The reliability of mechanical structures is crucial for their safe operation

to address the problem of low accuracy and low efficiency in reliability analysis of complex mechanical structures

a new active learning surrogate model based reliability analysis method was proposed.The spatial location characteristics of excellent fitting samples were studied and three constraints

such as surface constraint

distance constraint

and domain constraint

were proposed accordingly.Correspondingly

three control functions were established to achieve the three constraints.Then

three control functions were organically collaborated

and an effective new learning function

collaboration-mean point constrained learning(CPCL)function was proposed.Combined with the augmented radial basis function(ARBF)

a collaboration-mean point constrained active learning surrogate model(ARBF+CPCL)reliability analysis method was established.Finally

three cases were employed to verify the high computational accuracy and computational efficiency of ARBF+CPCL reliability analysis method

and the application ability of ARBF+CPCL method in practical engineering cases was proved through the reliability analysis example of the turbine disk.