Intelligent optimization and machine learning algorithms for structural anomaly detection using seismic signals

• Published in: Mechanical systems and signal processing Vol. 133; p. 106250
• Main Authors: Trapp, Maximilian, Bogoclu, Can, Nestorović, Tamara, Roos, Dirk
• Format: Journal Article
• Language: English
• Published: Berlin Elsevier Ltd 01.11.2019; Elsevier BV
• Subjects: Algorithms; Anomalies; Artificial intelligence; Boring machines; Computer simulation; Deep Gaussian covariance network; Drilling machines (tools); Earthquake damage; Gaussian processes; Inverse problem; Kalman filter; Machine learning; Optimization; Optimization techniques; Parameter estimation; Propagation velocity; Structural anomaly detection; Tunnel construction; Unscented hybrid simulated annealing; Structural anomaly detection; Deep Gaussian covariance network; Kalman filter; Unscented hybrid simulated annealing; Gaussian processes; Inverse problem
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2019.106250

•Full waveform inversion of seismic waves is used to locate a structural anomaly.•Seismic waves are acquired from ultrasonic experimental measurements.•Three global search methods are tested for the solution of the inverse problem.•The accuracy and the efficiency of these methods are compared. 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.