Assessment of health state of large-scale infrastructure systems are crucial to ensure their operational safety. In this study, we propose the image-based conditional assessment of large-scale systems using deep learning approaches. The deep convolutional neural networks are optimally designed for satellite images to extract the sensitive features for assessment. The findings show that the machine learning methods exhibit great potential for infrastructure assessment, such as high bridges, and oil/gas pipeline assessment at both spatial and temporary scales over conventional methods.
corrosion still responds for huge maintenance cost of nationwide civil structures. In this study, we explored a machine learning approach to extract information from sensory data for early-age corrosion-induced damage identification and classification. Lamb-wave guided signals of steel samples collected from simulated corrosion damage were used for model training and calibration. The results showed that the machine learning method allowed effective information fusion for early-age corrosion.
As compared to conventional physics-based techniques, advances in sensors and computing technologies have been promoting data-enabled structural diagnosis and conditional assessment using machine learning techniques in structural health monitoring (SHM). Machine learning helps civil engineers to extract valuable information from large amount of data to make time-sensitive decision. The application of different machine learning techniques to large-scale civil structures is, however, still impeded by challenges. In this study, we use representative supervised support vector machine (shallow learning) and deep Bayesian deep belief network (deep learning) to demonstrate their merits and limitations in structural diagnosis and conditional assessment. A benchmark in the literature is used for the demonstration. The results showed that the shallow learning highly relies on the hand-crafted features, while optimization of kernels is another challenge during learning process. The deep learning could promote the learning accuracy without kernel design. Although the noise could lead to difficulty in data mining, the comparison demonstrated that the deep learning has less sensitivity to the impacts of noise interference than those of shallow learning.
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