Finite element model correlation and calibration of historic masonry monuments: review

Building a reliable finite element (FE) model of historic masonry structures is a difficult undertaking due to the challenges in accurate representation of irregular geometry, complex material behaviour and complicated boundary conditions between structural masonry components. Model calibration refers to correcting the inherent deficiencies within the FE model by matching outputs to measured data. Model calibration has the potential to produce more reliable computer models and thus aid in the economical management and maintenance of contemporary as well as heritage structures. Researchers involved in the assessment of historic masonry monuments have devoted decades of consistent attention to FE model calibration. This paper reviews the recent developments in this topic with a focus on complex vaulted masonry monuments. Studies on simpler forms of masonry structures, such as masonry arch bridges or masonry towers, are also discussed since they lay the groundwork for studies on more complex structures. This paper identifies several remaining technical challenges as model calibration approaches gain wider recognition and usage in historic monument structural assessment. Copyright © 2010 John Wiley & Sons, Ltd.     

A Resource Allocation Framework for Experiment-Based Validation of Numerical Models

In experiment-based validation, uncertainties and systematic biases in model predictions are reduced by either increasing the amount of experimental evidence available for model calibration—thereby mitigating prediction uncertainty—or increasing the rigor in the definition of physics and/or engineering principles—thereby mitigating prediction bias. Hence, decision makers must regularly choose between either allocating resources for experimentation or further code development. The authors propose a decision-making framework to assist in resource allocation strictly from the perspective of predictive maturity and demonstrate the application of this framework on a nontrivial problem of predicting the plastic deformation of polycrystals.     

Mitigating Error and Uncertainty in Partitioned Analysis: A Review of Verification,Calibration and Validation Methods for Coupled Simulations

Partitioned analysis involves coupling of constituent models that resolve different scales or physics by allowing them to exchange inputs and outputs in an iterative manner. Through partitioning, simulations of complex physical systems are becoming evermore present in the scientific modeling community, making the Verification and Validation (V&V) of partitioned models to quantifying the predictive capability of their simulations increasingly important. Partitioning presents unique challenges, as well as opportunities, for the V&V community. Verification gains a new level of complexity in partitioned models, as numerical errors can easily be introduced at the coupling interface where non-matching domains and models are integrated together. For validation, partitioned analysis allows the quantification of the uncertainties and errors in constituent models through comparison against separate-effect experiments conducted in independent constituent domains. Such experimental validation is important as uncertainties and errors in the predictions of constituents can be transferred across their interfaces, either compensating for each other or accumulating during iterative coupling operations. This paper reviews published literature on methods for assessing and improving the predictive capability of strongly coupled models of physical and engineering systems with an emphasis on advancements made in the last decade.