Advanced Computational Approaches For Retrofitting Existing Structures

Abstract

The aging infrastructure in many parts of the world necessitates effective retrofitting strategies to ensure structural integrity and safety. This study empirically evaluates the use of advanced computational techniques—such as Finite Element Modeling (FEM), Artificial Intelligence (AI), and Building Information Modeling (BIM)—in retrofitting existing structures. The paper draws on quantitative data from a sample of 50 retrofitting projects across varied climatic and seismic zones to understand the influence of computational technologies on structural resilience, cost efficiency, and time optimization. Findings show that structures retrofitted with AI-enhanced FEM models demonstrated a 25–30% improvement in load-bearing capacity and a 20% reduction in material use, while BIM-integrated retrofits improved project delivery timelines by 18%. Survey responses from 120 engineers and architects indicated growing confidence in algorithm-aided retrofitting solutions. The study integrates performance metrics from pre- and post-retrofitting assessments and evaluates these results against traditional retrofitting benchmarks. The conclusion reveals a strong correlation between computational integration and retrofit success rates. This research contributes new empirical insights to the growing body of literature promoting digitized solutions for sustainable structural rehabilitation. Ultimately, it proposes a paradigm shift toward hybrid computational frameworks for improving retrofitting performance metrics and decision-making.