Integration of Artificial Neural Networks and Machine Learning for Predictive Modelling of Structural Health in Civil Engineering Concrete Bridges

Abstract

Structural health monitoring (SHM) has become a crucial aspect of maintaining the safety and durability of civil engineering structures, particularly concrete bridges. Traditional methods often rely on periodic inspections and manual sensor data analysis, which can be both time-intensive and susceptible to human error. With advancements in technology, Artificial Intelligence (AI) techniques such as Artificial Neural Networks (ANNs) and Machine Learning (ML) algorithms have emerged as effective tools for predictive modeling in SHM. These AI models can process large amounts of real-time sensor data to predict key indicators like crack propagation, load-bearing capacity, and overall structural health. By providing accurate predictions, AI models enable more efficient maintenance scheduling, reducing both the risk of structural failure and unnecessary repair costs. This research explores various AI-driven models, comparing the performance of ANNs with other machine learning techniques such as Random Forest and Gradient Boosting. The findings demonstrate that AI can significantly improve the precision of SHM, offering a more scalable and cost-effective solution for infrastructure management, ultimately extending the service life of bridges while ensuring public.