The in-situ stress can significant influence the damage caused to rock. A comprehensive analysis of the in-situ stress field is essential for tunnel design, construction and geological monitoring. This study establishes a 3D geologic model using the finite difference method, explicit considering material heterogeneity through random field theory. After conducting 300 simulations, the distribution pattern of the in-situ stress field was statistically analyzed. The inversion accuracy, considering material heterogeneity, is superior to that for homogeneous materials at the measurement points, with smaller relative errors. The extent of in-situ stresses in both the horizontal and vertical directions of the model depend not only the burial depth but also on the physico-mechanical properties of the material. In particular, the distribution of the in-situ stress field exhibits heterogeneity in localized regions, influenced by the material's variability. In the river valley area, the river valley bank slopes are divided into three zones based on the stress force values: the stress release zone, the stress concentration zone, and the virgin rock stress zone. The stress distribution around the tunnel shows significant non-uniformity and irregular fluctuations, with alternating high-stress and low-stress regions. Notably, stress concentration occurs at the crown, sidewalls, and both sides of the tunnel bottom. These in-situ stress fields, which account for the spatial variability of rock parameters, provide a more realistic and accurate reference for engineering practice.

This study investigated the mechanical properties of a low-plasticity clay soil reinforced with polypropylene (PP) fiber in various contents (0.05%, 0.10%, 0.15%, and 0.20%) and lengths (6, 12, and 19 mm). The reinforced specimens were subjected to unconsolidated-undrained (UU) triaxial compression tests under three different confining pressures (50, 100, and 200 kPa). The optimum fiber contents in specimens reinforced with 6-, 12-, and 19-mm PP fiber were determined as 0.15%, 0.15%, and 0.20%, respectively. As a result, the highest values regarding deviator stress at failure (sigma dev), energy absorption capacity (EAC), and shear strength parameters occurred in specimens containing 0.20% PP (19 mm). As a result of the reinforcement process, the most remarkable improvements in the sigma dev, cohesion, internal friction angle, and EAC values of the natural soil are 59.95%, 21.80%, 63%, and 34.70%, respectively. Linear and nonlinear relationships between sigma dev and fiber length, fiber content, and confining pressure were investigated by multiple linear regression and artificial neural network methods. Equations were generated to predict sigma dev of a low-plasticity clay soil reinforced with PP fiber and were made available to geotechnical researchers.

The energy absorption capacity (EAC) of earthen materials significantly influences the safety of civil projects. Furthermore, the development of machine learning techniques, including Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models, entails financial and non-financial benefits by reducing the need for performing expensive, exhausting and time-consuming laboratory tests. This study investigates the EAC of sandy soil reinforced by three different forms of processed lignocellulosic fiber pulps. The studied influence parameters included fiber type, curing time, effective confining pressure, and fiber content. Artificial neural network (ANN) models were developed to assess the EAC of the reinforced specimens and evaluate the impact of studied parameters. The analysis of each fiber type was carried out using Multiple Linear Regression (MLR) methods. The specimens, subjected to a 7-day curing period and reinforced with 2% of lignocellulosic fibers of 1.5 mm in length, exhibited the greatest EAC values. Sensitivity analysis identified effective confining pressure as the most influential factor on the EAC of the reinforced specimens. This study demonstrates the advantageous impact of processed lignocellulosic fibers, which are environmentally harmless substances, in enhancing the EAC of sandy soil and its ductility response. As a result, this decreases the likelihood of unexpected and catastrophic failures. This research also demonstrates the high capability of ANN-based models in predicting EAC at various influence parameters.

Saffron ( Crocus sativus : Iridaceae) is a fall-blooming perennial plant and its dried stigma is the priciest spice and a key non-oil export for Iran's economy. The bulb mite, Rhizoglyphus robini , is a polyandrous and multivoltine species and its damage to saffron corms directly and indirectly causes lower yields of saffron crops. Environmental conditions and abiotic factors, such as temperature, humidity, density, and diet affect the morphological traits of living organisms and subsequently affect biological abilities. In this study, changes in temperature, soil moisture, density (nymphs + adults), time, and corm weight on the morphological traits of the saffron bulb mite, including body length and width, and leg sizes of adult females were investigated in a saffron field in the Dargaz County of Iran during 2022. The results of variance analysis of the morphometrical parameters of the mite species, including body length, body width, and four pairs of legs in different months were significant. Based on simple and multiple linear regression models as well as non-linear regression, the effect of temperature and density (nymphs + adults) was reversed and the effect of soil moisture and corm weight was direct on morphometrical parameters of this species. Based on our results, soil moisture has a strong relation with female body size traits (body length, width, and leg lengths). This indicates that irrigation cycle management might be an important factor in bulb mite management in saffron agroecosystems.

Urbanization and agricultural land use have led to water quality deterioration. Studies have been conducted on the relationship between landscape patterns and river water quality; however, the Wuding River Basin (WDRB), which is a complex ecosystem structure, is facing resource problems in river basins. Thus, the multi-scale effects of landscape patterns on river water quality in the WDRB must be quantified. This study explored the spatial and seasonal effects of land use distribution on river water quality. Using the data of 22 samples and land use images from the WDRB for 2022, we quantitatively described the correlation between river water quality and land use at spatial and seasonal scales. Stepwise multiple linear regression (SMLR) and redundancy analyses (RDA) were used to quantitatively screen and compare the relationships between land use structure, landscape patterns, and water quality at different spatial scales. The results showed that the sub-watershed scale is the best spatial scale model that explains the relationship between land use and water quality. With the gradual narrowing of the spatial scale range, cultivated land, grassland, and construction land had strong water quality interpretation abilities. The influence of land use type on water quality parameter variables was more distinct in rainy season than in the dry season. Therefore, in the layout of watershed management, reasonably adjusting the proportion relationship of vegetation and artificial building land in the sub-basin scale and basin scope can realize the effective control of water quality optimization.

We study the statistical relations between the black carbon (BC) content in the atmospheric column and the surface albedo (A), the values of which are available from MERRA-2 reanalysis data for four test areas near the Arctic coast of Russia in April 2010-2016. We also analyze the atmospheric meteorological parameters: air temperature and rainfall and snowfall amounts. The statistical analysis has been carried out using diurnally averaged parameters. An increase in the air temperature is accompanied everywhere by a decrease in the surface albedo, both on a monthly scale and in daily variations. Precipitation in the form of fresh snow increases the surface albedo. On the whole over 7 years, a significant negative correlation between BC andAin April was found in Nenets Autonomous okrug and on the Gydan Peninsula. Separate years (generally diverse for different areas) are revealed when day-to-day variations inAand BC correlate within a month, again with negative coefficients. We estimated possible albedo variations due to changes in different parameters, as well as variations in albedo radiative forcing.