Dr Rolands Kromanis joined NTU, Civil Engineering Section in September, 2015. He is the module leader of Year 1, BSc Civil Engineering, Mathematics module (DESN10045) and MSc Structural Engineering with Materials, Condition Assessment and Health Monitoring of Civil Infrastructure module (DESN40149). Roland delivers (i) lectures to Year 1 BEng and BSc students in Civil Engineering Design Project module (DESN10037) and AutoCAD and Excel seminar sessions for Year 1 and 2 students.
Roland also supervises final year and MSc projects, is a director of studies for PhD students and is a research active lecturer.
Roland has received BEng (hons) in Civil and Structural Engineering (University of Bradford), MSc in Structural Engineering (University of Dundee) and PhD in Civil Engineering with Computer Sciences (University of Exeter). His research focuses on the structural performance evaluation of bridges using measurements from continuous monitoring. He was also a practising structural engineer involved in engineering consultancy, signal processing and energy-efficient housing projects in Europe.
Roland is interested in applications of technologies to understand behaviour of structures. For example, complimenting a structure with a sensory network, which mimics a ‘nervous system’ in human bodies, enables collections of information related to inputs to and outputs from the system. Then the understanding of interactions between physical and digital worlds using appropriate interpretation tools can be made possible, hence broadening our knowledge of ways things and we work. The main challenge in all this is to make sense of collected measurements.
Roland's current research focuses on:
- development of "DeforMonit" - a low cost vision-based deformation monitoring system using phone cameras (see more on YouTube, Research Gate).
- analysis of pedestrian (crowed) induced excitations on footbridges - capture human induced forces and motion.
- introduction of vision-based approaches for deformation monitoring of steel structures.
- introduction of an image processing methodology, which will be used to detect specific features in an image, derive correlations between features, recognize these features in other images and analysis derived correlations for changes/anomalies.
- condition assessment of heritage structures using low-cost vision-based systems
- Stevens P (PI), Forsythe S, Liang H, Sale C, Breedon Ph, Kromanis R (2017) 3D Printed Antimicrobial Silicone Composites for Medical Devices, NTU Health and Wellbeing Proof of Concept Fund, £19,975
- Kromanis R (PI). (2016) Structural health monitoring of wind turbines: Characterizing response using a regression-based approach, British Council and Newton Fund to attend a workshop, £1,100,-
- Liang H (PI), Kromanis R (Co-I) and Sargent Ph. (Co-I) (2016) Detection and Classification of Corrosion on Steel Structures Using Scanning Technologies, Innovate UK (KTP project), £414,163,-
- Kromanis R (PI). (2013) Evaluation of Quasi-static Thermal Response on Bridges, Pengnian Scholarship, £1,000
Roland is a:
- member of the Sensing and Computing for Intelligent Infrastructure research group, University of Exeter,
- review editor for frontiers in Built Environment, Structural Sensing Section,
- reviewer for Elsevier and ASCE journals.
- works on collaborative projects with University of Nottingham, Nottingham Geospatial Institute,
- delivers short courses in AutoCAD, Revit and MatLAB.
- Kromanis R, Kripakaran P and B Harvey. (2015) Long-term SHM of the Cleddau Bridge: Evaluation of quasi-static temperature effects on bearing movements. Structure and Infrastructure Engineering: Maintenance, Management, Life-Cycle Design and Performance, Accepted.
- Kromanis R and Kripakaran P. (2015) SHM of Bridges: Characterising Thermal Response and Detecting Anomaly Events Using a Temperature-Based Measurement Interpretation Approach. Journal of Civil Structural Health Monitoring, Submitted
- Kromanis R and Kripakaran P. (2014) Predicting thermal response of bridges using regression models derived from measurement histories. Computers & Structures, 136, 64-77.
- Kromanis R and Kripakaran P. (2013) Support vector regression for anomaly detection from measurement histories. Advanced Engineering Informatics, 27(4), 486–495.