Advanced weather monitoring for a Cable stayed bridge Chandrasekar Venkatesh June 11, 2018 Bachelors in Electrical and Electronics Engineering Ph.D. in Electrical Engineering Department of Electrical Engineering and Computer Science, College of Engineering and Applied Science Committee Chair: Dr. Arthur Helmicki Committee Members: Dr.Victor Hunt Dr. Douglas Nims Dr. Ali Minai Dr. William Wee Abstract In the northern United States, Canada, and many northern European countries, snow and ice pose serious hazards to motorists. Potential traffic disruptions caused by ice and snow are challenges faced by transportation agencies. Successful winter maintenance involves the selection and application of the most optimum strategy, over optimum time intervals. The risk associated with operating the bridges during winter emergencies varies depending on the size of the structure, the material of the stays, volume of average daily traffic, geographical location, nature of terrain and surroundings etc. The ‘Dashboard’, a monitoring system designed to help the bridge maintenance and operation personnel was developed at University of Cincinnati Infrastructure Institute. This was implemented at the Veterans Glass City Skyway Bridge in Toledo, Ohio. This system was also extended to the Port Mann Bridge in Vancouver, Canada. The aim of this research is to come up with an advanced monitoring system which will help the bridge management team make control actions during winter emergencies on the VGCS and Port Mann bridges. The current monitoring system gives information on the status of ice accumulation/ snow accretion or shedding based on last one hour’s weather data. This dissertation focuses on adding intelligence to the existing system through addition of sensors, identifying patterns in events, adding cost-benefit analysis and incorporating forecast parameters, while also extending the system to other bridges and structures. In essence a new, more intelligent monitor designed to make the control decisions easier and have all necessary information to make such decisions in one place will be invaluable to the officials in the transportation departments. ii iii Acknowledgements I am grateful for my parents and my sister for their patience, encouragement and infinite support all my life. I would like to express my sincere gratitude to Dr. Arthur Helmicki and Dr. Victor Hunt at the University of Cincinnati Infrastructure Institute, who have been great mentors during this rewarding journey. Their continuous feedback, enthusiasm, and encouragement were very significant for the completion of this project. I would also like to thank Dr. Douglas Nims, Dr.Ali Minai and Dr. William Wee for their thoughtful comments and discussions to improve the quality of my dissertation. I am also thankful for Dr. Mahdi Norouzi, Mr. Biswarup Deb, Mr. Nithyakumaran Gnanasekaran and Ms. Monisha Baskaran at University of Cincinnati and Dr. Ahmed Abdelaal of the University of Toledo for their collaboration and assistance throughout the project. This project would have never been possible without the funding and support from the Ohio Department of Transportation (ODOT) and the British Columbia Ministry of Transportation(BCMOT). I am also grateful to all my roommates, lab mates and friends who supported me during my time at Cincinnati. Lastly, I thank the College of Engineering and Applied Science at the University of Cincinnati, my Graduate Program Coordinator Ms. Julie Muenchen and the Department of Electrical Engineering and Computer Science for all the facilities and scholarships. iv Table of Contents Abstract ..................................................................................................................................... ii Acknowledgements ................................................................................................................... iv 1. Introduction........................................................................................................................1 2. Literature review and Weather monitoring system ..........................................................5 2.1. Data sources ....................................................................................................................5 2.2. Ice accumulation/Snow accretion and shedding conditions .........................................7 2.2.1. Ice accumulation and shedding .......................................................................................7 2.2.2. Snow accretion and shedding ..........................................................................................8 2.3. Dashboard algorithm ........................................................................................................9 2.4. Dashboard performance .................................................................................................. 12 2.5. Weather forecast data ..................................................................................................... 13 3. Case study 1: VGCS bridge ................................................................................................ 15 3.1. Bridge introduction............................................................................................................. 15 3.2. Dashboard details, Past events analysis and performance ................................................ 17 3.2.1. Introduction ................................................................................................................ 18 3.2.2. Sensor suite installation ............................................................................................... 20 3.2.3. State transitions ........................................................................................................... 21 3.2.4. Dashboard performance .............................................................................................. 23 v 3.3. Improvements to the algorithm ....................................................................................... 31 3.4. Accumulation and shedding pattern in significant events form the past ........................... 36 3.4.1. Revisiting past evets ........................................................................................................ 36 3.4.2. Assumptions and limitations ....................................................................................... 45 3.4.3. Types of ice accumulation ........................................................................................... 46 3.4.4. Types of ice shedding.................................................................................................. 48 3.4.5. Relationship with lane closure ..................................................................................... 50 3.5. Lane closure and associated costs ................................................................................... 54 3.6. Probability of accidents and associated costs .................................................................. 56 3.7. Conclusion ..................................................................................................................... 58 4. Case Study 2: Port Mann bridge ......................................................................................... 60 4.1. Bridge Introduction ........................................................................................................ 60 4.2. Dashboard details, past events analysis and performance ................................................ 64 4.2.1. Introduction ................................................................................................................ 64 4.2.2. Source stations and weights......................................................................................... 65 4.2.3. State transitions ........................................................................................................... 71 4.2.4. Past events and Dashboard performance ...................................................................... 73 4.3. Snowpack processing ..................................................................................................... 76 4.4. Snow thickness on stays - Ts calculations ....................................................................... 86 4.5. Using forecast data sources in dashboard system ............................................................ 91 vi 4.5.1. Data sources and models ............................................................................................. 91 4.5.2. Dashboard rules and thresholds ................................................................................. 103 4.5.3. Analysis and results .................................................................................................. 105 4.6. Forecast dashboard system ........................................................................................... 117 4.6.1. Forecast bar charts .................................................................................................... 117 4.6.2. Event summary charts ............................................................................................... 123 4.6.3. New dashboard components ...................................................................................... 126 4.7. Conclusion ................................................................................................................... 128 5. Conclusion and Future Work ...........................................................................................