Revolutionizing Resilience: A Comprehensive Review of Technological, AI-Driven Innovations in Anchorage Zone Design and Maintenance

Mercy Muroyiwa

doi:10.54097/b3g5x678

Authors

Mercy Muroyiwa

DOI:

https://doi.org/10.54097/b3g5x678

Keywords:

Anchorage zones, Artificial Intelligence, Structural Health Monitoring, Predictive Maintenance, Digital Twins, Reinforcement Learning, Physics-Informed Machine Learning, Sensor Networks, Structural Optimization, Infrastructure Resilience.

Abstract

Anchorage zones, the critical junctures transferring immense prestressing forces in concrete and steel structures, remain persistent vulnerability hot spots. Susceptible to stress concentrations, corrosion propagation, and fatigue-induced degradation, their premature failure jeopardizes structural integrity despite conservative design codes and labor-intensive inspections. Traditional approaches often fail to capture the dynamic interplay of environmental stressors (chloride ingress, humidity fluctuations, thermal cycling) and evolving operational loads (increasing traffic volumes, extreme weather events). This review synthesizes groundbreaking advancements in artificial intelligence (AI) that are fundamentally transforming anchorage zone engineering, moving beyond static safety factors towards dynamic, predictive, and optimized management. We detail innovations including: high-accuracy convolutional neural networks (CNNs) achieving 92% crack detection in real-time; hybrid physics-informed neural networks (PINNs) slashing finite element analysis (FEA) computational overhead by 60%; integrated digital twin frameworks fusing LiDAR and fiber optic sensing for millimeter-level displacement tracking; and reinforcement learning (RL) algorithms dynamically modulating prestress levels in response to forecasted loads, demonstrably reducing critical stress peaks by 25%. Robust case studies, notably on the Hong Kong-Zhuhai-Macao Bridge and Japan’s Akashi Kaikyō Bridge, evidence up to 40% life cycle cost savings through AI-prioritized interventions. While challenges persist notably data scarcity for rare failure modes and computational demands of real-time digital twins emerging solutions like generative AI for synthetic data augmentation, edge computing deployments, probabilistic Bayesian updating, and multi-agent RL coordination chart a clear road map. This review establishes AI not merely as a tool, but as an indispensable paradigm for realizing resilient, adaptive, and economically sustainable anchorage systems in next-generation infrastructure.

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