Since the 1990s, with the continuous advancement of urbanization, the proportion of urban population has gradually increased. There is a serious shortage of land resources, and people’s demand for underground space is increasing day by day. The construction of subway stations has developed into an inevitable trend in the future construction engineering industry, and it is also necessary to select the best solution from various solutions. The purpose of this paper is to study how to evaluate and analyze the economy of the deep foundation pit envelope structure of subway stations based on fuzzy logic, so as to choose the optimal and most economical plan. This paper proposes a fuzzy comprehensive evaluation method based on fuzzy logic, which is a reasonable method for the classic evaluation index. The experimental results of this paper show that in 2015 about 8% of people chose to travel by subway. By 2020, 54.5% of people chose to travel by subway, an increase of 46.5% during this period. It can be seen that more and more people are willing to take the subway, and subway transportation is a public transportation mode with large transportation volume. It has obvious public welfare, and it can relieve the urban traffic pressure very well, so the investment in subway construction in various cities is also increasing.

With the development of science and technology, the demand for traffic has increased, and the requirements for tunnel excavation have become more and more stringent. Tunnel excavation is an important traffic construction engineering technology. Due to the influence of many factors in the excavation process, surface settlement or deformation will inevitably occur, so its deformation must be predicted in real time to prevent safety accidents and property losses. The previous numerical methods and neural network methods cannot accurately predict in real time, and the intelligent neural network model can more accurately predict the deformation of the ground because of the characteristics of adaptive organizational learning according to different situations and different environments. This article aims to study and design an intelligent neural network model to predict and calculate the amount of ground deformation caused by the tunnel excavation process. An intelligent neural network model with more accurate prediction is proposed, and simulation experiments are carried out on tunnel excavation of different terrains, and the accuracy of the model for predicting the deformation amount is calculated. The experimental results show that the prediction error range of the model is 10 times smaller than that of the traditional neural network. The prediction accuracy of this model is above 95%, and the volatility rate of prediction accuracy is lower than 11%, while the volatility rate of traditional prediction accuracy is even more than 365%. The intelligent neural network model can effectively predict the deformation of tunnel excavation.

Establishing and simplifying the high-speed railway seismic source model in near-field was crucial to analyzing the propagation law and energy radiation of high-speed railway seismic wave. With the forward calculation of Green function, the study on the simulation signal was carried out by MATLAB. By comparing the simulation signal with real signal, the Green function in the near-field term of high-speed-railway seismic source was verified to be reliable. In addition, according to simulation signal analysis on vibration curves, frequency spectra and energy distribution maps, the results showed that in the near-field of high-speed railway seismic source，the vibration curves were characterized with two peaks in the direction parallel to the high-speed railway, reflecting the propagation law of high-speed railway seismic wave under the moving-line source, while the propagation law and energy attenuation were obvious in the direction perpendicular to the ground surface. The high-speed railway seismic signal appeared broadband with clear discrete spectral lines, of which the feature frequencies varied from 60Hz to 370Hz, affected by the propagation distance of high-speed railway seismic wave. Within 150 meters, the energy of high-speed railway seismic wave under the feature frequencies below 200Hz attenuated greater, while when the high-speed railway seismic wave propagated farther than 150 meters, that under the feature frequencies between 200Hz to 300Hz attenuated much more. The energy distribution of high-speed railway seismic wave depended on the frequency response property at seismic source, and the frequency response property showed significant difference with the frequency ranging from 40Hz to 500Hz.