There are two specific aims in this study; first is to develop and validate an automated crack detection technique for the fire damaged beam. Second is to investigate whether the detected crack information and thermal-structural behaviors can be numerically related. To fulfill the aims, fire tests and residual strength tests are conducted on RC beams having different fire exposure time periods and sustained load levels. To detect the automated cracks, surface images of the fire damaged beam surfaces are taken with digital cameras and an automatic crack detection method is developed using a convolutional neural network (CNN) which is a deep learning technique primarily used for analyzing intricate structures of high-dimensional data [such as high definition (HD) images and videos]. The quantity of cracks detected using the proposed CNN changes depending on the test variables, and the changing trends are similar to those of the crack lengths obtained from the optical observation. Additionally, it is found that the quantity of the automatically detected cracks is numerically related to the temperatures inside the beams as well as the stiffnesses obtained from the residual strength tests.
The purpose of this study is to investigate the effect of fire-damaged areas associated with wall width on the axial strength of fire-damaged reinforced concrete (RC) walls. Toward that goal, Finite Element (FE) models of RC walls in real scale are generated with various wall widths of (600, 1500, and 3000) mm and number of heated surfaces such as half-surface, single-surface and double-surfaces. For the analyses, experiments are used to obtain temperature distributions inside the walls depending on the fire-damaged areas, and to validate the FE models. The analytical results show that the axial strength of the fire-damaged wall increases linearly with the wall width, except that the ratios of axial force to wall width showed slightly off from the average for the case of walls heated on half of the surface. Using the axial strength data of fire-damaged concrete walls obtained from the current and previous studies, regression analysis is conducted to estimate axial strength reduction ratios of fire-damaged concrete walls, considering various influencing parameters, such as concrete strength, fire-damaged areas, wall width and height. As a conclusion, multiple linear regression formulations from the regressions analyses are able to estimate axial strength reduction ratios of the fire-damaged concrete walls considering various influencing parameters of the wall size, concrete strength and fire-damaged area and the estimations showed good agreements with the data collected from experiments and FE analyses.
Most of the columns in actual fire conditions are heated on partial faces rather than all four sides due to the floor plan, which results in asymmetric behaviors of columns. The asymmetric behaviors of fire-damaged columns may cause more vulnerability to the structural performance. In this study, temperature distribution and residual strength of reinforced concrete columns exposed to fire were investigated according to various heated areas. To achieve the objective, columns were heated for 2 h according to ISO-834 standard time-temperature curve and subsequently tested under the axial loading after a week. The test results show that the residual strength of the fire-damaged columns decreased as the heated area increased, and the residual strength reduced additionally due to asymmetric heating.
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