Machine learning applications for building structural design and performance assessment: State-of-the-art review

被引:269
|
作者
Sun, Han [1 ]
Burton, Henry V. [2 ]
Huang, Honglan [2 ]
机构
[1] Yahoo Res, Sunnyvale, CA 02902 USA
[2] Univ Calif Los Angeles, Dept Civil & Environm Engn, Los Angeles, CA USA
来源
基金
美国国家科学基金会;
关键词
Machine learning; Artificial intelligence; Building structural design and performance; assessment; Supervised learning; Unsupervised learning; TIME-DEPENDENT DEFORMATIONS; IMPROVED PREDICTION MODEL; DAMAGE DETECTION; NEURAL-NETWORKS; SHEAR-STRENGTH; CONCRETE; REGRESSION; VISION; CLASSIFICATION; CAPACITY;
D O I
10.1016/j.jobe.2020.101816
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Machine learning models have been shown to be useful for predicting and assessing structural performance, identifying structural condition and informing preemptive and recovery decisions by extracting patterns from data collected via various sources and media. This paper presents a review of the historical development and recent advances in the application of machine learning to the area of building structural design and performance assessment. To this end, an overview of machine learning theory and the most relevant algorithms is provided with the goal of identifying problems suitable for machine learning and the appropriate models to use. The machine learning applications in building structural design and performance assessment are then reviewed in four main categories: (1) predicting structural response and performance, (2) interpreting experimental data and formulating models to predict component-level structural properties, (3) information retrieval using images and written text and (4) recognizing patterns in structural health monitoring data. The challenges of bringing machine learning into structural engineering practice are identified, and future research opportunities are discussed.
引用
收藏
页数:14
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