The structural behavior of concrete beams containing recycled coarse aggregates (RCAs) was investigated in this study using detailed experimental data. Twelve concrete beams were tested in the experimental program: nine beams with varying RCA contents and three control beams with natural coarse aggregates (NCAs). The parameters for investigating the structural behavior of the RCA concrete beams under flexure were the RCA content (30%, 50%, and 100%) and tensile rebar ratio (0.50%, 0.79%, and 1.14%). The crack pattern of the RCA beams was similar to that of the NCA beams; however, the RCA beams exhibited smaller crack spacing than the NCA beams. The flexural strength was slightly affected by the RCA content. However, the ductility of the beam was not significantly influenced by the RCA content. A comparison of the experimental results and the calculations from the ACI 318 and EC 2 provisions for the flexural strength showed that the current provisions conservatively predicted the flexural strength of the RCA concrete beams.

Ultrahigh-performance concrete (UHPC) and high-strength concrete (HSC) are currently widely used because of their distinct superior properties. Thus, a comprehensive comparison of the flexural behavior of UHPC and HSC beams is presented in this study. Nine UHPC beams and three HSC beams were subjected to pure bending tests to investigate the effect of various reinforcement ratios and steel fiber volume contents on the cracking and failure patterns, load-deflection behavior, ductility, and flexural toughness of these beams. The addition of steel fibers in the UHPC improved the energy absorption capacity of the beams, causing the UHPC beams to fail via rebar fracture. The deflection and curvature ductility indices were determined and compared in this study. The ductility indices of the HSC beam tended to decrease sharply as the rebar ratio increased, whereas those of the UHPC beam did not show a clear trend with respect to the rebar ratio. In addition, a comparison between the results in this study and the results from previous studies was performed. In this study, the addition of steel fiber contents up to 1.5% in UHPC increased the load capacity, ductility, and flexural toughness of the UHPC beams, whereas the addition of a steel fiber content of 2.0% did not significantly increase the ductility or flexural toughness of the UHPC beams.

is paper describes an experimental study on the mechanical properties of high-strength fiber-reinforced concrete (HSFRC). e experimental parameters included the content and length of the steel fiber as well as the use of either a single-type fiber or hybrid steel fibers. e steel fiber contents were 1.0, 1.5, and 2.0% based on the volume of HSFRC, and the steel fiber lengths were 13, 16.5, and 19.5 mm. In addition, hybrid steel fibers incorporating steel fibers of different lengths were used. Compression tests and crack mouth opening displacement tests were performed for each HSFRC mixture with different experimental parameters. e mechanical properties of the HSFRC, such as compressive strength, elastic modulus, and tensile strength, increased with the steel fiber content. e mechanical property results of the HSFRC mixture using a single fiber length of 13 mm were greater than the results of the other mixtures. e compressive strength, elastic modulus, and tensile strength of the HSFRC mixture with hybrid steel fibers were similar to those of the mixtures with a single length of steel fiber. Additionally, based on the test results of the material properties, equations for predicting the elastic modulus and tensile strength of the HSFRC were suggested; the predictions using the proposed formula closely agreed with the experimental results.

The flexural responses of high-strength fiber-reinforced concrete (HSFRC) beams and high-strength concrete (HSC) beams are compared in this study. A series of HSFRC and HSC beams were tested under pure flexural loading. The effects of the type of concrete, compressive strength of the concrete, and tensile rebar ratio on the flexural behavior of the concrete beams were investigated. The flexural behavior of the HSFRC and HSC beams including the induced crack and failure patterns, load and deflection capacity, crack stiffness, ductility index, and flexural toughness was compared. The crack stiffness of the HSC and HSFRC beams increased with the rebar ratio. For the same rebar ratios, the crack stiffness of the HSFRC beams was much greater than that of the HSC beams. The ductility index of the HSC beams decreased sharply with an increase in the rebar ratio, but the ductility index of the HSFRC beams did not show a clear decrease with increasing rebar ratio. The flexural toughness of the HSFRC beams was greater than that of the HSC beams at higher rebar ratios of 1.47% and 1.97%, indicating that the energy absorption of the HSFRC beams was greater than that of the HSC beams. Test results also indicated that HSFRC developed better and more consistent ductility with higher rebar ratio. In addition, the tested bending strength and sectional analysis results were compared.

Crop monitoring is highly important in terms of the efficient and stable performance of tasks such as planting, spraying, and harvesting, and for this reason, several studies are being conducted to develop and improve crop monitoring robots. In addition, the applications of deep learning algorithms are increasing in the development of agricultural robots since deep learning algorithms that use convolutional neural networks have been proven to show outstanding performance in image classification, segmentation, and object detection. However, most of these applications are focused on the development of harvesting robots, and thus, there are only a few studies that improve and develop monitoring robots through the use of deep learning. For this reason, we aimed to develop a real-time robot monitoring system for the generative growth of tomatoes. The presented method detects tomato fruits grown in hydroponic greenhouses using the Faster R-CNN (region-based convolutional neural network). In addition, we sought to select a color model that was robust to external light, and we used hue values to develop an image-based maturity standard for tomato fruits; furthermore, the developed maturity standard was verified through comparison with expert classification. Finally, the number of tomatoes was counted using a centroid-based tracking algorithm. We trained the detection model using an open dataset and tested the whole system in real-time in a hydroponic greenhouse. A total of 53 tomato fruits were used to verify the developed system, and the developed system achieved 88.6% detection accuracy when completely obscured fruits not captured by the camera were included. When excluding obscured fruits, the system’s accuracy was 90.2%. For the maturity classification, we conducted qualitative evaluations with the assistance of experts.