Multicriteria Sensitivity Analysis for Numerical Model Validation of Experimental Data
Bobby Rio Indriyantho (Universitas Diponegoro)
Joko Purnomo (Petra Christian University)
Aries Purwanto (Universitas Diponegoro)
M Ottele (TU Delft - Materials and Environment)
Ay Lie Han (Universitas Diponegoro)
Buntara Sthenly Gan (Nihon University)
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Abstract
Sensitivity analysis is a decisive step in experimental and numerical structural mechanics. The analysis of structural model quantifies the importance of each input parameter, potential interaction and effects on structural response. Therefore, this study aimed to help reduce the uncertainty surrounding major variables, providing valuable guidance for conducting future experiments. During the investigation, numerically deterministic sensitivity analysis based on multicriteria model evaluations of load-displacement curves representing actual behavior of the member correctly, were reviewed. Multicriteria model combined the evaluation of peak load, energy dissipation before ultimate loading, and toughness of load-displacement response. The methodology led to a strong sensitivity analysis method, generating an agreement between numerical and experimental responses. Moreover, an investigation of the method was presented for a geopolymer haunch, the numerical model was based on rigid body spring model (RBSM), which enabled precise behavior simulation of reinforced concrete structures. RBSM was refined, enabling in-depth evaluation of stress-strain contours, plasticity index, initial crack formation and crack propagation, as well as RBSM-spring failure modes. The proposed multicriteria sensitivity analysis can be implemented with other simulation methods, such as finite element analysis (FEA) and structural simulation software. The recommended method is applicable to any structural member, where laboratory-tested full-scale specimens were functioning as validation tools. Following the proposed multicriteria sensitivity analysis, experimental load-displacement curves of this study supported the results of numerical RBSM in an acceptable range of error predictions.