. Arwade,et al.  Computational Analysis of Randomness in Structural Mechanics , 2011 .

[10]  George Deodatis,et al.  Identification of critical samples of stochastic processes towards feasible structural reliability applications , 2014 .

[11]  Gordon A. Fenton,et al.  Influence of spatial variability on slope reliability using 2-D random fields. , 2009 .

[12]  Gianluca Cusatis,et al.  Lattice Discrete Particle Model for Fiber-Reinforced Concrete. I: Theory , 2012 .

[13]  Bruce R. Ellingwood,et al.  Risk‐benefit‐based design decisions for low‐probability/high consequence earthquake events in Mid‐America , 2005 .

[14]  Konrad Bergmeister,et al.  Robustness‐based performance assessment of a prestressed concrete bridge , 2014 .

[15]  G. Cusatis,et al.  High-Order Microplane Theory for Quasi-Brittle Materials with Multiple Characteristic Lengths , 2014 .

[16]  Werner G. Müller,et al.  Collecting Spatial Data: Optimum Design of Experiments for Random Fields , 1998 .

[17]  Yunping Xi,et al.  Statistical Size Effect in Quasi-Brittle Structures: I. Is Weibull Theory Applicable? , 1991 .

[18]  C. Bucher Adaptive sampling — an iterative fast Monte Carlo procedure , 1988 .

[19]  Bruce R. Ellingwood Structural safety special issue: General-purpose software for structural reliability analysis , 2006 .

[20]  Jan Podroužek,et al.  SPATIAL DEGRADATION IN RELIABILITY ASSESSMENT OF AGEING CONCRETE STRUCTURES , 2015 .

[21]  D. Novák,et al.  Small-sample probabilistic assessment-FREET software , 2003 .

[22]  G. Cusatis,et al.  Lattice Discrete Particle Model (LDPM) for failure behavior of concrete. II: Calibration and validation , 2011 .

[23]  Rostislav Chudoba,et al.  Stochastic modeling of multi-filament yarns : II. Random properties over the length and size effect , 2006 .