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The “Structural Health Monitoring” is a project supported by National Natural Science Foundation for Distinguished Young Scholars of China (Grant No. 50725828). To meet the urgent requirements of analysis and assessment of mass monitoring data of bridge environmental actions and structural responses, the monitoring of environmental actions and action effect modeling methods, dynamic performance monitoring and early warning methods, condition assessment and operation maintenance methods of key members are systematically studied in close combination with structural characteristics of long-span cablestayed bridges and suspension bridges. The paper reports the progress of the project as follows. (1) The environmental action modeling methods of long-span bridges are established based on monitoring data of temperature, sustained wind and typhoon. The action effect modeling methods are further developed in combination with the multi-scale baseline finite element modeling method for long-span bridges. (2) The identification methods of global dynamic characteristics and internal forces of cables and hangers for long-span cable-stayed bridges and suspension bridges are proposed using the vibration monitoring data, on the basis of which the condition monitoring and early warning methods of bridges are developed using the environmental-condition-normalization technique. (3) The analysis methods for fatigue loading effect of welded details of steel box girder, temperature and traffic loading effect of expansion joint are presented based on long-term monitoring data of strain and beam-end displacement, on the basis of which the service performance assessment and remaining life prediction methods are developed.

A reliability assessment method of fatigue life based on the long-term monitoring data is developed for welded details in steel box girder, and the application research is presented with examples of welded rib-to-deck details in Runyang Bridges. Firstly the fatigue damage limit-state function is established based on S-N curves and Miner’s rule, and the probability distribution characteristics of the coefficients in the function are discussed in detail. The uncertainties in fatigue loading effects are mainly studied based on long-term monitoring data. In the traditional studies, only the uncertainty of equivalent stress range is considered in fatigue reliability assessment. However, stress cycle number is also treated as a random variable in this paper because we know traffic flow every day differs in a thousand ways. Then the optimization method is employed to calculate the fatigue reliability. After studying the changing law of the reliability indices with time and the effect of the randomness of stress cycle number on reliability, the effect of the traffic growth on the reliability is studied. This study shows that the uncertainty in the fatigue life of the welded details can be well studied based on structural health monitoring, so it is necessary to carry out long-term strain monitoring of the welded details for accurate fatigue reliability assessment during the whole service period.

By using of long-term monitoring data of Runyang Suspension Bridge, the improved back-propagation neural networks (BPNNs) are formulated for modeling the correlations between modal frequencies and environmental conditions including wind, temperature and vehicle load. Then, with the correlation models the environmental effects on modal frequencies are quantified and the abnormal changes of measured frequencies are detected by means of the hypothesis tests. Analysis results reveal that BPNN-based correlation models improved by both early stopping and Bayesian regularization techniques exhibit excellent generalization capability. And the developed correlation models can effectively reduce the environmental variability in modal frequencies. The t-test method provides a good capability to detect the damage-induced 0.16% and 0.12% abnormal changes of the 5th and 6th modal frequencies, respectively. Hence, the proposed method is suitable for real-time monitoring of suspension bridge conditions.