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Indexed by:期刊论文
Date of Publication:2016-10-01
Journal:STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
Included Journals:SCIE、EI、Scopus
Volume:54
Issue:4
Page Number:1073-1092
ISSN No.:1615-147X
Key Words:Tunnel boring machine; Multidisciplinary design optimization; System decomposition; Complex geological condition
Abstract:A Tunnel Boring Machine (TBM) is an extremely large and complex engineering machine that usually works under a complicated geological environment to excavate tunnel underground. Considering the large number of sub-systems that usually belong to different disciplines, it is a challenging task to define, model, and optimize the whole TBM from the perspective of system engineering. Also, due to the complex mechanism and geological environment, the flexibility and efficiency of existing TBM excavation strategies are generally limited. To address these challenges, a multidisciplinary modeling is presented so that corresponding analytical or empirical models of each sub-system are formulated, and a multidisciplinary design optimization (MDO) method is applied to the TBM system optimization. Four excavation strategies are studied and compared, including: (i) two existing excavation strategies, and (ii) two new proposed excavation strategies by making control and/or structure parameters adaptive to geological conditions. Two case studies with these four excavation strategies are presented to illustrate the effectiveness and benefits of designing TBM using MDO methodologies. Wherein, Case I aims to minimize the construction period taking into account the restriction of sub-systems, and Case II simultaneously minimizes construction period, cost, and energy consumption. Since the resulting MDO formulation is straightforward to be solved as a single problem, the All-At-Once (AAO) method is utilized in this paper. The optimization results obtained by modeling the problem as MDO show that the excavation strategy with adaptive control and structure parameters can significantly reduce the total construction time, with lower cost and energy consumption.
