Validation of a simplified micromodel for analysis of infilled RC frames exposed to cyclic lateral loads

An RC frame structure with masonry infill walls (‘‘framed-masonry’’) exposed to lateral loads acts as a composite structure. Numerical simulation of framed-masonry is difficult and generally unreliable due to many difficulties and uncertainties in its modelling. In this paper, we reviewed the usability of an advanced non-linear FEM computer program to accurately predict the behaviour of framed-masonry elements when exposed to cyclic lateral loading. Numerical results are validated against the test results of framedmasonry specimens, with and without openings. Initial simplified micromodels were calibrated by adjustment of the input parameters within the physically justifiable borders, in order to obtain the best correlation between the experimental and numerical results. It has been shown that the use of simplified micromodels for the investigation of composite masonry-infilled RC frames requires in-depth knowledge and engineering judgement in order to be used with confidence. Modelling problems were identified and explained in detail, which in turn offer an insight to practising engineers on how to deal with them

Verification of an agent-based disease model of human mycobacterium tuberculosis infection

Agent-Based Models are a powerful class of computational models widely used to simulate complex phenomena in many different application areas. However, one of the most critical aspects, poorly investigated in the literature, regards an important step of the model credibility assessment: solution verification. This study overcomes this limitation by proposing a general verification framework for Agent-Based Models that aims at evaluating the numerical errors associated with the model. A step-by-step procedure, which consists of two main verification studies (deterministic and stochastic model verification), is described in detail and applied to a specific mission critical scenario: the quantification of the numerical approximation error for UISS-TB, an ABM of the human immune system developed to predict the progression of pulmonary tuberculosis. Results provide indications on the possibility to use the proposed model verification workflow to systematically identify and quantify numerical approximation errors associated with UISS-TB and, in general, with any other ABMs

Methodology for characterizing modeling and discretization uncertainties in computational simulation

This research effort focuses on methodology for quantifying the effects of model uncertainty and discretization error on computational modeling and simulation. The work is directed towards developing methodologies which treat model form assumptions within an overall framework for uncertainty quantification, for the purpose of developing estimates of total prediction uncertainty. The present effort consists of work in three areas: framework development for sources of uncertainty and error in the modeling and simulation process which impact model structure; model uncertainty assessment and propagation through Bayesian inference methods; and discretization error estimation within the context of non-deterministic analysis

Uncertainty and error in computational simulations

The present paper addresses the question: ``What are the general classes of uncertainty and error sources in complex, computational simulations?`` This is the first step of a two step process to develop a general methodology for quantitatively estimating the global modeling and simulation uncertainty in computational modeling and simulation. The second step is to develop a general mathematical procedure for representing, combining and propagating all of the individual sources through the simulation. The authors develop a comprehensive view of the general phases of modeling and simulation. The phases proposed are: conceptual modeling of the physical system, mathematical modeling of the system, discretization of the mathematical model, computer programming of the discrete model, numerical solution of the model, and interpretation of the results. This new view is built upon combining phases recognized in the disciplines of operations research and numerical solution methods for partial differential equations. The characteristics and activities of each of these phases is discussed in general, but examples are given for the fields of computational fluid dynamics and heat transfer. They argue that a clear distinction should be made between uncertainty and error that can arise in each of these phases. The present definitions for uncertainty and error are inadequate and. therefore, they propose comprehensive definitions for these terms. Specific classes of uncertainty and error sources are then defined that can occur in each phase of modeling and simulation. The numerical sources of error considered apply regardless of whether the discretization procedure is based on finite elements, finite volumes, or finite differences. To better explain the broad types of sources of uncertainty and error, and the utility of their categorization, they discuss a coupled-physics example simulation