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Qiang Xu,Shengxiang Lei,Yongquan Zhu,Wei Zhao,Cong Wang,Dapeng Wang 대한토목학회 2022 KSCE JOURNAL OF CIVIL ENGINEERING Vol.26 No.9
The deformation pattern of the stratum caused by constructing a new metro tunnel crossing an existing tunnel is different from the deformation pattern caused by general tunnel construction. However, the prediction results by the ordinary surface settlement prediction model often bring significant errors because the complex influence of existing tunnels on the surface settlement caused by the excavation of new tunnels is always neglected. Based on the equivalent layered method and stochastic medium theory, a prediction model for the surface settlement due to excavating a new tunnel under an existing tunnel in the heterogeneous stratum was established. By equating the bending stiffness of an existing tunnel before and after applying the equivalent layered method, the layer index was determined. The critical parameters of the stochastic medium theory were derived based on the relationship between the critical parameters of both the Peck empirical formula and the stochastic medium theory. The surface settlement of some typical projects was predicted and compared by the prediction model in this paper and the ordinary prediction model. The comparison shows that the proposed prediction model and parameter determination method in this paper had high accuracy and applicability. The results of the prediction model in this paper fit the results of numerical calculation. The research of this paper can provide a new method for the theoretical prediction of surface settlement caused by the excavation of a new tunnel under an existing tunnel in the heterogeneous stratum and the determination of critical parameters of the stochastic medium theory.problem in the construction industry, and helps reducing the material waste and budget cost.
Dezhou Xu,Chunhua Zheng,Yunduan Cui,Shengxiang Fu,Namwook Kim,Suk Won Cha 한국정밀공학회 2023 International Journal of Precision Engineering and Vol.10 No.1
Hybrid vehicles (HVs) that equip at least two different energy sources have been proven to be one of effective and promising solutions to mitigate the issues of energy crisis and environmental pollution. For HVs, one of the core supervisory control problems is the power distribution among multiple power sources, and for this problem, energy management strategies (EMSs) have been studied to save energy and extend the service life of HVs. In recent years, with the rapid development of artificial intelligence and computer technologies, learning algorithms have been gradually applied to the EMS field and shortly become a novel research hotspot. Although there are some brief reviews on the learning-based (LB) EMSs for HVs in recent years, a state-of-the-art and thorough review related to the applications of learning algorithms in HV EMSs still lacks. In this paper, learning algorithms applied in HV EMSs are categorized and reviewed in terms of the reinforcement learning algorithms and deep reinforcement learning algorithms. Apart from presenting the recent progress of learning algorithms applied in HV EMSs, advantages and disadvantages of different learning algorithms and LB EMSs are also discussed. Finally, a brief outlook related to the further applications of learning algorithms in HV EMSs, such as the integration towards autonomous driving and intelligent transportation system, is presented.