Research Of Structural Reliability Analysis Method

During construction and service, structures are subjected to the various loads, such as the self weight, the weight of equipments, crowd, and vehicle and to the impact from natural environment such as wind, rain, snow and so on. Also, structures have characteristics of high construction cost and long working period, and their reliability not only influences on the production practice, but also relations to the safety of people's life and property. Therefore, structures must have certain reliability to make sure that they can satisfy each operational function in prescriptive period.Structural reliability theory is a powerful tool to solve structure uncertainties and evaluate structural performance. The present usually reliability analysis method for engineering structures are Monte Carlo Simulate (MCS), First-order Second-moment method (FOSM) and Reliability Surface Method (RSM). FOSM is usually used for the explicit performance function, and the accuracy of FOSM can not satisfy the engineering requirement sometimes for the structures which have higher reliability requirement. MCS has to use a large number of sample test and the efficiency is low. The implicit performance function is approached with RSM, and it's an effective way to solve the implicit performance function reliability analysis, and the key issue of RSM is how to construct a high-fidelity, stable, easy-used response surface function with fewer samples.This thesis concludes domestic and foreign research present situation of the structure reliability. Based on a study of present reliability analysis methods, the main task of this thesis is to find a more efficient algorithm and try to apply it to engineering practice.The main work of this thesis is as follows:(1) On calculating methods of reliability for the explicit performance function, a clear understanding has been formed on the computation principle and the advantages and disadvantages of the present algorithms. It laid a good theoretical foundation for the algorithm proposed in the thesis.(2) On calculating methods of reliability for the implicit performance function, this thesis firstly elaborates on the computation principle and the advantages and disadvantages of Polynomial Response Surface Method, Artificial Neural Networks Response Surface Method and Support Vector Regression Response Surface Method, and then discusses the selection of parameters.(3) Two improved response surface method has been put forward which is named Quadratic Polynomial Response Surface-Curvature Fitting Method and Quadratic Polynomial Response Surface-Laplace integral Method.The main conclusions obtained in this thesis are as follow:(1) Based on FOSM, Second-order second-moment method modifies the results of FOSM, and has better calculation accuracy. The calculation accuracy of curvature fitting method depends on the degree of performance function fitted by quadratic polynomial. The calculation accuracy of laplace integral depends on the value of design point.(2) Quadratic Polynomial Response Surface Method whose computation principle is simple can calculates the precise value of design point for most implicit performance function. But for some special highly nonlinear structure, Quadratic Polynomial Response Surface Method can't get precise value of design point or can't be convergent. By contrast, Support Vector Regression Response Surface Method has good convergent, and it also reduce calculation amount. But the computation principle of Support Vector Regression Response Surface Method is complex, and the selection of its parameters isn't proved in theory. The values of its parameters are given in this thesis. Because Artificial Neural Networks Response Surface functions is highly nonlinear function, so it's difficult to get precise value of design point in theory. Because the selection of initially sample isn't proved in theory, Latin Hypercube Sampling has been put forward in this thesis, and effective parameter values are proposed proved by numerical examples.(3) Two improved response surface method has been put forward which is named Quadratic Polynomial Response Surface-Curvature Fitting Method and Quadratic Polynomial Response Surface-Laplace integral Method. Proved by numerical examples, these methods are easy to operate and effectively improve the calculation accuracy. Furthermore these methods apply to engineering practice well.