Investigation of Machine Learning Methods for Structural Safety Assessment under Variability in Data: Comparative Studies and New Approaches
Publication: Journal of Performance of Constructed Facilities
Volume 35, Issue 6
Abstract
Due to the importance of civil structures and infrastructures, structural safety assessment or structural health monitoring has become a basic necessity for every society. Recent developments of sensing and data acquisition systems enable civil engineers to exploit machine learning methods based on data-driven strategies for structural safety assessment and damage detection. However, the choice of an appropriate machine learning method may be problematic, particularly under some challenging issues such as the negative effects of environmental and/or operational variability (EOV), and the necessity of estimating some influential unknown elements of parametric machine learning methods called hyperparameters. Accordingly, this article focuses on three main aspects: (1) comparing various machine learning methods, (2) developing semiparametric algorithms, and (3) proposing automated algorithms for hyperparameter optimization of semiparametric and parametric machine learning methods. An innovative automated output-only approach is proposed to qualitatively and relatively predict the levels of EOV in terms of strong or weak variability. The main contributions of this article include comparing various machine learning methods, which will enable civil engineers to choose the most appropriate technique, and proposing automated approaches to hyperparameter optimization and variability level prediction. Dynamic and statistical features extracted from measured vibration data of two full-scale bridges were considered to perform the comparative studies and investigate the proposed methods. The results demonstrated that the semiparametric methods provide the best performance when their unknown parameters are determined appropriately.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
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Information & Authors
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Published In
Journal of Performance of Constructed Facilities
Volume 35 • Issue 6 • December 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jun 3, 2021
Accepted: Jul 28, 2021
Published online: Sep 22, 2021
Published in print: Dec 1, 2021
Discussion open until: Feb 22, 2022
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