Abstract:
The main purpose of this research is to study the feasibility of pavement monitoring using the dynamic response of conventional vehicle and smartphone sensors. The estimation of unknown vehicle dynamic parameters is also studied. In this study, the pavement condition has been monitored in terms of International Roughness Index (IRI) as it is used all over the world to measure road roughness as a part of pavement condition monitoring strategy.
The conventional vehicle is modeled using a Quarter-Car (QC) vehicle model. For estimation of unknown vehicle dynamic parameters, Grey-Box model algorithm is used to establish a relationship between the input (speed bump) and output (vertical accelerations) of the QC dynamic system. Numerical simulations as well as field tests are performed for unknown vehicle parameter estimations. In numerical simulations, two parameters are estimated within 12.1% of the actual values and another parameter within 22.5%. In field testing, the coefficient of variance for the estimated parameters are found to be within 15.2%.
In this study, Inverse State-Space Representation of QC dynamic system is developed for reconstruction of pavement profiles of the unknown pavements to be monitored using vertical acceleration of the vehicle as the input and the pavement profile is obtained as the output. From the reconstructed pavement profile, IRI of the pavement is estimated. The measurements of IRI of the pavements are done both numerically and practically. In numerical simulations, IRI of the reconstructed pavement profiles are found to be within 4.1% of the IRI of the actual profiles. In practical experiment, the proposed method is found to be repeatable and fairly accurate.
The results of the study indicate that it is possible to determine IRI of a pavement surface with reasonable accuracy using conventional vehicles and smartphone. The large-scale application of the proposed method will result in a cost effective, quick, flexible and convenient method for pavement monitoring with the potential of including the entire road network into the pavement monitoring system.
