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Peridynamics theory is a nonlocal meshless method that replaces differential equations with spatial integral equations, and has shown good applicability and reliability in the analysis of discontinuities. Further, with characteristics of clear physical meaning and simple and reliable numerical calculation, the bond-based peridynamics method has been widely applied in the field. However, this method describes the interaction between material points simply using a single elastic “spring”, and thus leads to a fixed Poisson’s ratio, relatively low computational efficiency and other inherent problems. As such, the goal of this review paper is to provide a summary of the various methods of bond-based peridynamics modeling, particularly those that have overcome the limitations of the Poisson’s ratio, considered the shear deformation and modeling of two-dimensional thin plates for bending and three-dimensional anisotropic composites, as well as explored coupling with finite element methods. This review will determine the advantages and disadvantages of such methods and serve as a starting point for researchers in the development of peridynamics theory.

A novel approach to determine the translaminar crack resistance curve of composite laminates by means of a machine learning model is presented in this paper. The main objective of the proposed method is to extract hidden information of crack resistance from strength values of center-cracked laminates. Compared to traditional measurements, the notable advantage is that only tensile strength values are required which can be obtained by a rather simpler experimental procedure. This is achieved by the incorporation of the finite fracture mechanics, which links crack resistance with strength values. In order to get training dataset, a semi-analytical method using both finite element method and finite fracture mechanics is employed to generate strength values of center-cracked specimens with different random R-curves, which serve as inputs for our artificial neural network. Regarding the outputs, principal component analysis is performed to reduce dimensionality and find suitable descriptors for crack resistance curves. After successfully training machine learning model, experimental studies on basalt fiber reinforced laminates are conducted as validation. Results have proven the effectiveness of the proposed strategy for predicting crack resistance curves, as well as the feasibility of using machine learning-based framework to find out more information about composites from simple experimental data.