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The low atmospheric pressure and low oxygen content in high-altitude environment have great impacts on the functions of human body. Especially for the personnel engaged in complicated physical labor such as tunnel construction, high altitude can cause a series of adverse physiological reactions, which may result in multiple high-altitude diseases and even death in severe cases. Artificial oxygen supply is required to ensure health and safety of construction personnel in hypoxic environments. However, there are no provisions for oxygen supply standard for tunnel construction personnel in high-altitude areas in current tunnel construction specifications. As a result, this paper has theoretically studied the impacts of high-altitude environment on human bodies, analyzed the relationship between labor intensity and oxygen consumption in high-altitude areas and determined the critical oxygen-supply altitude values for tunnel construction based on two different standard evaluation systems, i.e., variation of air density and equivalent PIO2. In addition, it has finally determined the oxygen supply standard for construction personnel in high-altitude areas based on the relationship between construction labor intensity and oxygen consumption.

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Traditional control algorithm of shutting down the air ducts for a fixed period is not applicable to take both the riding comfort and the air quality inside high-speed train carriages into account in long tunnels. Inspired by the morphological similarity of the tunnel pressure waves generated by the same train passes through the same tunnel, an upgraded iterative learning control algorithm for suppressing the air pressure variation excited by the quasi-periodic varying-amplitude tunnel pressure wave is developed. Firstly, the mathematical model of the control system is established, in which the air ducts, gaps and random interferences are considered. Then, the methodology of determining the goal in each iteration is formed, and the implementation of the iterative learning control algorithm is discussed. Finally, simulations of the algorithm are carried out. The simulation results show that in the upgraded iterative learning control algorithm, both the goal and the output of the air pressure inside the carriage will converge into a range determined by the amplitude and random interferences. By comparing with the traditional control algorithm, the upgraded iterative learning control algorithm is more adaptable to meet the needs of riding comfort.