Characterizing Tensile Strength of Building Sandstone via Uniaxial Tensile, Compressive, and Flexural Bending Tests.

Sandstone is widely used a construction and building material. However, its uniaxial tensile strength (UTS) is not adequately understood. To characterize the uniaxial tensile strength of natural sandstone, three groups of specimens were fabricated for four-point bending, uniaxial compressive, and tensile tests. To characterize the evolution of the stress-strain profiles obtained via these tests, representative expressions were developed in terms of normalized strain and strength. The magnitude of the uniaxial tensile strength exceeded that of the four-point bending strength, indicating that the uniaxial tensile strength cannot be represented by the four-point bending strength. The experimental ratio of uniaxial tensile and compression strength (33-41) was underestimated by the empirical expressions reported in the literature. The suggested correction coefficient for the FBS is 0.25. The compressive modulus (Ec) was generally identical to the experimental results published in the literature, whereas the tensile modulus (Et) was overestimated. The experimental modular ratio, Et/Ec, ranged from 0.12 to 0.14; it was not sensitive to Poisson's ratio, but it increased slightly with the compressive modulus. This work can serve as a reference for computing the load-bearing capacity of sandstone components under tension.

Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting.

Advancements in deep learning and computer vision have led to the discovery of numerous effective solutions to challenging problems in the field of agricultural automation. With the aim to improve the detection precision in the autonomous harvesting process of green asparagus, in this article, we proposed the DA-Mask RCNN model, which utilizes the depth information in the region proposal network. Firstly, the deep residual network and feature pyramid network were combined to form the backbone network. Secondly, the DA-Mask RCNN model added a depth filter to aid the softmax function in anchor classification. Afterwards, the region proposals were further processed by the detection head unit. The training and test images were mainly acquired from different regions in the basin of the Yangtze River. During the capturing process, various weather and illumination conditions were taken into account, including sunny weather, sunny but overshadowed conditions, cloudy weather, and daytime greenhouse conditions as well as nighttime greenhouse conditions. Performance experiments, comparison experiments, and ablation experiments were carried out using the five constructed datasets to verify the effectiveness of the proposed model. Precision, recall, and F1-score values were applied to evaluate the performances of different approaches. The overall experimental results demonstrate that the balance of the precision and speed of the proposed DA-Mask RCNN model outperform those of existing algorithms.