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Bolted shear connectors have the advantages of being easily fitted and dismantled during construction, the initial elastic stiffness of which has a great influence on the structural performance of the connected composite structures. In this paper, the initial elastic behaviors of three types of bolted shear connectors used in steel-concrete composite structures (i.e., the bolt with nonembedded nut, the bolt with single-embedded nut, and the bolt with double-embedded nuts) are investigated using finite element analysis (FEA). After the FE models are verified against the experimental results in other literature, an extensive parametric study is carried out to investigate the effects of eight parameters of the composite structures on the initial shear stiffness and tension stiffness as well as coupling stiffness. Empirical formulas are subsequently developed for obtaining the initial elastic stiffness of the bolted shear connectors, based on which further FEA is performed. The FEA results are in good agreement with the experimental results, illustrating the effectiveness of the empirical formulas.

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This paper takes intelligent control technology as the core technology, combines with meteorological and road freezing information collection technology and water supply and drainage technology. Firstly, this paper introduces the design of grading subsystem of fixed spray type anti-ice and snow removal control system, and proposes a new type of nozzle suitable for the viaduct at the same time, which can improve spraying effect of the deicing liquid while improving safety guarantee. Then based on analysis of collected on-site road conditions and meteorological actual data and combined with logic control algorithm, a live ice and snow early warning mathematical model is designed to predict the snowy condition of the road surface, and automatically control equipment to spray deicing fluid before or after road icing.. It uses PID control technology to ensure pressure balance inside the spray pipe. The real-time field data is connected to the cloud server with 4G wireless transmission technology, and the existing data and current data are compared and analyzed through the recursive neural network, so as to further accurately predict the ice and snow situation, classify danger level according to national road safety standards, and release it to other platforms. Based on application of actual field, this paper designs a method that can be combined with prevention in advance and post-disposal to eliminate road icing and reduce risks of vehicle driving safety in severe weather.