An Advanced Methodological Optimization for Structural Integrity Assessment in High-Rise Buildings Using Integrated Sensor Data and Machine Learning Algorithms

Abstract

The growing complexity of urban environments and the increasing demand for sustainable construction have amplified the necessity for advanced structural integrity assessments in high-rise buildings. This study introduces an innovative methodological optimization framework, integrating real-time sensor data with machine learning algorithms to enhance the predictive accuracy of structural assessments. Employing a case study of a prominent skyscraper, we demonstrate the effectiveness of our approach in identifying potential degradation points and optimizing maintenance schedules. The findings indicate significant improvements in assessment precision and resource allocation, paving the way for more resilient architectural practices and sustainable urban development. The proposed framework offers a transformative solution that bridges the gap between traditional assessment techniques and contemporary data analytics, ensuring the longevity and safety of high-rise structures.