Seyedmeysam Khaleghian

An investigation towards intelligent tyres using finite element analysis

ABSTRACT Intelligent tyres have the potential to be widely used to enhance the safety of road transportation systems by providing an estimation of the road surface friction, tyre load and several other important characteristics. Since the tyre–road contact characteristics play an important role in stability and control of vehicles under severe manoeuvers, tyre interaction with the road surface needs to be evaluated in the contact patch region. In this research, a finite element model is implemented to investigate the effects of different parameters, including vehicle velocity and normal load, on the projected contact patch area. Furthermore, a tyre with a tri-axial accelerometer attached to its inner-liner is tested on different road surfaces with different contact frictions and at different loading conditions. To validate the model, the radial and circumferential accelerations obtained from the simulation are compared with the experimental results. The effects of velocity, normal load and coefficient of friction on the contact patch area are investigated and it is concluded that the circumferential component of acceleration is the key factor for estimating the tyre contact patch length.

Fuzzy logic inference-based Pavement Friction Management and real-time slippery warning systems: a proof of concept study

Minimizing roadway crashes and fatalities is one of the primary objectives of highway engineers, and can be achieved in part through appropriate maintenance practices. Maintaining an appropriate level of friction is a crucial maintenance practice, due to the effect it has on roadway safety. This paper presents a fuzzy logic inference system that predicts the rate of vehicle crashes based on traffic level, speed limit, and surface friction. Mamdani and Sugeno fuzzy controllers were used to develop the model. The application of the proposed fuzzy control system in a real-time slippery road warning system is demonstrated as a proof of concept. The results of this study provide a decision support model for highway agencies to monitor their networks friction and make appropriate judgments to correct deficiencies based on crash risk. Furthermore, this model can be implemented in the connected vehicle environment to warn drivers of potentially slippery locations.

Damage diagnosis in intelligent tires using time-domain and frequency-domain analysis

Abstract Tire durability plays an important role in road transportation safety and is taken very seriously by all tire manufacturers. Defects in tires can cause vehicle instability and create catastrophic accidents. In this article, a finite element model of the intelligent tire is developed using implicit dynamic analysis and is used for defect detection. Processing and analyzing the acceleration signals, measured at the center of the tire inner-liner, for the undamaged and damaged tires, can result in detecting the crack locations around the tire circumference. Additionally, prediction models used for damage diagnosis based on optimized number and location of sensors, was developed. Several sensors located at different locations around the circumference of the damaged tire and away from the crack surface, are used in order to assess sensor location sensitivity from the crack surface. It is observed that the radial component of the acceleration signal has the highest potential to be used as the signal of choice in defect detection as compared to circumferential acceleration signals.

Pavement friction management – artificial neural network approach

ABSTRACT The Highway Safety Improvement Program (HSIP) requires state highway agencies to improve their roadway network safety through a ‘strategic’ and ‘data-driven’ approach. As part of HSIP, the Federal Highway Administration mandates that states develop a Pavement Friction Management System to reduce the rate of fatal and injury-causing crashes and prioritise their safety improvement projects based on the crash risk. This paper aims to predict the rate of wet and dry vehicle crashes based on surface friction, traffic level, and speed limit using an artificial neural network (ANN). Three learning algorithms, Levenberg–Marquardt, conjugate gradient, and resilient back-propagation, were examined to train the network. Levenberg–Marquardt produced the best precision and was used to develop the model. The results of the study suggest that the ANN model can reliably predict the rate of crashes. The prediction model can be used as a scale to prioritise safety improvement projects based on the rate of fatal and injury-causing crashes.