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• Developing an enhanced inverse beam element iEBT2 for deformation estimation. • iEBT2 allowing deformation reconstruction even with missing strain measures. • Using weighting constants to penalize contributions from "measure-less" stations. • All the results have demonstrated the effectiveness of iEBT2 element. The inverse finite element method (iFEM) is a mechanics-based algorithm for deformed-shape estimation of structures. In this paper, an enhanced inverse beam element named iEBT2 is developed based on classical beam theory. The element formulation is derived by minimizing a weighted-errors functional that consists of experimental and numerical section strains. The improved coefficient matrix K R is always non-singular, assuring that the solution of iFEM formulation exists. Location-independent feature of matrix K R simplifies the inverse finite element modeling, especially for complicated structures. Weighting constants are utilized to define error functional, aiming to penalize the contributions from "measure-less" stations. Numerical and experimental cases have been performed and demonstrated excellent predictive capability when iEBT2 model has complete strain measures. In the case of missing strain components, iEBT2 enables deformation estimation and its accuracy is acceptable in general. The enhanced inverse element extends the practical usefulness of iFEM in shape-sensing analysis of civil infrastructures. [ABSTRACT FROM AUTHOR]

• A DIC algorithm that can deal with discontinuous displacement is proposed. • The algorithm can automatically select the most suitable subset form. • The concept of pixel-level ZNCC distribution map is proposed. • The method has good applicability and scalability in engineering applications. Digital image correlation (DIC) is one of the most used techniques for measuring deformation. It is widely used in mechanical performance testing, structural health monitoring and many other fields. The standard subset based DIC technology assumes that the deformation is continuous. However, discontinuous deformation such as cracks and dislocations are quite common in practical applications. The measurement accuracy of DIC in these areas will be significantly reduced, or even measurement failure. This is a limitation for the application of DIC. To overcome this problem, this paper proposes a variable subset DIC algorithm. Taking the zero-mean normalized cross-correlation (ZNCC) function as the correlation criterion, the concept of pixel-level ZNCC value distribution map is proposed. The proposed algorithm can automatically select the most suitable subset for matching under the guidance of the pixel-level ZNCC value distribution map. This method has the advantages of good flexibility, strong applicability, and high precision. [ABSTRACT FROM AUTHOR]