Abstract
A multidisciplinary design optimization model is developed in this article to optimize the performance of the hard rock tunnel boring machine using the collaborative optimization architecture. Tunnel boring machine is a complex engineering equipment with many subsystems coupled. In the established multidisciplinary design optimization process of this article, four subsystems are taken into account, which belong to different sub-disciplines/subsytems: the cutterhead system, the thrust system, the cutterhead driving system, and the economic model. The technology models of tunnel boring machine’s subsystems are build and the optimization objective of the multidisciplinary design optimization is to minimize the construction period from the system level of the hard rock tunnel boring machine. To further analyze the established multidisciplinary design optimization, the correlation between the design variables and the tunnel boring machine’s performance is also explored. Results indicate that the multidisciplinary design optimization process has significantly improved the performance of the tunnel boring machine. Based on the optimization results, another two excavating processes under different geological conditions are also optimized complementally using the collaborative optimization architecture, and the corresponding optimum performance of the hard rock tunnel boring machine, such as the cost and energy consumption, is compared and analysed. Results demonstrate that the proposed multidisciplinary design optimization method for tunnel boring machine is reliable and flexible while dealing with different geological conditions in practical engineering.
Highlights
Tunnel boring machine (TBM) was first designed by Maus in 1846.1 it was not widely adopted until 1960s when Robbins and Wilson mostly solved the challenges in cutter adjustment, cutterhead driving, and thrust.[2]
Fattahi and Babanouri[7] used the hybrid of support vector regression (SVR) and the differential evolution (DE) algorithm, artificial bee colony (ABC) algorithm, and gravitational search algorithm (GSA) to predict the TBM’s performance and found that the hybrid of DE and SVR is more robust than other combinations
The results showed that the optimum cutter spacing deduced from numerical simulations agreed well with those determined from linear cutting machine (LCM) tests
Summary
Tunnel boring machine (TBM) was first designed by Maus in 1846.1 it was not widely adopted until 1960s when Robbins and Wilson mostly solved the challenges in cutter adjustment, cutterhead driving, and thrust.[2]. The whole structure of the hard rock TBM.[3] optimized the edge disk cutters oblique angle, polar radius, and polar angle using nondominated sorting genetic algorithm II (NSGA-II) By applying this method to the TB880E models cutterhead, it is found that the TBM’s performance was significantly improved. Section ‘‘Technology models of the hard rock TBM’s subsystems’’ gives the TBM’s performance indexes used in this article and analyzes the technology models of the subsystems of the hard rock TBM, including the cutterhead system, the thrust system, the cutterhead driving system, and the economic analysis. Tunneling cost per unit length is used in this article to assess the cost of the tunneling engineering excavated by the hard rock TBM, and it consists of two parts: the normal excavation cost and the tools cost as formulated in equation (9). The cutters are assumed to distribute averagely on the cutterhead, so the cutter spacing s (m) can be described as equation (14)
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