Identification of Non Linear Parameters from Masonry Diagonal Compression Tests using Digital Image Correlation

During seismic events, masonry walls are subjected to in-plane loadings that primarily induce compression along their diagonals. The ASTM-standardized diagonal compression test replicates these conditions on square masonry panels, allowing for the analysis of failure mechanisms and the evaluation of diagonal compressive strength. This method has been widely applied to both unreinforced and reinforced masonry, facilitating direct comparisons under identical loading conditions. Traditionally, vertical and horizontal diagonal strains are measured using Linear Variable Differential Transducers (LVDTs); however, this contact-based approach may limit strain analysis in heterogeneous materials such as masonry. To overcome these limitations, Digital Image Correlation (DIC) has emerged as a robust, noncontacting technique for measuring full-field displacements. With an appropriate postprocessing strategy, DIC enables for the measurement of surface strains, observation of crack initiation and propagation, and the definition of damage indicators that differentiate mortar and block behavior. This approach is particularly advantageous for heterogeneous materials, where conventional instrumentation may not capture localized phenomena accurately. Although diagonal compression tests are well-established, few studies have combined full-field measurement techniques such as DIC with nonlinear parameter identification. To address this gap, numerical simulations are performed using a finite element code and employing an isotropic damage model that incorporates friction and unilateral effects, together with a detailed blockand-mortar representation based on the nonlinear model proposed by Vassaux et al. Initial results, obtained after applying the measured boundary conditions, show that the model successfully reproduces the expected behavior of masonry panels. The next step involves identifying model parameters using a Finite Element Model Updating (FEMU) algorithm, combined with sensitivity analysis and uncertainty quantification to ensure robust calibration.