Bridge Resistance Updating Based on the General Particle Simulation Algorithms of Complex Bayesian Formulas

The existing bridges are subjected to time-variant loading and resistance degradation processes. How to update resistance probability distribution functions with resistance degradation model and proof load effects has become one of the research hotspots in bridge engineering field. To solve with the above issue, this paper proposed the general particle simulation algorithms of complex Bayesian formulas for bridge resistance updating. Firstly, the complex Bayesian formulas for updating resistance probability model are built. For overcoming the difficulty for the analytic calculation of complex Bayesian formulas, the general particle simulation methods are provided to obtain the particles of complex Bayesian formulas; then, with the improved expectation maximization optimization algorithm obtained with the combination of K-MEANS algorithm and Expectation Maximization (EM) algorithm, the above simulated particles can be used to estimate the posteriori probability density functions of resistance probability model; finally, a numerical example is provided to illustrate the feasibility and application of the proposed algorithms.

Resistance Assessment of Service-Proven Aging Bridges Incorporating Deterioration-Load Dependency

The historical vehicles passed through an existing bridge can be regarded as proof-loading tests for the bridge, and, as a result, are evident of the bridge’s performance. Such service history information has been utilized to update the estimate of bridge resistance in previous studies with the help of a Bayesian method, where the resistance deterioration process was assumed to be independent of the vehicle load process. This assumption is, however, untenable in many cases where the deterioration stochastic process is statistically correlated with the load process (e.g., a greater load intensity may affect/accelerate the deterioration of structural resistance and the accumulation of structural fragility). With this regard, this paper investigates the effect of correlation between the resistance deterioration and load processes on updating the resistance of aging bridges with prior service load information. The copula function is employed to model the joint distribution of the correlated deterioration and load processes, with which the correlation is measured by the Kendall’s tau. A new method is developed in this paper to assess the updated bridge resistance taking into consideration the deterioration-load dependency in an explicit form. The applicability of the proposed method is illustrated using an existing RC beam bridge. The sensitivity analysis is conducted to examine how the deterioration-load dependency affects the updated resistance of service-proven aging bridges.

Deterministic and Probabilistic Analysis of NPP Communication Bridge Resistance Due to Extreme Loads

This paper presents the experiences from the deterministic and probability analysis of the reliability of communication bridge structure resistance due to extreme loads - wind and earthquake. On the example of the steel bridge between two NPP buildings is considered the efficiency of the bracing systems. The advantages and disadvantages of the deterministic and probabilistic analysis of the structure resistance are discussed. The advantages of the utilization the LHS method to analyze the safety and reliability of the structures is presented

Study on the Online Dynamic Weighing System of Heavy Duty Apron Feeder Based on Labview

The heavy duty apron feeder is a mechanism for material transportation. According to the differences on test requirements and effects, the traditional sensor development test weighing system presently is adopted widely. In the design, resistance strain gauge is installed on the feeder chain plate; and the whole-armed bridge compensates temperature to test the voltage value outputted by bridge resistance gauge; based on labview software, the virtual online dynamic weighing system is developed, realizing online monitoring at full time, warning, alarming and shutdown protective operation.