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Machine learning applications for building structural design and performance assessment: State-of-the-art review
Journal of Building Engineering ( IF 6.4 ) Pub Date : 2020-09-21 , DOI: 10.1016/j.jobe.2020.101816
Han Sun , Henry V. Burton , Honglan Huang

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.



中文翻译:

机器学习在建筑结构设计和性能评估中的应用:最新审查

机器学习模型已显示出可用于预测和评估结构性能,识别结构状况以及通过从各种来源和媒体收集的数据中提取模式来告知抢先和恢复决策的有用方法。本文介绍了机器学习在建筑结构设计和性能评估领域的应用的历史发展和最新进展。为此,提供了机器学习理论和最相关算法的概述,目的是确定适合机器学习的问题和要使用的适当模型。然后将机器学习在建筑结构设计和性能评估中的应用分为四个主要类别:(1)预测结构响应和性能,(2)解释实验数据并建立模型以预测组件级的结构特性;(3)使用图像和文字检索信息;(4)识别结构健康监测数据中的模式。确定了将机器学习引入结构工程实践的挑战,并讨论了未来的研究机会。

更新日期:2020-10-11
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