.png){kind=icon}
返回
Constrained Bayesian optimization for engineering bridge design
.png){kind=icon}
OA
AI
DOI：10.1007/s00158-024-03951-3.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Designing a bridge is a complex endeavour, involving multiple variables, limitations and requirements. The design process often includes high-fidelity analyses that are computationally expensive, and the internal working tools of the analysis software are often unknown. This limits the applicability of conventional numerical optimization, especially due to time constraints. As a mean to reduce the computational burden, surrogate modelling may be applied. Surrogate models are constructed on the basis of observed results from the computationally expensive high-fidelity analyses, and serves as a fast approximation of unobserved regions in the design space. If probabilistic surrogates are applied, the probabilistic element may be exploited in the optimization phase, resulting in a scheme known as Bayesian optimization. In this article, it is described how to derive a constrained Bayesian optimization scheme in the process of bridge design, where both the goal and constraints are approximated using probabilistic surrogates. The article also presents a case study where constrained Bayesian optimization is applied to a three-span post-tensioned concrete girder and the results are compared to conventional surrogate-based optimization. The results from the case study show that the Bayesian optimization schemes converge after about six iterations, significantly faster than the conventional surrogate optimization scheme, with a consistently higher relative improvement-providing a faster and more confident process for surrogate-based optimization.
Keyword:
Bayesian optimization
Bridge engineering
Surrogate modelling
Gaussian process regression
Constrained optimization
期刊
IF:
4
论文数:
4.9K
被引数:
1.8W
机构
暂无机构信息
引用论文
分享收藏Taking the Human Out of the Loop: A Review of Bayesian Optimization将人类带出循环: 贝叶斯优化的回顾.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming关于大规模非线性规划的内点滤波器线搜索算法的实现
PREAI
分享收藏SciPy 1.0: fundamental algorithms for scientific computing in PythonSciPy 1.0: Python中科学计算的基本算法NATURE METHODS
OAAI
NATURE METHODSIF32.1
OAAI分享收藏
查看更多内容