• Structural Engineering and Mechanics
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• v.25 no.1
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• pp.75-89
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• 2007

Post-earthquake reconnaissance and experimental research indicate that squat reinforced concrete (RC) columns in existing buildings or bridge piers are vulnerable to non-ductile shear failure. Recently, several experimental studies were conducted to investigate upgrading the shear resistance capacity of such columns in order to modify their failure mode to ductile one. Among these upgrading methods is the use of fibre-reinforced polymer (FRP) jackets. One of the preferred analytical tools to simulate the response of frame structures to earthquake loading is the lumped plasticity macromodels due to their computational efficiency and reasonable accuracy. In these models, the columns' nonlinear response is lumped at its ends. The most important input data for such type of models is the element's lateral force-displacement backbone curve. The objective of this study is to verify an analytical method to predict the lateral force-displacement ductility relationship of axially and laterally loaded rectangular RC squat columns retrofitted with FRP composites. The predicted relationship showed good accuracy when compared with tests available in the literature.

• Smart Structures and Systems
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• v.31 no.4
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• pp.365-381
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• 2023

The existing vision-based techniques for inspection and condition assessment of civil infrastructure are mostly manual and consequently time-consuming, expensive, subjective, and risky. As a viable alternative, researchers in the past resorted to deep learning-based autonomous damage detection algorithms for expedited post-disaster reconnaissance of structures. Although a number of automatic damage detection algorithms have been proposed, the scarcity of labeled training data remains a major concern. To address this issue, this study proposed a semi-supervised learning (SSL) framework based on consistency regularization and cross-supervision. Image data from post-earthquake reconnaissance, that contains cracks, spalling, and exposed rebars are used to evaluate the proposed solution. Experiments are carried out under different data partition protocols, and it is shown that the proposed SSL method can make use of unlabeled images to enhance the segmentation performance when limited amount of ground truth labels are provided. This study also proposes DeepLab-AASPP and modified versions of U-Net++ based on channel-wise attention mechanism to better segment the components and damage areas from images of reinforced concrete buildings. The channel-wise attention mechanism can effectively improve the performance of the network by dynamically scaling the feature maps so that the networks can focus on more informative feature maps in the concatenation layer. The proposed DeepLab-AASPP achieves the best performance on component segmentation and damage state segmentation tasks with mIoU scores of 0.9850 and 0.7032, respectively. For crack, spalling, and rebar segmentation tasks, modified U-Net++ obtains the best performance with Igou scores (excluding the background pixels) of 0.5449, 0.9375, and 0.5018, respectively. The proposed architectures win the second place in IC-SHM2021 competition in all five tasks of Project 2.