STATISTICAL EVALUATION OF FRACTURE-TOUGHNESS TEST DATA

Application of fracture mechanics for verification of the fracture res istance of structural components and pipelines is well established in the offshore industry, and development of reliability analysis methods and calibration of assessment procedures is now in progress. One impo rtant parameter in fracture mechanics evaluations is the fracture toug hness-a parameter characterized by large scatter, sensitivity to fabri cation conditions and large costs related to testing. This has raised the need for efficient methods for characterization of the fracture to ughness test data in terms of characteristic values and parametric dis tribution functions. Two relevant data sets are investigated with resp ect to their statistical properties and the ability of different distr ibution functions to fit the data. Three methods for estimation of cha racteristic values for fracture toughness are described. Their relevan ce and capabilities are investigated by numerical simulations with sam ple data sets drawn randomly from a larger population. It is concluded that the distribution functions based on a physical model of the frac ture have disadvantages such as unstable behaviour for small data sets and lack of capability in providing certain estimations. The log-norm al distribution showed a stable and predictable behaviour for sample s izes down to 3, the methods for calculation of distribution parameters and characteristic values with specified confidence levels are demons trated to be satisfactory.