An interval model updating strategy using interval response surface models

• Interval response surface models are developed for interval model updating.
• Parameter and response intervals can be easily estimated for an efficient updating.
• Interval overestimation is avoided providing precise interval estimation.
• Both eigenvalues and eigenvectors can be adopted as responses.
• The updating process is performed within a non-probabilistic framework.

Stochastic model updating provides an effective way of handling uncertainties existing in real-world structures. In general, probabilistic theories, fuzzy mathematics or interval analyses are involved in the solution of inverse problems. However in practice, probability distributions or membership functions of structural parameters are often unavailable due to insufficient information of a structure. At this moment an interval model updating procedure shows its superiority in the aspect of problem simplification since only the upper and lower bounds of parameters and responses are sought. To this end, this study develops a new concept of interval response surface models for the purpose of efficiently implementing the interval model updating procedure. The frequent interval overestimation due to the use of interval arithmetic can be maximally avoided leading to accurate estimation of parameter intervals. Meanwhile, the establishment of an interval inverse problem is highly simplified, accompanied by a saving of computational costs. By this means a relatively simple and cost-efficient interval updating process can be achieved. Lastly, the feasibility and reliability of the developed method have been verified against a numerical mass–spring system and also against a set of experimentally tested steel plates.