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Reliability-based design optimization of crane bridges using Kriging-based surrogate models

  • Xiaoning FanEmail author
  • Pingfeng Wang
  • FangFang Hao
Industrial Application

Abstract

Cranes as indispensable and important hoisting machines of modern manufacturing and logistics systems have been wildly used in factories, mines, and custom ports. For crane designs, the crane bridge is one of the most critical systems, as its mechanical skeleton bearing and transferring the operational load and the weight of the crane itself thus must be designed with sufficient reliability in order to ensure safe crane services. Due to extremely expensive computational costs, current crane bridge design has been primarily focused either on deterministic design based on conventional design formula with empirical parameters from designers’ experiences or on reliability-based design by employing finite-element analysis. To remove this barrier, the paper presents the study of using an advanced surrogate modeling technique for the reliability-based design of the crane bridge system to address the computational challenges and thus enhance design efficiency. The Kriging surrogate models are first developed for the performance functions for the crane system design and used for the reliability-based design optimization. Comparison studies with existing crane design methods indicated that employing the surrogate models could substantially improve the design efficiency while maintaining good accuracy.

Keywords

Bridge crane Design Reliability Kriging Surrogate models 

Notes

Acknowledgements

The first author would like to acknowledge the support of this work by the National Natural Science Foundation of China under grant no. 51275329. The second author would like to acknowledge the support of this work by the National Science Foundation (NSF) of the United States through the Faculty Early Career Development (CAREER) award (CMMI-1351414) and the NSF award (CMMI-1538508).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringTaiyuan University of Science and TechnologyTaiyuanChina
  2. 2.Department of Industrial and Enterprise Systems EngineeringUniversity of Illinois at Urbana ChampaignUrbanaUSA

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