Urban Big Data and the Development of City Intelligence

This study provides a definition for urban big data while exploring its features and applications of Chi- na＇s city intelligence. The differences between city intelligence in China and the ＂smart city＂ concept in other countries are compared to highlight and contrast the unique definition and model for China＇s city intelligence in this paper. Furthermore, this paper examines the role of urban big data in city intel- ligence by showing that it not only serves as the cornerstone of this trend as it also plays a core role in the diffusion of city intelligence technology and serves as an inexhaustible resource for the sustained development of city intelligence. This study also points out the challenges of shaping and developing of China＇s urban big data. Considering the supporting and core role that urban big data plays in city intel- ligence, the study then expounds on the key points of urban big data, including infrastructure support, urban governance, public services, and economic and industrial development. Finally, this study points out that the utility of city intelligence as an ideal policy tool for advancing the goals of China＇s urban de- velopment. In conclusion, it is imperative that China make full use of its unique advantages-including using the nation＇s current state of development and resources, geographical advantages, and good hu- man relations-in subjective and objective conditions to promote the development of city intelligence through the proper application of urban big data.

Realtime prediction of hard rock TBM advance rate using temporal convolutional network(TCN)with tunnel construction big data

Real-time dynamic adjustment of the tunnel bore machine(TBM)advance rate according to the rockmachine interaction parameters is of great significance to the adaptability of TBM and its efficiency in construction.This paper proposes a real-time predictive model of TBM advance rate using the temporal convolutional network(TCN),based on TBM construction big data.The prediction model was built using an experimental database,containing 235 data sets,established from the construction data from the Jilin Water-Diversion Tunnel Project in China.The TBM operating parameters,including total thrust,cutterhead rotation,cutterhead torque and penetration rate,are selected as the input parameters of the model.The TCN model is found outperforming the recurrent neural network(RNN)and long short-term memory(LSTM)model in predicting the TBM advance rate with much smaller values of mean absolute percentage error than the latter two.The penetration rate and cutterhead torque of the current moment have significant influence on the TBM advance rate of the next moment.On the contrary,the influence of the cutterhead rotation and total thrust is moderate.The work provides a new concept of real-time prediction of the TBM performance for highly efficient tunnel construction.