Role of AI in Enhancing Safety and Reliability of Large-Scale Engineering Infrastructure
Keywords:
Artificial Intelligence, Machine Learning, Deep Learning, Infrastructure Safety,, Structural Reliability,, Predictive Maintenance, XGBoost, LSTM, Explainable AI, Smart Infrastructure.Abstract
Large-scale engineering infrastructures are essential components of contemporary
civilization; yet, the growing complexity of these infrastructures, the fact that they are aging,
the environmental exposure they are subjected to, and the operating demands they place on
them offer considerable issues in terms of safety, dependability, and maintenance
management. Advances in AI, machine learning, and deep learning have enabled real-time
monitoring, predictive analysis, anomaly detection, and infrastructure reliability assessment.
The study developed a Python-based predictive framework using structural, environmental,
operational, and historical factors to evaluate infrastructure risks. Models were assessed
using F1-score, MAE, RMSE, accuracy, precision, and recall. Results showed improved
reliability prediction, with LSTM performing strongly in time-dependent failure prediction
and XGBoost achieving 97% classification accuracy. Results from this study suggest that
AI-based predictive frameworks can revolutionize infrastructure management by offering
foresight into risk, enabling preventative maintenance, and improving reliability.
