Existing RC structures show accidental loadings vulnerabilities because of inadequate design detailing causing in premature failure. The installation of a certain reinforcement measure on the susceptible structure is a common option to mitigate these fragility. At this time, reinforcement measures aided by the participation of stakeholders are crucial to combat multiple hazards. However, previous studies did not necessarily dealt with the integration multi-hazard loss, multi-uncertainty, and dependence configuration in a performance-based engineering decision-making framework, to retrofit selection from the robustness of structures perspective. In this paper, an efficient fuzzy full robustness-based decision-making framework is proposed to tackle the challenge of integration of multi-uncertainty and dependence, and further to avoid biased strategies. This framework contained Probabilistic Multi-hazards Fuzzy Global Vulnerability (PMFGV), Loss (PMFGL), and Robustness Assessment (PMFRA) for structures, by incorporating the fuzzy theory and Vine-Copula tree structure. The mentioned robustness index can be used as a reference indicator in the rehabilitation and retrofitting planning. The PMFRA-based making-decision methodology is applied to RC structures with three design schemes, which were established the platform OpenSEEsPy. Results confirm that the established assessment framework is effective in evaluating actual RC structures’ robustness. The fuzzy full robustness index for the frame S-RC, SS-RC, and RS-RC, was < 0.327, 0.421, 0.470 > , < 0.400, 0.503, 0.523 > , < 0.356, 0.453, 0.507 > , respectively. Whether the robustness-cost-based or the Cumulative Prospect Theory-based, the robustness of the reinforcement strategy with the installation of secondary beam (SS-RC) outdistance that of the additional reinforcement ratio (RS-RC). Such parameters are suggested as reasonable and meaningful making-decision index for selecting the optimal retrofitting schemes for structures subjected to multiple hazards, which leads to a balance between the enhancement of full robustness and reduction of life cost.
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