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

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.

Automated summary

This paper reviews the historical development and recent advances of machine learning in building structural design and performance assessment, including predicting structural response and performance, interpreting experimental data, information retrieval using images and text, and recognizing patterns in structural health monitoring data. The challenges of integrating machine learning into structural engineering practice are identified, along with discussions on future research opportunities.