Tunneling parameters optimization based on multi-objective differential evolution algorithm
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METHODOLOGIES AND APPLICATION
Tunneling parameters optimization based on multi-objective differential evolution algorithm Hongyuan Wang1
•
Jingcheng Wang1 • Yaqi Zhao1 • Haotian Xu1
Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract This paper focuses on the geological adaptive control of tunneling boring machine (TBM). To deal with the issue that the geological condition is uncertain, clustering analysis is used to identify geological types based on two important indices indicating the strength of rock. Based on the results from above, the principles of variation and range of tunneling parameters under different rock condition are determined. Furthermore, considering several vital performances of TBM during tunneling operation, a multi-objective optimization problem is proposed. In the light of non-dominated sorting and crowded distance evaluation concepts in non-dominated sorting genetic algorithm-II, the proposed multi-objective optimization problem is solved by using differential evolution algorithm. Based on the practical construction data, the simulation results show that the proposed method is effective in improving the performance of tunneling operation of TBM, compared with unoptimized operating actions from current systems. Keywords Multi-objective optimization Clustering analysis Differential evolution algorithm Tunnel boring machine Geological adaptive control
1 Introduction TBM is an efficient large-scale machine that is widely used in large tunnel construction, especially for geology with hard rock and certain special geological conditions. This kind of special manufacturing equipment plays an important role in civil infrastructure construction, such as subway engineering, water conservancy, subsea tunnel (Liu et al. 2016a; Zheng et al. 2016). Reasonable setting of TBM tunneling parameters (such as advancing speed and cutterhead rotation speed) is a vital section regarding to the performance of TBM operation. Current TBM systems mainly adjust control parameters by operators (also called driver) according to their own experiences. However, such manipulating is very conservative and inefficient,
Communicated by V. Loia. & Hongyuan Wang [email protected] 1
Department of Automation, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Jiao Tong University, Shanghai 200240, China
especially for varying geological conditions. Even worse, the geological conditions are unknown in advance or its estimation is not accurate. The overview and development of TBM can be found in Home (2016), Ba¨ppler (2016) Gong et al. (2016), and its dynamics can be referred to Li et al. (2013,2011). There are relatively few studies on the prediction and setting methods of control parameters at present, which are mainly based on the surrounding of rock parameters, like rock joint spacing, joint tendency, quality indicators (RQD), uniaxial compressive strength (UCS). The literature (Li et al.
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