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By combining time-frequency images and deep learning models, the nonlinear ultrasound signals can be classified, detected, and predicted, using the nonlinear coefficient as a fundamental label for training deep learning models. This integrated approach enables quantitative identification and real-time monitoring of concrete damage, promoting the widespread adoption of nonlinear ultrasonic techniques in engineering applications. As a basis, the relationship between damage variations and nonlinear coefficients is discussed by performing nonlinear ultrasonic damage testing on concrete specimens with different crack lengths and angles. The testing signals are converted into time-frequency images using the short-time Fourier transform and the continuous wavelet transform, and both types of images are combined for data augmentation and input into the deep learning model for training, with nonlinear coefficients serving as labels for the time-frequency images. The MobileNetV2, VGG16, and ResNet18 deep learning models are trained separately on time-frequency image datasets for the length specimens, the angle specimens, and the length-angle specimens, and the performance of the different models is evaluated and compared. The results show that all three models have accuracy rates above 94%, indicating good identification performance. Finally, with the example, the nonlinear coefficients of the testing signals are compared with the labels of the nonlinear coefficients in the time-frequency images identified by the deep learning model, which confirms the high accuracy of damage identification by the deep learning model. [ABSTRACT FROM AUTHOR]

A nondestructive technique is lacking for the quick and accurate detection of wetting deformation in field measurements. In this study, we investigated the feasibility of using resistivity to assess wetting deformation. A modified triaxial apparatus was used to conduct wetting tests on a soil−rock mixture (SRM), with simultaneous measurement of resistivity and deformation. The results indicate that resistivity gradually decreases during consolidation and that the magnitude of resistivity changes is affected by the rock content and confining pressure. The resistivity values exhibited a gradual decrease during shearing, with the magnitude of this decrease showing a negative correlation with the rock content. In addition, there was a positive correlation between the rate of deformation and the rate of change of the resistivity. The resistivity in the wetting tests was divided into three distinct stages: prewetting, during wetting, and postwetting. Throughout these stages, a consistent decrease in resistivity was observed. The inflection points observed in the resistivity–axial strain curve can be used to determine the beginning and end of wetting. A mathematical model based on the exponential function is proposed for assessing wetting deformation via resistivity measurements. The experimental results verify the feasibility of real-time monitoring and evaluation of wetting deformation using resistivity. [ABSTRACT FROM AUTHOR]

Dioecious plants are often used in landscaping because of sex differences in individual appearance, resistance and ornamental value. Although a large number of studies have investigated the overall differences in resource allocation between different sexes in dioecious plants, the effects of environmental factors such as temperature and precipitation on resource allocation in sex-dimorphic plants are not fully understood. In order to explore the influencing factors, 39 works of literature on dioecious plant resource allocation published in English databases from 1992 to 2023 were selected, and the biomass data of dioecious plant stems, leaves, flowers and fruits from these pieces of literature were extracted. A total of 545 independent experimental groups were obtained, which were divided into four groups for analysis, and the data were analyzed using METAWIN 2.1 software. Four sets of data were used to quantitatively study the effects of different temperatures, precipitations and life forms on the resource allocation of dioecious plants of different sexes in large-scale space. The results showed that female plants invested more resources in reproductive growth and less resources in vegetative growth. In terms of total biomass, the average biomass of female plants was 3.09% higher than that of male plants, indicating that female plants reduced nutrient investment to compensate for reproductive investment in the process of resource allocation. Temperature and precipitation significantly affect the adaptability of male and female plants to environmental changes and the cooperative relationships among the stressed components. The vegetative biomass investment of female plants showed a positive correlation with the increase in temperature, while the reproductive biomass showed a negative correlation with the decrease. The average annual precipitation had little effect on the vegetative biomass of dioecious plants, but had a significant effect on reproductive biomass. The study of this trade-off relationship is helpful in revealing the relationship between vegetative growth and reproductive growth of plants, exploring the countermeasures of plant life history, and providing a scientific basis for urban landscaping and urban forest management. [ABSTRACT FROM AUTHOR]