Model tuning in engineering: Uncovering the logic. (January 2016)

• Record Type: Journal Article
• Authors: Steele, Katie; Werndl, Charlotte
• Other Names: Hack Erwin guest-editor.; Lampeas George guest-editor.
• Is Part Of: Journal of strain analysis for engineering design. Volume 51:Number 1(2016)
• Page Start: 63
• Page End: 71
• Publication Date: 2016-01
• Subjects: Tuning -- updating -- confirmation -- evidence -- Bayesianism; Strains and stresses -- Periodicals; Strain gages -- Periodicals; 624.176
• DOI: 10.1177/0309324715575445
• Languages: English
• ISSNs: 0309-3247

Abstract

In engineering, as in other scientific fields, researchers seek to confirm their models with real-world data. It is common practice to assess models in terms of the distance between the model outputs and the corresponding experimental observations. An important question that arises is whether the model should then be 'tuned', in the sense of estimating the values of free parameters to get a better fit with the data, and furthermore whether the tuned model can be confirmed with the same data used to tune it. This dual use of data is often disparagingly referred to as 'double-counting'. Here, we analyse these issues, with reference to selected research articles in engineering (one mechanical and the other civil). Our example studies illustrate more and less controversial practices of model tuning and double-counting, both of which, we argue, can be shown to be legitimate within a Bayesian framework. The question nonetheless remains as to whether the implied scientific assumptions in each case are apt from the engineering point of view.