Experimental and modeling investigation of the thermal conductivity of fiber-reinforced soil subjected to freeze-thaw cycles

The thermal conductivity of fine-grained soil, both unreinforced and reinforced with randomly oriented basalt, glass, and steel fibers, was tested by means of the transient hot-wire method with a Quickline-30 Thermal Properties Analyzer. The thermal conductivities of specimens were determined as a function of fiber volume fractions, freeze-thaw cycles, and temperature through laboratory studies. Thermal conductivity of the fiber-reinforced soil decreased for all freeze-thaw cycles and temperature values. The most remarkable reduction of thermal conductivity was measured on all ratios of the steel fiber-reinforced soil and 1% basalt fiber-reinforced soil. Moreover, the statistical-physical model proposed by Usowicz was applied to evaluate the thermal conductivity of fiber-reinforced soil by considering soil-fiber composites and environmental factors. The results showed a close match between the values estimated by the statistical-physical model and the experimental values for various fiber-reinforced soils in a wide range of fiber ratios, temperatures, water contents, and freeze-thaw cycles

Dynamic behavior of fiber-reinforced soil under freeze-thaw cycles

This research presents the dynamic behavior of fiber-reinforced soil exposed to freeze-thaw cycles. The series of dynamic triaxial tests were conducted on fine-grained soil mixed with different percentages of basalt and glass fibers subjected to freeze-thaw cycles. The results showed that after freeze-thaw cycles, with the addition of basalt and glass fibers, the damping ratio and the shear modulus increased at a constant confining pressure because of the increase of stiffness, but the shear modulus decreased with increasing shear strain. Moreover, the theoretical analytical formulations were developed to define for dynamic shear stress and dynamic shear modulus. The parameters were predicted by Hardin-Drnevich model and Kondner-Zelasko model. The shear modulus was expressed as a function of freeze-thaw cycles, fiber contents, confining pressure and initial water content. Finally, ten coefficients were calibrated by analyzing the experimental results and then employed to describe dynamic shear modulus of the fiber-reinforced soil

Ice needles weave patterns of stones in freezing landscapes

Self-organization is increasingly recognized as fundamental to pattern formation in geomorphology. Relative to other fields, however, underlying mechanisms have received little attention from theoreticians. Here, we introduce phase separation theory to study the formation of sorted patterned ground in cold regions; textquotedblleftsortedtextquotedblright refers to the segregation of soil and stones due to feedbacks between stone concentration and recurring ice growth. Using detailed measurements of the concentration of stones in soil and their displacements, we demonstrate that phase separation accounts for the observed sorting and patterns. Our study highlights phase separation theory as a source of important insight into studying ground patterns in cold regions and their potential value in signaling important changes in ground conditions with the warming climate.Patterned ground, defined by the segregation of stones in soil according to size, is one of the most strikingly self-organized characteristics of polar and high-alpine landscapes. The presence of such patterns on Mars has been proposed as evidence for the past presence of surface liquid water. Despite their ubiquity, the dearth of quantitative field data on the patterns and their slow dynamics have hindered fundamental understanding of the pattern formation mechanisms. Here, we use laboratory experiments to show that stone transport is strongly dependent on local stone concentration and the height of ice needles, leading effectively to pattern formation driven by needle ice activity. Through numerical simulations, theory, and experiments, we show that the nonlinear amplification of long wavelength instabilities leads to self-similar dynamics that resemble phase separation patterns in binary alloys, characterized by scaling laws and spatial structure formation. Our results illustrate insights to be gained into patterns in landscapes by viewing the pattern formation through the lens of phase separation. Moreover, they may help interpret spatial structures that arise on diverse planetary landscapes, including ground patterns recently examined using the rover Curiosity on Mars.The experimental data analyzed during this study are available in the manuscript and SI Appendix files. All custom-made simulation codes are available online at GitHub: https://github.com/liuqx315/Phase-separation-patterned-ground. All other study data are included in the article and/or supporting information

Retrogressive thaw slumps along the Qinghai-Tibet Engineering Corridor: a comprehensive inventory and their distribution characteristics

The important Qinghai&ndash;Tibet Engineering Corridor (QTEC) covers the part of the Highway and Railway underlain by permafrost. The permafrost on the QTEC is sensitive to climate warming and human disturbance and suffers accelerating degradation. Retrogressive thaw slumps (RTSs) are slope failures due to the thawing of ice-rich permafrost. They typically retreat and expand at high rates, damaging infrastructure, and releasing carbon preserved in frozen ground. Along the critical and essential corridor, RTSs are commonly distributed but remain poorly investigated. To compile the first comprehensive inventory of RTSs, this study uses an iteratively semi-automatic method built on deep learning to delineate thaw slumps in the 2019 PlanetScope CubeSat images over a &sim;&thinsp;54&thinsp;000&thinsp;km2 corridor area. The method effectively assesses every image pixel using DeepLabv3+ with limited training samples and manually inspects the deep-learning-identified thaw slumps based on their geomorphic features and temporal changes. The inventory includes 875 RTSs, of which 474 are clustered in the Beiluhe region, and 38 are near roads or railway lines. The dataset is available at https://doi.org/10.5281/zenodo.6397029​​​​​​​ (Xia et al., 2021a), with the Chinese version at DOI: https://doi.org/10.11888/Cryos.tpdc.272672 (Xia et al.&nbsp;2021b). These RTSs tend to be located on north-facing slopes with gradients of 1.2&ndash;18.1∘ and distributed at medium elevations ranging from 4511 to 5212&thinsp;m&thinsp;a.s.l. They prefer to develop on land receiving relatively low annual solar radiation (from 2900 to 3200 &thinsp;kWh&thinsp;m&minus;2), alpine meadow covered, and loam underlay. Our results provide a significant and fundamental benchmark dataset for quantifying thaw slump changes in this vulnerable region undergoing strong climatic warming and extensive human activities. &nbsp;</p

Permafrost Distribution along the Qinghai-Tibet Engineering Corridor, China Using High-Resolution Statistical Mapping and Modeling Integrated with Remote Sensing and GIS

Permafrost distribution in the Qinghai-Tibet Engineering Corridor (QTEC) is of growing interest due to the increase in infrastructure development in this remote area. Empirical models of mountain permafrost distribution have been established based on field sampled data, as a tool for regional-scale assessments of its distribution. This kind of model approach has never been applied for a large portion of this engineering corridor. In the present study, this methodology is applied to map permafrost distribution throughout the QTEC. After spatial modelling of the mean annual air temperature distribution from MODIS-LST and DEM, using high-resolution satellite image to interpret land surface type, a permafrost probability index was obtained. The evaluation results indicate that the model has an acceptable performance. Conditions highly favorable to permafrost presence (≥70%) are predicted for 60.3% of the study area, declaring a discontinuous permafrost distribution in the QTEC. This map is useful for the infrastructure development along the QTEC. In the future, local ground-truth observations will be required to confirm permafrost presence in favorable areas and to monitor permafrost evolution under the influence of climate change

Spatial Analyses and Susceptibility Modeling of Thermokarst Lakes in Permafrost Landscapes along the Qinghai–Tibet Engineering Corridor

Thermokarst lakes (TLs) caused by the thaw of massive ground ice in ice-rich permafrost landscapes are increasing and have strong impacts on the hydro–ecological environment and human infrastructure on the Qinghai–Tibet Plateau (QTP), however, its spatial distribution characteristics and environmental controls have not been underrepresented at the local scale. Here, we analyzed the spatial distribution of small TLs along the Qinghai–Tibet Engineering Corridor (QTEC) based on high-resolution (up to 2.0 m) satellite images. The TLs gathered in the plains and upland plateau and covered 8.3% of the QTEC land. We deployed a random-frost method to investigate the suitable environmental conditions for TLs. Climate including summer rainfall and the air temperature was the most important factor controlling the TL distribution, followed by topography and soil characteristics that affected the ground ice content. TL susceptibility was mapped based on the combinations of climate, soil, and topography grid data. On average, around 20% of the QTEC area was in a high to very-high-susceptibility zone that is likely to develop TLs in response to climate change. This study improved the understanding of controlling factors for TL development but also provided insights into the conditions of massive ground ice and was helpful to assess the impacts of climate change on ecosystem processes and engineering design

sEMG-Based Neural Network Prediction Model Selection of Gesture Fatigue and Dataset Optimization

The fatigue energy consumption of independent gestures can be obtained by calculating the power spectrum of surface electromyography (sEMG) signals. The existing research studies focus on the fatigue of independent gestures, while the research studies on integrated gestures are few. However, the actual gesture operation mode is usually integrated by multiple independent gestures, so the fatigue degree of integrated gestures can be predicted by training neural network of independent gestures. Three natural gestures including browsing information, playing games, and typing are divided into nine independent gestures in this paper, and the predicted model is established and trained by calculating the energy consumption of independent gestures. The artificial neural networks (ANNs) including backpropagation (BP) neural network, recurrent neural network (RNN), and long short-term memory (LSTM) are used to predict the fatigue of gesture. The support vector machine (SVM) is used to assist verification. Mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) are utilized to evaluate the optimal prediction model. Furthermore, the different datasets of the processed sEMG signal and its decomposed wavelet coefficients are trained, respectively, and the changes of error functions of them are compared. The experimental results show that LSTM model is more suitable for gesture fatigue prediction. The processed sEMG signals are appropriate for using as the training set the fatigue degree of one-handed gesture. It is better to use wavelet decomposition coefficients as datasets to predict the high-dimensional sEMG signals of two-handed gestures. The experimental results can be applied to predict the fatigue degree of complex human-machine interactive gestures, help to avoid unreasonable gestures, and improve the user’s interactive experience

Failure diagnosis of the steam generator based on improved BP network and Particle

In order to overcome the problems of slow rate of convergence and falling easily into local minimum in BP algorithm, this paper introduces the adaptive particle swarm optimization algorithm and combining model. The paper applies it to steam turbine-generators fault diagnosis. The experiment data shows that the algorithm converges quickly and recognizes faults efficiently; it has a reference value for faults diagnosis

Rehabilitation Effect Evaluation of CFRP-Lined Prestressed Concrete Cylinder Pipe under Combined Loads Using Numerical Simulation

Prestressed concrete cylinder pipe (PCCP) has been widely used for water transfer and transit projects. However, prestressing wire breaks may result in the rupture of pipes and cause catastrophes. Carbon fiber reinforced polymer (CFRP) liners adhered to the inner concrete core can provide an effective method of internal repair and strengthening of PCCP. To evaluate the rehabilitation effect of CFRP-lined PCCP under combined loads, two contrasting three-dimensional finite element models that investigated the visual cracking of concrete and the yielding of steel cylinders were developed. A conceptual zone was introduced to analyze the different states of the pipe during the phase of wire break. In particular, the complex CFRP-concrete bonded interface was simulated by a cohesive element layer with a bilinear traction-separation response. The results show that CFRP has a good rehabilitation effect on the inner concrete core and steel cylinder but only a slight effect on the outer concrete core, prestressing wire, or mortar. A one-hoop CFRP layer diminishes the area of a yielding steel cylinder of 4.72 m2. In addition, CFRP works more effectively along with an increase in the number of broken wires. This research can provide a basis for strengthening distressed PCCP pipelines