Integration of Vehicle-Based Sensing and Vehicle Dynamic Model for Evaluating Highway Infrastructure Resilience

Chun-Hsing Ho, Jimmie Devany, Manuel Lopez, Jr Mentors: Imad Al-Qadi, Xiuyu Liu Integration of Vehicle-Based Sensing and Vehicle Dynamic Model for Evaluating Highway Infrastructure Resilience Research Team Members § § § § § § Northern Arizona University: Chun-Hsing Ho (PI) Jimmie Devany, Manuel Lopez, Jr., (Undergraduate students) Illinois Center for Transportation Dr Imad L Al-Qadi (Mentor) Mr Xiuyu Liu (Doctoral student) Introduction and Challenge § Pavement International Roughness Index (IRI) is an important measure of pavement rideability § Current IRI has been introduced in 1980’s and its theoretical quarter car model has not been updated (Curtesy of Al-Qadi and Liu) Introduction: Vehicle-Mounted Sensors § Vehicle-mounted accelerometers were developed in the Northern Arizona University laboratory using a sensor logger consisting of triple-axis accelerometers, computer boards, GPS, and a battery Introduction: Full Car Model § A full-car model, comprises of two axles and a main vehicle body with seven DOF, has been developed by Al-Qadi and coworkers at the Illinois Center for Transportation of UIUC to estimate pavement roughness based on IRI values Objectives and Scope § Overcome quarter-car limitation § Propose an integrated system of Vehicle mounted sensors vehicle mounted accelerometers and a full-car model to predict smart phone embedded accelerometer IRI § Vehicle-mounted sensors and smart phone embedded accelerometers could be a cost effective method Data collection and analysis: First trial § Baseline Rd to Chandler Blvd Data collection and analysis: First trial § 27th Ave to 51st Ave East/West Bound IRI v Acceleration Data 0.4 y = 0.0023x - 0.0215 R² = 0.8033 0.35 Zg Data 0.3 0.25 0.2 0.15 0.1 0.05 0 20 40 60 80 100 IRI Data 120 140 160 180 200 Data Collection on Two I-10 Corridors in Phoenix Window Interpolation Method: Data Matching and Selection § All selected acceleration points within a “window of IRI” are exported, averaged and recorded, and a table is generated in ArcGIS Linear Regression Results IRI-Acceleration Correlations Simulated Vehicle Responses § Full-car model predicts vehicle responses based on road- Vertical Velocity (in/s) 3000 2000 1000 -1000 -2000 -3000 10 Time (s) 15 Vertical Displacement (in) Vertical Acceleration (in/s2) roughness level, driving speed, and vehicle’s dynamic properties 20 30 20 10 -10 -20 -30 15 20 -1 -2 -3 10 Time (s) 10 Time (s) 15 20 Correlation of Full-Car Model and Field Data between simulation and field measured data is 0.922 § Good agreement between field measurements and vehicle dynamic simulations 0.40 Mean Absolute Acceleration (g) § The correlation coefficient Simulation Measurement 0.35 0.30 0.25 0.20 0.15 0.10 60 80 100 120 140 Road Roughness IRI (in/mi) 160 180 Conclusions § Vibration data collected from vehicle-mounted sensors could be a proper representation of actual pavement responses § The recently developed full-car model by Al-Qadi and coworkers has been successfully used to validate the field data § Results show that the integration, of vehicle-mounted sensor measurements and the newly developed full-car model, could successfully predict pavement roughness Acknowledgement § This research was performed under an appointment to the U.S Department of Homeland Security (DHS) Science & Technology (S&T) Directorate Office of University Programs Summer Research Team Program for Minority Serving Institutions, administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S Department of Energy (DOE) and DHS ORISE is managed by ORAU under DOE contract number DE-SC0014664 All opinions expressed in this paper are the author’s and not necessarily reflect the policies and views of DHS, DOE or ORAU/ORISE ... consisting of triple-axis accelerometers, computer boards, GPS, and a battery Introduction: Full Car Model § A full-car model, comprises of two axles and a main vehicle body with seven DOF, has... integrated system of Vehicle mounted sensors vehicle mounted accelerometers and a full-car model to predict smart phone embedded accelerometer IRI § Vehicle- mounted sensors and smart phone embedded... driving speed, and vehicle? ??s dynamic properties 20 30 20 10 -10 -20 -30 15 20 -1 -2 -3 10 Time (s) 10 Time (s) 15 20 Correlation of Full-Car Model and Field Data between simulation and field measured