Analysis of Structural Integrity in High-Rise Buildings Under Dynamic Load Conditions Using AI: A Computational Perspective

Abstract

The structural integrity of high-rise buildings under dynamic load conditions, such as earthquakes, wind, and other environmental factors, is critical to ensuring safety and resilience. Traditional methods for assessing these conditions often rely on static or generalized models, lacking the precision needed for real-time and complex scenarios. To address these limitations, this study proposes a Dynamic Load Conditions using Artificial Intelligence (DLC-AI) framework, which integrates advanced AI algorithms to model, predict, and analyze dynamic stress and strain in high-rise structures. The framework leverages real-time data, machine learning models, and computational simulations to improve predictive accuracy and adaptive responses to varying load conditions. Application of DLC-AI demonstrates enhanced accuracy in predicting structural weaknesses and mitigating potential risks. Findings reveal that the proposed method significantly improves structural resilience, reduces response times during emergencies, and provides architects and engineers with actionable insights for sustainable urban development.