A Framework to Automate Reliability-based Structural Optimization based on Visual Programming and OpenSees

Proceedings of the 8th International Conference on Construction Engineering and Project Management (ICCEPM 2020), 2020

Recommended citation: Lin, J.R.*, Xiao, J., and Zhang, Y. (2020). "A Framework to Automate Reliability-based Structural Optimization based on Visual Programming and OpenSees" Proceedings of the 8th International Conference on Construction Engineering and Project Management (ICCEPM 2020). 225-234. Hong Kong. https://doi.org/10.13140/RG.2.2.23831.73126 cited by count

Abstract

Reliability-based structural optimization usually requires designers or engineers model different designs manually, which is considered very time consuming and all possibilities cannot be fully explored. Otherwise, a lot of time are needed for designers or engineers to learn mathematical modeling and programming skills. Therefore, a framework that integrates generative design, structural simulation and reliability theory is proposed. With the proposed framework, various designs are generated based on a set of rules and parameters defined based on visual programming, and their structural performance are simulated by OpenSees. Then, reliability of each design is evaluated based on the simulation results, and an optimal design can be found. The proposed framework and prototype are tested in the optimization of a steel frame structure, and results illustrate that generative design based on visual programming is user friendly and different design possibilities can be explored in an efficient way. It is also reported that structural reliability can be assessed in an automatic way by integrating Dynamo and OpenSees. This research contributes to the body of knowledge by providing a novel framework for automatic reliability evaluation and structural optimization.

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Recommended citation: Lin, J.R.*, Xiao, J., and Zhang, Y. (2020). "A Framework to Automate Reliability-based Structural Optimization based on Visual Programming and OpenSees" Proceedings of the 8th International Conference on Construction Engineering and Project Management (ICCEPM 2020). 225-234. Hong Kong.

This research is supported by the Beijing Natural Science Foundation (No. 8194067), the National Science Foundation of China (No. 51908323), the National Key R&D Program of China (No. 2018YFD1100900), and the Young Elite Scientists Sponsorship Program by China Association for Science and Technology (No. QNRC2016001)

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