Authors:
Koya Ihara
1
;
Shohei Kato
2
;
Takehiko Nakaya
3
;
Tomoaki Ogi
3
and
Hiroichi Masuda
3
Affiliations:
1
Dept. of Computer Science and Engineering, Graduate School of Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555 and Japan
;
2
Dept. of Computer Science and Engineering, Graduate School of Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, Japan, Frontier Research Institute for Information Science, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555 and Japan
;
3
Shimizu Corporation, 2-16-1 Kyobashi, Chuo-ku, Tokyo 104-8370 and Japan
Keyword(s):
Constrained Combinatorial Optimization, Genetic Algorithm, Particle Swarm Optimization, Shield Tunneling.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Constraint Satisfaction
;
Enterprise Information Systems
;
Evolutionary Computing
;
Industrial Applications of AI
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing
;
Symbolic Systems
Abstract:
It is expected that artificial intelligence reduce labor and improve productivity of the shield tunneling, which is one of the tunnel construction method. In the planning process of the shield tunneling, segments of the tunnel are assigned along to the predetermined curve called the planning line. Conventionally, skilled engineers manually assign the segments to minimize the amount of gaps between each segment and the planning line. Nevertheless, we have only to reduce each gap less than a tolerance, and there is a demand to reduce the amount of soil excavated along to the segments. Handling the reducing gaps as constraints and reducing the amount of excavated soil as an objective, this paper addresses the segment assignment as a constrained combinatorial optimization problem. These constraints are severe, and the problem have an extremely narrow feasible region. To solve the problem we proposed e constrained discrete genetic algorithm (eDGA) and e constrained integer categorical par
ticle swarm optimization (eICPSO), adapting a constraint handling method called the e constrained method to the discrete genetic algorithm and the integer categorical particle swarm optimization. The effectiveness of the eDGA and eICPSO to the segment assignment is shown by the two-dimensional simulator experiment using real construction data. The experimental results show that the proposed method have a potential to find the segment assignment reducing the amount of excavated soil as compared to the conventional method (skilled engineer) while keeping the all gaps between segments and the planning line falling within the tolerance.
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