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Studies on the work integration of persons with disabilities (PwD) and the role of social dialogue therein are scarce. The study examines how the different systems of workers’ representation and industrial relations in Slovakia and Norway facilitate PwD work integration. Taking a social ecosystem perspective, we acknowledge the role of various stakeholders and their interactions in supporting PwD work integration. The paper’s conceptual contribution lies in including social dialogue actors in this ecosystem.

Evidence was collected via desk research, 35 semi-structured in-depth interviews with 51 respondents and stakeholder workshops in 2019–2020.

The findings from Norway confirm the expected coordination of unions and employers in PwD work integration. Evidence from Slovakia shows that in decentralised industrial relations systems, institutional constraints beyond the workplace determine employers’ and worker representatives’ approaches in PwD integration. Most policy-level outcomes are contested, as integration occurs predominantly via sheltered workplaces without interest representation.

This paper identifies the primary sources of variation in the work integration of PwD. It also highlights opportunities for social partners across both situations to exercise agency and engagement to improve PwD work integration.

By integrating two streams of literature – social policy and welfare state and industrial relations – this paper examines PwD work integration from a social ecosystem perspective. Empirically, it offers novel qualitative comparative evidence on trade unions’ and employers’ roles in Slovakia and Norway.

Ensuring the safety of structures is important. However, when a structure possesses both an implicit performance function and an extremely small failure probability, traditional methods struggle to conduct a reliability analysis. Therefore, this paper proposes a reliability analysis method aimed at enhancing the efficiency of rare event analysis, using the widely recognized Relevant Vector Machine (RVM).

Drawing from the principles of importance sampling (IS), this paper employs Harris Hawks Optimization (HHO) to ascertain the optimal design point. This approach not only guarantees precision but also facilitates the RVM in approximating the limit state surface. When the U learning function, designed for Kriging, is applied to RVM, it results in sample clustering in the design of experiment (DoE). Therefore, this paper proposes a FU learning function, which is more suitable for RVM.

Three numerical examples and two engineering problem demonstrate the effectiveness of the proposed method.

By employing the HHO algorithm, this paper innovatively applies RVM in IS reliability analysis, proposing a novel method termed RVM-HIS. The RVM-HIS demonstrates exceptional computational efficiency, making it eminently suitable for rare events reliability analysis with implicit performance function. Moreover, the computational efficiency of RVM-HIS has been significantly enhanced through the improvement of the U learning function.