Safety evaluation of truss structures using nested discrete Bayesian networks

Abstract

Truss structures have been widely adopted for civil structures such as long-span buildings and bridges. An actual truss system is usually statically indeterminate having numerous members and high redundancy. It is practically difficult to evaluate the truss safety through traditional reliability-based approaches in view of complex failure modes and uncertainties. Moreover, monitoring data are generally insufficient in reality due to limited sensors under cost consideration. Therefore, a nested discrete Bayesian network has been developed for safety evaluation of truss structures. A concept of member risk coefficient is first proposed based on the mechanical relationship between load effects and member resistance. According to the coefficients of all members, member risk sequences are found as the basis for establishing the topology of a member-level Bayesian network. Each network node represents a truss member and a nodal variable having three states: elasticity, plasticity, and failure. Two relevant member nodes are connected by a directed edge whose causality strength is expressed by a conditional probability table. Meanwhile, a system-level network topology is established to reflect the effects of member states on the truss system. The system is assigned with a node having two states: safety and failure. The directed edge of each member node directly points to the system node. Then, the two networks are combined to form a nested network topology. By this means, direct topology learning is avoided in order to find rational and concise topologies satisfying the mechanical characteristics of civil structures. After that, the conditional probability tables for the nested network are obtained through parameter learning on complete numerical observation data. The data acquirement procedure takes into account uncertainties by defining the randomness of cross-sectional areas and external loads. With the conditional probability tables, the nested network is ready for use. When new evidence from limited monitored members is input into the nested network, the state probabilities of the other members, as well as the system, are simultaneously updated using exact inference algorithms. The inference ability using insufficient information well accords with the demand of engineering practice. Finally, the proposed method has been successfully verified against both numerical and experimental truss structures. It was found that the network estimations could be further confirmed with more evidence.

Safety evaluation of truss bridges using continuous Bayesian networks

“…Therefore, the authors have developed a mechanical analysis based method for topology definition of truss structures. 30 The strength failure of members is taken into account. This study further extended the method to consider the unstability of truss members, which is described below.…”

“…At this moment, mechanical analysis may offer more rational topologies than direct learning or expert experience. 30 With respect to the parameter learning for CPDs, a complete sample set is always expected. However, in civil engineering practice, the monitoring data are often incomplete due to a limited number of sensors.…”

Reliability of simple space truss structure

“…Standards that have been regulated have defined deterministic load but cannot be used in a probabilistic approach due to the many variations in load symptoms that can occur. There are many natural conditions and structural components that have the potential as loads, but this load is not available in detail and monitoring data are in fact generally insufficient due to limited equipment with cost consideration (Fang, Tan, and Zhang 2020) so these loads can be considered as uncertain loads.…”