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A new type of square reinforced concrete frame column with a hollow section and spiral stirrups at the section corners is proposed. To investigate its seismic performance, three full-scale columns of this type and one full-scale conventional solid column were designed and tested under a quasi-static load. The failure mode, load-bearing, deformation, and energy-dissipation capacities of the proposed column were evaluated and compared with those of the solid column. The effects of the cavity ratio and axial load on the seismic performance were investigated.

Fatigue life assessment of critical bridge members using online monitoring data has been investigated in recent years. To make a quick and efficient evaluation, a representative block of strain cycles is usually defined. However, such block sometimes fails to cover the change in fatigue damage by temperature fluctuation and traffic growth, which is important to the life prediction of the welds in a long-span suspension bridge. To find the influence of temperature and traffic volume on the fatigue damage, an equivalent vehicle load method is dedicated through finite element analysis, and the statistical disposition of traffic flow has been conducted, using data from the Runyang Bridge. The influence of traffic growth can be estimated and eliminated from the total damage so that the temperature effect on the fatigue damage is finally obtained. It is found that temperature has a linear effect on the fatigue damage. According to the fitted relationship between fatigue damage, temperature and the traffic flow, a more objective fatigue assessment is undertaken, based on the rain-flow counting method and the Palgren-Miner rule.

This paper describes a study on damage identification using wavelet packet analysis and neural networks. The identification procedure could be divided into three steps. First, structure responses are decomposed into wavelet packet components. Then, the component energies are used to define damage feature and to train neural network models. Finally, in combination with the feature of the damaged structure response, the trained models are employed to determine the occurrence, the location and the qualification of the damage. The emphasis of this study is put on multi-damage case. Relevant issues are studied in detail especially the selection of training samples for multi-damage identification oriented neural network training. A frame model is utilized in the simulation cases to study the sampling techniques and the multi-damage identification. Uniform design is determined to be the most suitable sampling technique through simulation results. Identifications of multi-damage cases of the frame including different levels of damage at various locations are investigated. The results show that damages are successfully identified in all cases.