Open Access
ARTICLE
Neural Network-Based Structural Health Monitoring: Predicting Material Degradation in Civil Engineering Infrastructure
Issue Vol. 1 No. 01 (2025): Volume 01 , Issue 01 Oct 2025 --- Section Articles --- Published Date: 2025-10-01
Abstract
Structural Health Monitoring (SHM) has emerged as a high-priority research domain for ensuring the safety, reliability, and long-term durability of civil engineering infrastructure, including bridges, pavements, tunnels, dams, and high-rise buildings. Traditional inspection methods—manual visual surveys, destructive testing, and periodic evaluations—are increasingly insufficient in detecting micro-level degradation or predicting catastrophic failures in advance. This research explores the integration of Artificial Neural Networks (ANNs) into SHM systems to provide continuous, data-driven prediction of material deterioration such as corrosion, fatigue, crack propagation, concrete spalling, and stress accumulation. By analyzing high-frequency sensor data (strain, vibration, acoustic emission, temperature, humidity, and displacement), neural networks can automatically learn deformation patterns and predict failure risks with high accuracy. Experimental results demonstrate that ANN-enabled SHM significantly enhances early-warning capabilities, reduces maintenance costs, and supports life-cycle asset management. The study positions neural-network-driven SHM as a next-generation solution for smart infrastructure monitoring.
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