Current and Recent Projects

Structural Damage Identification in Steel Buildings. My research goal is to identify the existence, location and severity of damage, after the occurrence of a potentially destructive earthquake event. This is achieved by combining the information provided from dense sensor array measurements, such as the Community Seismic Network (CSN), with the information built into high-fidelity finite element models. Various approaches are developed, implemented and tested, yielding promising results.

My most recent work is related to extending the sparse Bayesian learning methodology for using input-output measured acceleration time histories. Extension of the methodology for time histories allows the use of nonlinear finite element models. The method is found to be effective for identifying structural damage. Future goals include applications on full scale or shake-table structures and data.

The project addresses an urgent need of characterizing the state of structural health in buildings immediately after an earthquake event so that corrective retrofit and repair actions can be made to maintain desired levels of structural safety and reliability. Although this project concentrates on high-rise steel-frame buildings, the framework itself is applicable to other infrastructure systems such as bridges, offshore structures, wind turbines etc, monitored by similar sensor networks. The framework can also be used for damage identification in related engineering disciplines such as mechanical and aerospace engineering.

Site Response Study for urban LA using Recordings from the 2019 Ridgecrest Earthquakes. As a member of Caltech's Community Seismic Network team (CSN), part of my work is associated with data processing, analysis, and visualization following recent events recorded the network in the Los Angeles area. Our most recently published study is concerned with data recorded from the July 2019 Ridgecrest earthquake sequence. The collected data is used in order to study the ground-motion response in urban Los Angeles, as well as for evaluating the predictive capabilities of 3D finite difference simulations and ground motion prediction equations. The study further promotes the importance of dense accelerometer arrays in understanding local site behavior.

Leakage Detection in Water Distribution Networks. Part of my recent research is in the area of leakage detection in water distribution networks (WDN), in collaboration with the Department of Mechanical Engineering of the University of Thessaly. Sparse Bayesian learning techniques, integrated with steady-state flow models, are used to identify leakage location and amount using flowrate sensors while accounting for nodal demands. The leakage is simulated as demand at the midpoint of the leaked pipe. Detection capabilities are illustrated under various modelling error conditions and uncertainties, with preliminary results demonstrating the potential of the technique to identify leakage in simplified WDN.

Response Reconstruction and Fatigue Damage Accumulation in Railway Bridges Using On-Board Sensing. Another area of recent research concerns the response reconstruction of railway bridges using sensory systems installed on-board passing trains, in collaboration with the Department of Mechanical Engineering of the University of Thessaly and the Department of Civil and Environmental Engineering of the Hong Kong University of Science and Technology. The project applies augmented Kalman filtering to identify contact forces between bridge and train. The estimated forces are used together with a finite element model of the bridge to fully reconstruct bridge responses — accelerations, strains, and stresses — while accounting for vehicle and bridge dynamics as well as rail profile irregularities. Successful results are expected to advance on-board sensing for railway bridge applications, including fatigue damage accumulation estimation during train passages.

Aerosol Transfer and Deposition on the Respiratory System. My diploma thesis (University of Thessaly - UTH) was in the area of modelling aerosol transport and deposition in the respiratory system. A dynamic, single-path model was developed for dry powder transport in the lungs accounting for select particle and patient specific parameters. The assumption of perfect alveolar mixing was explored. Comparison with experimental data was satisfactory and indicative of a perfect mixing mechanism being indeed present in the alveoli. Model updating-parameter estimation and a sensitivity analysis was performed in order to calibrate the model.