Hasil untuk "Engineering geology. Rock mechanics. Soil mechanics. Underground construction"

Cecilia Hernández-Ramírez, Jesusita Rosas-Diaz, Mario J. Romellón-Cerino et al.

Kamonrat Khontiang, Daojarus Ketrot, Saowanuch Tawornpruek et al.

Potassium (K) is essential for optimizing sugarcane production, playing a critical role in various processes that influence yield and quality. However, the effectiveness of different K forms in enhancing sugarcane productivity through foliar application remains underexplored, leaving a significant knowledge gap. This study investigates the impact of various foliar K supplements under differing soil K conditions, hypothesizing that such supplementation will enhance yield and nutrient uptake in ratoon sugarcane. Field trials were conducted on first ratoon sugarcane in loamy soil, using a 2 × 7 factorial in a randomized complete block design. The first factor compared no soil-applied K with soil-applied K, while the second factor consisted of foliar K treatments: water (control), 2.5% weight by volume of KCl, K₂SO₄, K₂SiO₃, KNO₃, diluted molasses, and vinasse at a 5× dilution. Results indicated that foliar supplementation with KNO₃ and K₂SiO₃ (without soil-applied K) effectively maintained ratoon sugarcane yield and sugar yield, comparable to yields achieved with soil-applied K combined with foliar water. Foliar K supplementation also improved the uptake of N, P, K, and Si in cane stalks, matching or exceeding uptake levels observed in ratoon sugarcane with soil-applied K. Although no yield enhancement was observed with the combination of foliar K supplementation and soil-applied K, most foliar K treatments increased K uptake even with adequate soil K levels. In conclusion, foliar K supplementation, particularly with KNO₃ and K₂SiO₃, is an effective strategy for maintaining sugarcane productivity, and improving nutrient use efficiency, especially when K fertilizer is unavailable or costly.

Korolev, Yury P., Korolev, Pavel Yu.

The aim of the study was to confirm the possibility of forecasting tsunamis of non-seismic (volcanic) origin using the express method of operational forecasting. The surface wave formed as a result of the explosive volcanic eruption on January 15, 2022 was a superposition of forced (baric) waves caused by an atmospheric pressure wave and free (gravity) waves generated by the disintergration of the disturbance in the source. The express method of operational tsunami forecasting was used to compute the gravitational component of the surface wave. The method allows one to compute the tsunami waveform at any point in the ocean and near the coast in real time based on the data from the sea level measurement stations. The computation of the tsunami on 15.01.2022, its gravitational component, at the DART stations remote from the source was performed based on the data from the DART stations 51425 and 52406 closest to the volcano. For an adequate forecast, the information on the tsunami of the DART stations closest to the source with the duration of a quarter of the first period is sufficient, which is especially important in the operational mode. The result satisfies the definition of the concept of "tsunami forecast" formulated by the Intergovernmental Oceanographic Commission of UNESCO. It has been confirmed that the express method can provide a tsunami forecast regardless of the mechanism of its excitation. It remains unclear how adequate the assessment of the amplitude of surface waves is based on the bottom pressure data is.

MU Linlong 1, 2 , WANG Chao 1, 2, WANG Le 3, CAO Jie 4, ZHANG Peiyun 1, 2

The volumetric deformation and particle breakage of crushed stone soil under high stress will alter its permeability. To investigate the changes in the permeability of crushed stone materials before and after particle breakge under confined pressure, a large-scale permeameter with an independently controllable axial loading system is designed to conduct seepage tests on the crushed stone soil under pressures. The permeability characteristics of crushed stone soil under confined pressure are obtained, and the relationships are established among permeability characteristics, pore ratio, gradation and axial pressure. The results indicate that as the axial pressure increases, particle breakage intensifies, leading to an increase in the content of fine particles and a decrease in the porosity, resulting in a significant reduction in seepage velocity. When the crushed stone soil experiences seepage within a small range of hydraulic gradients, there is a generally linear relationship between hydraulic gradient and flow velocity. The stress state has a significant impact on the permeability characteristics of the crushed stone soil, with a notable decrease in the permeability as the axial pressure increases.

Reza Taherdangkoo, Mostafa Mollaali, Matthias Ehrhardt et al.

The hydro-mechanical behavior of clay-sulfate rocks, especially their swelling properties, poses significant challenges in geotechnical engineering. This study presents a hybrid constrained machine learning (ML) model developed using the categorical boosting algorithm (CatBoost) tuned with a Bayesian optimization algorithm to predict and analyze the swelling behavior of these complex geological materials. Initially, a coupled hydro-mechanical model based on the Richards' equation coupled to a deformation process with linear kinematics implemented within the finite element framework OpenGeoSys was used to simulate the observed ground heave in Staufen, Germany, caused by water inflow into the clay-sulfate bearing Triassic Grabfeld Formation. A systematic parametric analysis using Gaussian distributions of key parameters, including Young's modulus, Poisson's ratio, maximum swelling pressure, permeability, and air entry pressure, was performed to construct a synthetic database. The ML model takes time, spatial coordinates, and these parameter values as inputs, while water saturation, porosity, and vertical displacement are outputs. In addition, penalty terms were incorporated into the CatBoost objective function to enforce physically meaningful predictions. Results show that the hybrid approach effectively captures the nonlinear and dynamic interactions that govern hydro-mechanical processes. The study demonstrates the ability of the model to predict the swelling behavior of clay-sulfate rocks, providing a robust tool for risk assessment and management in affected regions. The results highlight the potential of ML-driven models to address complex geotechnical challenges.

Stephen Whitelam, Corneel Casert, Megan Engel et al.

We present a set of computer codes designed to test methods for optimizing time-dependent control protocols in fluctuating nonequilibrium systems. Each problem consists of a stochastic model, an optimization objective, and C++ and Python implementations that can be run on Unix-like systems. These benchmark systems are simple enough to run on a laptop, but challenging enough to test the capabilities of modern optimization methods. This release includes five problems and a worked example. The problem set is called NESTbench25, for NonEquilibrium STatistical mechanics benchmarks (2025).

Roberto Verdecchia, Justus Bogner

From its first adoption in the late 80s, qualitative research has slowly but steadily made a name for itself in what was, and perhaps still is, the predominantly quantitative software engineering (SE) research landscape. As part of our regular column on empirical software engineering (ACM SIGSOFT SEN-ESE), we reflect on the state of qualitative SE research with a focus group of experts. Among other things, we discuss why qualitative SE research is important, how it evolved over time, common impediments faced while practicing it today, and what the future of qualitative SE research might look like. Joining the conversation are Rashina Hoda (Monash University, Australia), Carolyn Seaman (University of Maryland, United States), and Klaas Stol (University College Cork, Ireland). The content of this paper is a faithful account of our conversation from October 25, 2025, which we moderated and edited for our column.

Md. Naimur Rahman, Shafak Shahriar Sozol, Md. Samsuzzaman et al.

In the modern agricultural industry, technology plays a crucial role in the advancement of cultivation. To increase crop productivity, soil require some specific characteristics. For watermelon cultivation, soil needs to be sandy and of high temperature with proper irrigation. This research aims to design and implement an intelligent IoT-based soil characterization system for the watermelon field to measure the soil characteristics. IoT based developed system measures moisture, temperature, and pH of soil using different sensors, and the sensor data is uploaded to the cloud via Arduino and Raspberry Pi, from where users can obtain the data using mobile application and webpage developed for this system. To ensure the precision of the framework, this study includes the comparison between the readings of the soil parameters by the existing field soil meters, the values obtained from the sensors integrated IoT system, and data obtained from soil science laboratory. Excessive salinity in soil affects the watermelon yield. This paper proposes a model for the measurement of soil salinity based on soil resistivity. It establishes a relationship between soil salinity and soil resistivity from the data obtained in the laboratory using artificial neural network (ANN).

Ebube Alor, Ahmad Abdellatif, SayedHassan Khatoonabadi et al.

Software engineering (SE) chatbots are increasingly gaining attention for their role in enhancing development processes. At the core of chatbots are Natural Language Understanding platforms (NLUs), which enable them to comprehend user queries but require labeled data for training. However, acquiring such labeled data for SE chatbots is challenging due to the scarcity of high-quality datasets, as training requires specialized vocabulary and phrases not found in typical language datasets. Consequently, developers often resort to manually annotating user queries -- a time-consuming and resource-intensive process. Previous approaches require human intervention to generate rules, called labeling functions (LFs), that categorize queries based on specific patterns. To address this issue, we propose an approach to automatically generate LFs by extracting patterns from labeled user queries. We evaluate our approach on four SE datasets and measure performance improvement from training NLUs on queries labeled by the generated LFs. The generated LFs effectively label data with AUC scores up to 85.3% and NLU performance improvements up to 27.2%. Furthermore, our results show that the number of LFs affects labeling performance. We believe that our approach can save time and resources in labeling users' queries, allowing practitioners to focus on core chatbot functionalities rather than manually labeling queries.

Zhong Zhou, Junjie Zhang, Chenjie Gong et al.

Aiming at solving the challenges of insufficient data samples and low detection efficiency in tunnel lining crack detection methods based on deep learning, a novel detection approach for tunnel lining crack was proposed, which is based on pruned You Look Only Once v4 (YOLOv4) and Wasserstein Generative Adversarial Network enhanced by Residual Block and Efficient Channel Attention Module (WGAN-RE). In this study, a data augmentation method named WGAN-RE was proposed, which can achieve the automatic generation of crack images to enrich data set. Furthermore, YOLOv4 was selected as the basic model for training, and a pruning algorithm was introduced to lighten the model size, thereby effectively improving the detection speed. Average Precision (AP), F1 Score (F1), model size, and Frames Per Second (FPS) were selected as evaluation indexes of the model performance. Results indicate that the storage space of the pruned YOLOv4 model is only 49.16 MB, which is 80% compressed compared with the model before pruning. In addition, the FPS of the model reaches 40.58f/s, which provides a basis for the real-time detection of tunnel lining cracks. Findings also demonstrate that the F1 score and AP of the pruned YOLOv4 are only 0.77% and 0.50% lower than that before pruning, respectively. Besides, the pruned YOLOv4 is superior in both model accuracy and efficiency compared with YOLOv3, SSD, and Faster RCNN, which indicated that the pruned YOLOv4 model can realize the accurate, fast and intelligent detection of tunnel lining cracks in practical tunnel engineering.

Lihui Li, Chenglong Li, Beixiu Huang et al.

Reef limestone is a biogenic sedimentary rock widely distributed in coral reef areas, acting as an important foundation for coast construction. Due to its special biogenic origin, reef limestone is different from conventional rocks both in terms of rock structure and mechanical properties. In this study, mesoscale uniaxial compression experiments with five different loading directions were conducted on two kinds of reef limestones from the Maldives Islands and the South China Sea, respectively. The real-time high-resolution videos and images of failure processes were recorded simultaneously to investigate the fracture evolution and fracture surface roughness of reef limestones. It demonstrated that the reef limestones belonged to extremely soft to soft rocks, and their uniaxial compressive strength (UCS) values fluctuated with high discreteness. The mesoscale mechanical properties of reef limestones were highly anisotropic and mainly controlled by pore structure. The occurrence of dissolution pores in reef limestone tended to intensify mechanical anisotropy. With the integration of the fracture initiation and propagation features of reef limestones, it is supposed that the intrinsic mechanism of anisotropy was probably attributed to the differences in coral growth direction and dissolution. Furthermore, the quantified fracture surface roughness was revealed to have a good consistency with density and UCS for the reef limestones from the South China Sea. The findings are helpful for providing theoretical and experimental references for engineering construction in coral reef areas.

Hamzeh Noor, Mahmood Arabkhedri

IntroductionSoil erosion by water is one of the most common environmental problems worldwide and is considered a serious risk for sustainability in developing countries. Water erosion on a global scale is one of the most critical types of soil and environmental degradation due to its geographical extent and ecological effects. In this regard, effectively controlling sediment load is an important component in watershed management. In the formulation of a watershed management strategy, the estimation of sediment delivery ratio (SDR) plays a significant role. SDR is defined as the sediment yield from an area divided by the gross erosion of that same area. SDR is expressed as a percentage and represents the efficiency of the watershed in moving soil particles from areas of erosion to the point where sediment yield is measured. One of the problems in estimating the SDR in watersheds is the lack of proper information on the amount of soil erosion and sediment yield. In this context, the Sanganeh soil conservation research station, having measured soil erosion and sediment yield of small watersheds, is a suitable place to evaluate the accuracy of the RUSLE model and estimate the ratio of sediment delivery on the scale of small watersheds. The current research aims to achieve two goals: a) determining the accuracy of the RUSLE model in estimating soil erosion based on the measurements in the erosion plots, and b) estimating the SDR using the estimated soil erosion values as well as the sediment yield measured at the outlet small watershed are planned. Materials and MethodsConsidering the importance of soil erosion and the study of sediment processes in semi-arid rangeland ecosystems, the Khorasan Razavi Agricultural and Natural Resources Research Center (KANRRC) assessed some micro-watersheds for the collection of storm-wise runoff and associated sediment. The Sanganeh research micro-watershed, located 100 km from Mashhad City (northeast Iran), is one of the watersheds selected for this study. The watershed area, the longest waterways, and the mean slope of the watershed are 1.2 ha, 145.0 m, and 31.2%, respectively. The study watershed consists of semi-arid rangeland dominated by Bromus tectorum and Artemisia diffusa, with a coverage of 50%. The soil is Entisol and Aridosol, young, with a maximum depth of 30 cm. The mean electrical soil conductivity (EC), soil organic matter (OM), clay, sand, silt, and surface rock fragments of soils are 1.81, 1.57, 10.6, 54.7, 34.7, and 5%, respectively. In this research, three experimental small watersheds with areas between 4300-12000 m2 were selected along with the erosion plots in them. Then, 24 rainfall events related to two periods of 2006-2009 and 2016-2018 were recorded along with the corresponding data of runoff and sediment in watersheds and plots. In this study, water flow and sediment yield were monitored at the main outlet of the micro-watersheds and plots. The runoff volume was calculated after each storm event by multiplying the depth of collected water, measured using an iron ruler at five points in the tank (corners and central), by the surface area of the collector. The collected runoff and sediment were then mixed thoroughly and one sample was taken to determine sediment concentration and sediment yield. Then, by collecting the required information (includeing rainfall erosivity, topography, conservation practice, soil erodibility, and cover-crop management factors), the RUSLE model was run and compared with the observation data of the plots. The storm-wise soil erosion predictions were compared with observed data based on the criteria of the coefficient of determination (R2) and relative estimation error (RE). In the following, by modifying the RUSLE model and observing the sedimentation data of the studied watersheds, the value of the SDR was estimated. Results and DiscussionAfter collecting the required information, the RUSLE model was implemented at the plot scale. The accuracy of the model was evaluated using erosion plot data, which was not confirmed due to huge overestimations of RUSLE. Next, to achieve more accurate results, regression types (linear, exponential, power, etc.) were used between the observed and estimated values of soil erosion (RUSLE). After applying the correction coefficient, this model was able to estimate the average erosion rate of the whole period are 12, 17, and 2% for E1, E4, and E6 watersheds, respectively, which is within the acceptable range of soil erosion modeling. Therefore, it can be said that the accuracy of the modified RUSLE model (by regression model) in estimating the average soil erosion during the period is higher than the event-based scale. Also, the prediction of maximum event estimation error for E1, E4, and E6 watersheds was 25.7, 35.8, and 21.6%, respectively. After evaluating the accuracy of the RUSLE model at the plot scale and in order to know the amount of soil erosion at the watershed scale, the values ​​of L, S, K, and C factors for the watersheds were calculated based on a weighted average and entered into the modified model. Therefore, the results of the RUSLE model were generalized to the watershed scale. In the final stage, by dividing the amount of erosion by the corresponding amounts of sediment yield measured at the outlet of watersheds, the ratio of sediment delivery was calculated. The average SDR of the entire period in the E1, E4, and E6 watersheds are 42.2, 41.5, and 39.7%, respectively, and in the maximum events, it is one or two percent higher. ConclusionOverall, the results of this research showed that using the modified RUSLE model, it is possible to estimate the average soil erosion in the Sangane soil conservation station and also estimate the SDR. Therefore, this approach can be used in executive programs in similar areas. According to the obtained results, the classification of rainfall data based on the rain erosive factor and then the evaluation of the RUSLE model can provide more accurate results. In addition, in this research, due to the small area of the watersheds, waterway processes did not play a role in the deposition and transfer of eroded soils. It is also suggested that similar research could be done in larger watersheds. Finally, considering the determination of the SDR in this area, it is recommended to evaluate the accuracy of the experimental methods for determining the SDR.

Yury N. Poltev, Tatyana G. Koreneva, Vsevolod E. Maryzhikhin et al.

The content of Fe, As, Cu, Mn, Cr, Ni, Pb and Cd in the muscles of some aquatic organism species from the Sea of Okhotsk waters of Northeastern Sakhalin was estimated: walleye pollack (Gadus chalcogrammus Pallas, 1814), longhead dab (Limanda proboscidea Gilbert, 1896) and Bering flounder (Hippoglossoides robustus Gill & Townsend, 1897), snow crab (Chionoecetes opilio (O.Fabricius, 1788)). The concentrations of Fe and Cu are reliably higher in the snow crab, in contrast to fish, and Pb concentration is higher in fish relative to the snow crab. There was no difference in the content of trace elements between the flounders and snow crab, and in relation to the walleye pollock, the snow crab has reliably higher concentrations of Fe, Cu, and Hg and lower ones of Pb. The content of Fe is higher in the flounders compared to the walleye pollack. The concentrations of Pb, Cd, As and Hg are safe according to the hygienic requirements for food products and may indirectly indicate a favorable environmental situation in terms of the content of regulated toxic elements in the waters of Northeastern Sakhalin.

M. Boström, S. Kuthe, S. Carretero-Palacios et al.

Thin films of ice and water on soil particles play crucial roles in environmental and technological processes. Understanding the fundamental physical mechanisms underlying their formation is essential for advancing scientific knowledge and engineering practices. Herein, we focus on the role of the Casimir-Lifshitz force, also referred to as dispersion force, in the formation and behavior of thin films of ice and water on soil particles at 273.16 K, arising from quantum fluctuations of the electromagnetic field and depending on the dielectric properties of interacting materials. We employ the first-principles density functional theory (DFT) to compute the dielectric functions for two model materials, CaCO$_3$ and Al$_2$O$_3$, essential constituents in various soils. These dielectric functions are used with the Kramers-Kronig relationship and different extrapolations to calculate the frequency-dependent quantities required for determining forces and free energies. Moreover, we assess the accuracy of the optical data based on the DFT to model dispersion forces effectively, such as those between soil particles. Our findings reveal that moisture can accumulate into almost micron-sized water layers on the surface of calcite (soil) particles, significantly impacting the average dielectric properties of soil particles. This research highlights the relevance of DFT-based data for understanding thin film formation in soil particles and offers valuable insights for environmental and engineering applications.

Liu Linsong, Shi Songlin, Sun Junmin et al.

Based on the analysis of tectonic background and coal-accumulating environment of Jungar coalfield, the coal petrological characteristics, inorganic mineral composition, distribution and occurrence regularity of coal and gangue in No.6 coal of the Junger coalfield are studied, and the genesis is determined byutilizing the research methods of coal petrology, mineralogy and geochemistry.The study shows that the average contents of the inertinite, vitrinite and exinite in the maceral of No.6 coal in the study area are 59 %, 28 % and 13 %, respectively.Compared with the Late Paleozoic coals in other areas of North China, the content of the inertinite is high, which reflects an adequate supply of surface water during the formation of No.6 coal seam.The main inorganic minerals in coal and gangue are kaolinite and boehmite, associated with quartz, calcite, siderite, pyrite, anhydrite, anatase and svanbergite.The vertical changes of the mineral composition and main chemical elements of No.6 coal indicate that the middle of the coal seam is rich in boehmite, while the upper and lower parts are rich in kaolinite.There are three origins of kaolinite: colloidal precipitation crystallization, terrestrial transport sedimentation and volcanic ash alteration.And there are two origins of boehmite: alumina colloidal precipitation crystallization and desilication alteration of kaolinite.

A. Abdul Kareem, G. Yoganandham

India is known around the world for its diverse civilizations and mystical rituals. Scholars and philosophers of the time formed a century-old tradition in the depths of this culture. Despite a long history of being viewed as unscientific, scientists and doctors are now aware of the benefits of traditional Indian health care. Many investigations on traditional medicine and its apparently magical qualities in the treatment of terminal diseases are currently being done. Home remedies are used all around the world, but they are recognized as science in India only. Two traditional Indian medicinal traditions: Ayurveda and Siddha are progressively gaining traction in the global healthcare business. In this article, some of India’s most odd and effective medicinal practices, as well as the benefits of each therapy will be reviewed. Throughout history, traditional medicines were the only source of primary healthcare, and they made a substantial contribution. Knowledge of how to use medicinal plants to treat various ailments was highly valued by ancient cultures. Until the mid-nineteenth century, plants were the principal therapeutic agents used by humans, and they continue to play an important role in pharmaceutical formulations. Traditional medicine is used by around 80 percent of people in undeveloped countries for their primary health care needs because of its low prices, effectiveness, frequently restricted availability of modern medicine, and cultural and religious preferences. Plant research in the traditional system of medicine is becoming increasingly significant in the development of global healthcare and conservation efforts. Traditional medicine systems are being used to uncover biologically active chemicals that are useful to the pharmaceutical industry. To this end, as much information possible is presented about these areas in this article. There are a number of geographically specific traditional health behaviors and are well reviewed in this paper.

Cody Karcher, Robert Haimes

A method of Sequential Log-Convex Programming (SLCP) is constructed that exploits the log-convex structure present in many engineering design problems. The mathematical structure of Geometric Programming (GP) is combined with the ability of Sequential Quadratic Program (SQP) to accommodate a wide range of objective and constraint functions, resulting in a practical algorithm that can be adopted with little to no modification of existing design practices. Three test problems are considered to demonstrate the SLCP algorithm, comparing it with SQP and the modified Logspace Sequential Quadratic Programming (LSQP). In these cases, SLCP shows up to a 77% reduction in number of iterations compared to SQP, and an 11% reduction compared to LSQP. The airfoil analysis code XFOIL is integrated into one of the case studies to show how SLCP can be used to evolve the fidelity of design problems that have initially been modeled as GP compatible. Finally, a methodology for design based on GP and SLCP is briefly discussed.

Sarah Spiekermann, Till Winkler

Digital ethics is being discussed worldwide as a necessity to create more reliable IT systems. This discussion, fueled by the fear of uncontrollable artificial intelligence (AI) has moved many institutions and scientists to demand a value-based system engineering. This article presents how organizations can build responsible and ethically founded systems with the 'Value-based Engineering' (VBE) approach that was standardized in the IEEE 7000TM standard. VBE is a transparent, clearly-structured, step-by-step methodology combining innovation management, risk management, system and software engineering in one process framework. It embeds a robust value ontology and terminology. It has been tested in various case studies. This article introduces readers to the most important steps and contributions of the approach.

Seyed Masoud Soleimanpour, Ahmad Hatami, Gholam Reza Ghahari et al.

Introduction Marls as the most sensitive geological structure against erosion and weathering have a major role in sediment yield of watersheds. Due to the lack of vegetation or sparse pattern of vegetation cover in the Marl Formations; by identifying suitable plants and establishing and propagating them in these areas, the amount of erosion can be reduced. it is difficult to carry out the implementation of erosion control structures in Marl lands due to their mechanical properties; therefore, erosion control using biological measures is necessary and this requires accurate identification of species diversity. Due to the importance of this issue, suitable plants for the conservation of marls were identified in the Tanghesorkh Watershed of Fars Province.Materials and MethodsEleven points with dominance and the presence of marl outcrops were selected as sampling points. The sampling method for studying vegetation in these areas was based on physiognomic-floristic method and using transects and plot methods. Samples were collected in early autumn 2019, late winter 2019 and spring and summer 2020 and using valid scientific methods and plant species were identified. Also, biological form, longevity, vegetation form and chorology were determined. Then, different plant characteristics (density, frequency, canopy and root and rhizome status, amount of litter produced, generation method and longevity) and ranking of plants were studied in order to stabilize and protect the marl soils.Results and Discussion Around 108 plant species belonging to 29 families and 88 genera were identified. Asteraceae family with 18 species, Papilionaceae family with 17 species and Poaceae family with 13 species, are in the first to third ranks, respectively, and 16.67, 15.74 and 12.03% of the number of species to allocate them. In terms of longevity, 40 annual species (16 species of grass and 24 species of forb) and 68 species of perennials (8 species of grass, 30 species of forb, 18 bushes, 2 trees and 10 shrubs) were identified. A total of 37.04 and 62.96% of the total species accounted for plant. Life form included therophyte (36.12%), hemicryptophyte (24.07%), camphite (17.59%), geophyte (11.11%) and phanerophyte (11.11%). 55 species with a specific chorotype specific to the Iran-Turani vegetation zone had the highest frequency (50.93%). According to botanical characteristics, 30, 38, and 40 species in the first to third ranks for conservation of marl in this watershed were introduced.Conclusion Species such as Astragalus susianus, Astragalus faciculifolious, Astragalus gossypinus, Artemisia Aucheri, Convolvuvlus acathocladus, Convolvuvlus leiocalycinus, Amygdalus scoparia, Acantholimon asphodelinum, Stipa barbata and Glycyrrhiza glabra is recommended for the establishment and reproduction of in marl-covered areas in this watershed. Also, due to the irregular exploitation of these lands, it is necessary to protect these areas by controlling and managing livestock grazing. Therefore, this conservation provides opportunities for regeneration and survival of valuable plant species in this watershed.

YI Shun 1, 2, 3, 4, YUE Ke-dong 5, CHEN Jian 1, 2, 3, 4, HUANG Jue-hao 1, 2, 3, 4, LI Jian-bin 6, QIU Yue-feng 7, TIAN Ning 1, 2

Anisotropy exists in the spatial variability of soil parameters. Therefore, it is rational to indicate the spatial distribution of slope parameters using anisotropy random fields. Based on the anisotropic random field of shear strength of soil for a two-layer slope, the effects of vertical scales of fluctuation, horizontal scales of fluctuation and coefficient of variation (COV) of soil parameters on the slope failure probability, instability modes and risk assessments are studied. The main conclusions are drawn as follows: with the increase of COV, the risk of slope failure gradually increases. In low-variability soils, there is almost no risks of slope failure. On the whole, the failure probability of slope is consistent with the risk of failure as COV increases. The deep-layer slop mode accounts for a large proportion, but with the increase of COV, the deep-layer slope mode gradually becomes the shallow slope one. When the scale of fluctuation (including horizontal and vertical) increases, the failure probability of slope and risks increase accordingly. However, when the scale of fluctuation exceed a particular size, which is related to the size of the slope, the increasing amplitude of failure probability and risks slows down as the scale of fluctuation increases.