Multi-index evaluation on reinforcement programs for frame structures based on life cycle theory and time-varying reliability

Under the mandates of economic and environmental sustainability, the consideration of reinforcement engineering must extend beyond mere enhancement effects and short-term project costs. As a long-term engineering activity, reinforcement significantly impacts urban economies and environments. Although extensive research has evaluated the costs or environmental impacts of reinforcement engineering based on the whole life cycle theory, existing studies often focus on single indicators without adequately integrating them with other domains. Using a reinforced concrete frame structure as a case study, this paper develops a combined evaluation decision-making model integrating the Analytic Hierarchy Process for subjective assessment and an enhanced Rough Set method for objective evaluation. This model comprehensively assesses the seismic performance, economic costs, and environmental impacts of various reinforcement strategies. The results indicate that the combined strategy of enlarging column sections and carbon fiber reinforced polymer strengthening beams performs best in seismic performance, long-term economic benefits, and environmental impacts. Grounded in practical projects, this study adjusts the whole life cycle cost model to better align with engineering maintenance realities and introduces Rough Set theory to address deficiencies in objective indicator weighting. This research offers insights and guidance for leveraging information technology in resolving multi-criteria evaluation decision-making challenges in reinforcement engineering solutions. [Display omitted] •A combined model based on AHP and an improved neighborhood rough set was built.•Seismic performance, economic cost, and environmental impact were all considered.•Multi-index evaluation model is applied to define the optimal reinforcement scheme.•An improved neighborhood rough set method is used to count the objective weights.