The growing demand for a more efficient maintenance of concrete bridges requires a model that tracks the deterioration of each bridge based on inspection data. Although it has been expected that machine learning could be applied to this problem, inspection data sparsely distributed over time are not suitable for machine learning in contrast to the continuous big data usually targeted. This study applies machine learning to a regression model of crack formation and propagation using inspection data to confirm the applicability. It includes the selection of the optimal algorithm, development of the model based on a novel methodology, and factor analysis using the model. Accordingly, the model was constructed by Gaussian process regression and it could appropriately extract the differences in the progress of crack damage due to multiple influential factors. The results demonstrate the excellent applicability of machine learning even to sparse data.
The data acquired in civil engineering tasks often involve high acquisition costs, and the available datasets tend to have a limited number of samples and are highly biased. To estimate the performance of machine learning models, k‐fold cross‐validation (k‐CV) is widely used. However, if only limited data are available and the data distribution is biased, k‐CV tends to overestimate the performance for practical applications. This study proposed a new estimator, leave one reference out and k‐CV (LORO‐k‐CV), to determine the practical performance of machine learning models, that is, the generalization performance for population data in the target task, in case data are collected by multiple references resulting in biased data. LORO‐k‐CV is a combination of a new concept, LORO‐CV, that estimates the performance in the extrapolation region of the training data without human intervention and k‐CV, considering the ratio of the interpolation and extrapolation regions. The efficacy of LORO‐k‐CV was validated with its application to the regression task for the chloride‐ion concentration of concrete structures. To more specifically demonstrate the advantages of LORO‐k‐CV in model construction, the feature selections were conducted using both k‐CV and LORO‐k‐CV methods. These results revealed that LORO‐k‐CV can effectively construct a model with improved generalization performance even from the same data in cases where data are collected by multiple references, resulting in biased data.
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