Intelligent Optimization and Machine Learning Algorithms for Structural Anomaly Detection using Seismic Signals
This work addresses a domain-specific problem for tunnelling operations, offering an incremental improvement in anomaly detection methods.
The paper tackled the problem of detecting structural anomalies during mechanized tunnelling to prevent financial loss and drilling delays by using intelligent optimization and machine learning algorithms, resulting in increased efficiency through comparison of experimental vibration measurements with numerical simulations.
The lack of anomaly detection methods during mechanized tunnelling can cause financial loss and deficits in drilling time. On-site excavation requires hard obstacles to be recognized prior to drilling in order to avoid damaging the tunnel boring machine and to adjust the propagation velocity. The efficiency of the structural anomaly detection can be increased with intelligent optimization techniques and machine learning. In this research, the anomaly in a simple structure is detected by comparing the experimental measurements of the structural vibrations with numerical simulations using parameter estimation methods.