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Methodologies for Data Interpretation during Continuous Monitoring of Structures

Speaker: Irwanda Laory, University of Warwick, UK
Series: CEE Departmental Seminars
Location: Bowen Hall Auditorium
Date/Time: Friday, April 19, 2013, 4:30 p.m. - 6:00 p.m.


Irwanda Laory
Most civil engineering infrastructures, especially bridges worldwide, are approaching the end of their designed lifespan. Structural Health Monitoring (SHM) has the potential to provide a proper assessment of structural performance and further reducing cost through early damage detection and thus replacement avoidance. The bottleneck in SHM is data interpretation and this task is even more challenging in the presence of environmental variations. This talk will explore data interpretation for continuous monitoring of structures which includes 1) damage detection, 2) measurement-system configuration and 3) prediction of structural responses. For damage detection, a novel model-free method that combines Moving Principal Component Analysis (MPCA) and regression analysis will be described. The talk will then present a systematic approach for measurement-system configuration that involves multi-objective optimization and Multi-Criteria Decision-Making (MCDM) methods. Prediction of structural responses using regression analysis (multiple linear regression, artificial neural network, support vector regression and random forest) will also be depicted. These methods will be demonstrated in case studies that involve Ricciolo Bridge (Switzerland) and Tamar Bridge (UK). The talk will conclude with a brief discussion on the plan for future research.


Irwanda Laory is an assistant professor at the School of Engineering, University of Warwick. He graduated from Bandung Institute of Technology, Indonesia, with a BSc in Civil Engineering and then went on to Bauhaus Universität Weimar, Germany, where he obtained his MSc in civil engineering. This was followed by his PhD at Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland. His research interests lie in structural health monitoring, anomaly detection, measurement system design and computer-aided engineering.