Integrating Smart Polymers, Digital Twins, and AI for Corrosion Mitigation and Structural Health Monitoring in Large-Scale Infrastructure: A Case Study on the Golden Gate Bridge

Corrosion poses a significant threat to the longevity and safety of large-scale infrastructures, particularly in harsh marine environments. This study presents an innovative framework integrating smart polymers, digital twins, and AI-driven technologies to mitigate corrosion and enhance structural health monitoring (SHM). The Golden Gate Bridge served as a case study for implementing and validating this system. The application of smart polymers reduced chloride ion penetration by ۴۰% and delayed crack propagation by ۲۵%, as confirmed by material testing and digital twin simulations. These polymers demonstrated exceptional performance, retaining ۹۵% of tensile strength under marine exposure compared to ۷۰% for untreated sections. A high-fidelity digital twin enabled real-time monitoring and predictive analysis, identifying high-risk zones with an error margin of less than ۵%. AI models, including convolutional neural networks (CNNs) and hybrid physics-AI approaches, achieved a ۹۸% accuracy in crack detection and a ۹۲% precision in forecasting corrosion progression. Over a ۱۰-year projection, the proposed system reduced maintenance costs by ۲۲%, with inspection efforts decreasing by ۳۰% due to proactive maintenance strategies informed by real-time data. This study demonstrates the transformative potential of combining advanced materials and digital technologies to address corrosion challenges, improve structural resilience, and reduce lifecycle costs in critical infrastructures. The framework offers a scalable and sustainable solution for global infrastructure management.

کلیدواژه ها:

Smart Polymers ، Digital Twins ، Artificial Intelligence ، Corrosion Mitigation ، Structural Health Monitoring (SHM) ، Predictive Analytics ، Infrastructure Sustainability