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Prediction of how cracks nucleate and develop is a major concern in fracture mechanics. The purpose of this study is to provide an overview of the state of the art on fracture mechanics with primary focus on different methodologies available for crack initiation and growth prediction in rubber-based materials under the static and fatigue loading conditions. The concept of fracture mechanics applied to rubber-based materials and concern of finite element analysis for J-integral estimation in elastomers are discussed in this paper. The strain energy release rate is commonly used to describe the energy dissipated during fracture per unit of fracture surface area and can be calculated by J-integral method, which represents a path-independent integral around the crack tip. As fatigue crack growth most commonly occurs in structures, the high-cycle fatigue life of components needs to be predicted by using extended finite element, strain energy density, finite fracture mechanics, and other techniques which will be covered in this review paper. In addition, some recent testing and numerical results reported in the literature and their applications will be discussed.

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Tire durability plays an important role in road transportation safety. Damaged tires can cause vehicle instability and create potential traffic accidents. To study the potential of using the intelligent tire concept for health monitoring of the tire, a computational method for defect detection in tire structure was developed. Comparing the trend of acceleration signals for the undamaged and damaged tires can reveal information about the crack length and location around the tire circumference. To accomplish this, a finite element model of the intelligent tire was developed using implicit dynamic analysis. In addition, using the data from the model, a health monitoring algorithm was developed for predicting the crack locations using the acceleration signals obtained from the tri-axial accelerometer attached to the tire inner-liner. It is observed that the radial component of the acceleration signal plays a key role in defect detection in intelligent tires.