Predicting and validating the load-settlement behavior of large-scale geosynthetic-reinforced soil abutments using hybrid intelligent modeling

Settlement prediction of geosynthetic-reinforced soil(GRS)abutments under service loading conditions is an arduous and challenging task for practicing geotechnical/civil engineers.Hence,in this paper,a novel hybrid artificial intelligence(AI)-based model was developed by the combination of artificial neural network(ANN)and Harris hawks’optimisation(HHO),that is,ANN-HHO,to predict the settlement of the GRS abutments.Five other robust intelligent models such as support vector regression(SVR),Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimisation regression(SMOR),and least-median square regression(LMSR)were constructed and compared to the ANN-HHO model.The predictive strength,relalibility and robustness of the model were evaluated based on rigorous statistical testing,ranking criteria,multi-criteria approach,uncertainity analysis and sensitivity analysis(SA).Moreover,the predictive veracity of the model was also substantiated against several large-scale independent experimental studies on GRS abutments reported in the scientific literature.The acquired findings demonstrated that the ANN-HHO model predicted the settlement of GRS abutments with reasonable accuracy and yielded superior performance in comparison to counterpart models.Therefore,it becomes one of predictive tools employed by geotechnical/civil engineers in preliminary decision-making when investigating the in-service performance of GRS abutments.Finally,the model has been converted into a simple mathematical formulation for easy hand calculations,and it is proved cost-effective and less time-consuming in comparison to experimental tests and numerical simulations.

Mapping soil organic matter in cultivated land based on multi-year composite images on monthly time scales

Rapid and accurate acquisition of soil organic matter(SOM)information in cultivated land is important for sustainable agricultural development and carbon balance management.This study proposed a novel approach to predict SOM with high accuracy using multiyear synthetic remote sensing variables on a monthly scale.We obtained 12 monthly synthetic Sentinel-2 images covering the study area from 2016 to 2021 through the Google Earth Engine(GEE)platform,and reflectance bands and vegetation indices were extracted from these composite images.Then the random forest(RF),support vector machine(SVM)and gradient boosting regression tree(GBRT)models were tested to investigate the difference in SOM prediction accuracy under different combinations of monthly synthetic variables.Results showed that firstly,all monthly synthetic spectral bands of Sentinel-2 showed a significant correlation with SOM(P<0.05)for the months of January,March,April,October,and November.Secondly,in terms of single-monthly composite variables,the prediction accuracy was relatively poor,with the highest R^(2)value of 0.36 being observed in January.When monthly synthetic environmental variables were grouped in accordance with the four quarters of the year,the first quarter and the fourth quarter showed good performance,and any combination of three quarters was similar in estimation accuracy.The overall best performance was observed when all monthly synthetic variables were incorporated into the models.Thirdly,among the three models compared,the RF model was consistently more accurate than the SVM and GBRT models,achieving an R^(2)value of 0.56.Except for band 12 in December,the importance of the remaining bands did not exhibit significant differences.This research offers a new attempt to map SOM with high accuracy and fine spatial resolution based on monthly synthetic Sentinel-2 images.

Novel Hybrid X GBoost Model to Forecast Soil Shear Strength Based on Some Soil Index Tests

When building geotechnical constructions like retaining walls and dams is of interest,one of the most important factors to consider is the soil’s shear strength parameters.This study makes an effort to propose a novel predictive model of shear strength.The study implements an extreme gradient boosting(XGBoost)technique coupled with a powerful optimization algorithm,the salp swarm algorithm(SSA),to predict the shear strength of various soils.To do this,a database consisting of 152 sets of data is prepared where the shear strength(τ)of the soil is considered as the model output and some soil index tests(e.g.,dry unit weight,water content,and plasticity index)are set as model inputs.Themodel is designed and tuned using both effective parameters of XGBoost and SSA,and themost accuratemodel is introduced in this study.Thepredictionperformanceof theSSA-XGBoostmodel is assessedbased on the coefficient of determination(R2)and variance account for(VAF).Overall,the obtained values of R^(2) and VAF(0.977 and 0.849)and(97.714%and 84.936%)for training and testing sets,respectively,confirm the workability of the developed model in forecasting the soil shear strength.To investigate the model generalization,the prediction performance of the model is tested for another 30 sets of data(validation data).The validation results(e.g.,R^(2) of 0.805)suggest the workability of the proposed model.Overall,findings suggest that when the shear strength of the soil cannot be determined directly,the proposed hybrid XGBoost-SSA model can be utilized to assess this parameter.

STUDY ON THE MODEL FOR PREDICTING SOIL EROSION AND ITS APPLICATION IN ARID AREA

On the basis of analyzing the soil erosion factors in typical arid area basin, this article tries to build a model by using USLE (Universal Soil Loss Equation). The first step is to digitize the topographic map (1∶100?000) and form the DEM (Digital Elevation Model), then use them to obtain necessary data of topographic factors. The second step is to get main elements causing soil erosion through using Main Element Analyzing Program. The third step is to systematically analyze all factors of soil erosion by applying Grey Dynamic Model and Fuzzy Mathematics, and then take GIS software to draw the colored map in the way that different colors present different intensities of soil erosion. At last the regional change of soil erosion amount on the basis of the color map is analyzed.

Sentinel-2 Satellite Imagery Application to Monitor Soil Salinity and Calcium Carbonate Contents in Agricultural Fields

The estuary tides affect groundwater dynamics;these areas are susceptible to waterlogging and salinity issues.A study was conducted on two fields with a total area of 60 hectares under a center pivot irrigation system that works with solar energy and belong to a commercial farm located in Northern Sudan.To monitor soil salinity and calcium carbonate in the area and stop future degradation of soil resources,easy,non-intrusive,and practical procedures are required.The objective of this study was to use remote sensing-determined Sentinel-2 satellite imagery using various soil indices to develop prediction models for the estimation of soil electrical conductivity(EC)and soil calcium carbonate(CaCO_(3)).Geo-referenced soil samples were collected from 72 locations and analyzed in the laboratory for soil EC and CaCO_(3).The electrical conductivity of the soil saturation paste extract was represented by average values in soil dataset samples from two fields collected from the topsoil layer(0 to 15 cm)characteristic of the local salinity gradient.The various soil indices,used in this study,were calculated from the Sentinel-2 satellite imagery.The prediction was determined using the root mean square error(RMSE)and cross validation was done using coefficient of determination.The results of regression analysis showed linear relationships with significant correlation between the EC analyzed in laboratory and the salinity index-2“SI2”(Model-1:R^(2)=0.59,p=0.00019 and root mean square error(RMSE=1.32%)and the bare soil index“BSI”(Model-2:R^(2)=0.63,p=0.00012 and RMSE=6.42%).Model-1 demonstrated the best model for predicting soil EC,and validation R^(2)and RMSE values of 0.48%and 1.32%,respectively.The regression analysis results for soil CaCO_(3)determination showed linear relationships with data obtained in laboratory and the bare soil index“BSI”(Model-3:R^(2)=0.45,p=0.00021 and RMSE=1.29%)and the bare soil index“BSI”&Normalized difference salinity index“NDSI”(Model-4:R^(2)=0.53,p=0.00015 and RMSE=1.55%).The validation confirmed the Model-3 results for prediction of soil CaCO_(3)with R^(2)and RMSE values of 0.478%and 1.29%,respectively.Future soil monitoring programs might consider the use of remote sensing data for assessing soil salinity and CaCO_(3)using soil indices results generated from satellite image(i.e.,Sentinel-2).

Automated soil resources mapping based on decision tree and Bayesian predictive modeling

This article presents two approaches for automated building of knowledge bases of soil resources mapping. These methods used decision tree and Bayesian predictive modeling, respectively to generate knowledge from training data. With these methods, building a knowledge base for automated soil mapping is easier than using the conventional knowledge acquisition approach. The knowledge bases built by these two methods were used by the knowledge classifier for soil type classification of the Longyou area, Zhejiang Province, China using TM bi-temporal imageries and GIS data. To evaluate the performance of the resultant knowledge bases, the classification results were compared to existing soil map based on field survey. The accuracy assessment and analysis of the resultant soil maps suggested that the knowledge bases built by these two methods were of good quality for mapping distribution model of soil classes over the study area.

Prediction model for mercury transfer from soil to corn grain and its cross-species extrapolation

In this study the transfer characteristics of mercury（Hg） from a wide range of Chinese soils to corn grain（cultivar Zhengdan 958） were investigated. Prediction models were developed for determining the Hg bioconcentration factor（BCF） of Zhengdan 958 from soil, including the soil properties, such as p H, organic matter（OM） concentration, cation exchange capacity（CEC）, total nitrogen concentration（TN）, total phosphorus concentration（TP）, total potassium concentration（TK）, and total Hg concentration（THg）, using multiple stepwise regression analysis. These prediction models were applied to other non-model corn cultivars using a cross-species extrapolation approach. The results indicated that the soil p H was the most important factor associated with the transfer of Hg from soil to corn grain. Hg bioaccumulation in corn grain increased with the decreasing p H. No significant differences were found between two prediction models derived from different rates of Hg applied to the soil as HgCl2. The prediction models established in this study can be applied to other non-model corn cultivars and are useful for predicting Hg bioconcentration in corn grain and assessing the ecological risk of Hg in different soils.

Evaluation of creep models for frozen soils

To model the creep behavior of frozen soils, three creep stages have to be reasonably described （i.e., primary, secondary and tertiary stages）. Based on a series of uniaxial creep test results, three creep models were evaluated. It was shown that hypoplastic creep model has high prediction accuracy for both creep strain and strain rate in a wide stress range. The elementary rheological creep model can only be used for creep strains at low stress levels, because of the restraints of its mathematical construction. For the soft soil creep model, the progressive change from the primary to secondary and tertiary stages cannot be captured at high stress levels. Therefore, the elementary rheological and soft soil creep models can only be used for low stress levels without a tertiary stage; while the hypoplastic creep model is applicable at high stress levels with the three creep stages.

Analysis of Variation Characters and Prediction Model of Soil Temperature in Solar Greenhouse

[Objective] The aim was to study the soil temperature changes and its forecast model in greenhouse by solar heat. [Method] Annual and daily variation characters of soil temperature were analyzed in this paper by using the observation data of air temperature out of solar greenhouse and different layers soil temperature in it. The soil temperature (daily maximum, daily minimum and daily mean) forecasting models were also studied. Simulation and test were conducted to the forecast model of soil temperature in the greenhouse. [Result] The annual changes and daily changes of soil temperature of each layer in the greenhouse were in single peak curve. The lower layer temperature changes were smaller than the upper layer. The soil temperature of each layer within the greenhouse was closely related to the relevance of same type temperature outside the greenhouse of the day. Taking the average daily temperature, daily maximum temperature and daily lowest temperature of the day and the day before as forecast factors, soil temperature forecast model of different layer of same type within greenhouse was constructed. The simulation outcome of average daily temperature of each layer within the greenhouse was better than the simulation outcome of highest temperature of corresponding layer, worse than the simulation of lowest temperature of corresponding layer. The highest temperature of lower soil and daily temperature of soil were better than the upper layer. The simulated soil temperature was much more close to the observation when the observation was during 15-30 ℃. In other interval, it was lower than the observation. [Conclusion] The study offered theoretical reference for the growth environment of sunlight greenhouse plantation.

Surface Soil Moisture Simulation for a Typical Torrential Event with a Modified Noah LSM Coupling to the NWP Model

Surface soil moisture has great impact on both meso-and microscale atmospheric processes,especially on severe local convection processes and on the dynamics of short-lived torrential rains.To promote the performance of the land surface model (LSM) in surface soil moisture simulations,a hybrid hydrologic runoff parameterization scheme based upon the essential modeling theories of the Xin'anjiang model and Topography based hydrological Model (TOPMODEL) was developed in preference to the simple water balance model (SWB) in the Noah LSM.Using a strategy for coupling and integrating this modified Noah LSM to the Global/Regional Assimilation and Prediction System (GRAPES) analogous to that used with the standard Noah LSM,a simulation of atmosphere-land surface interactions for a torrential event during 2007 in Shandong was attempted.The results suggested that the surface,10-cm depth soil moisture simulated by GRAPES using the modified hydrologic approach agrees well with the observations.Improvements from the simulated results were found,especially over eastern Shandong.The simulated results,compared with the products of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture datasets,indicated a consistent spatial pattern over all of China.The temporal variation of surface soil moisture was validated with the data at an observation station,also demonstrated that GRAPES with modified Noah LSM exhibits a more reasonable response to precipitation events,even though biases and systematic trends may still exist.

Prediction Model of Soil Nutrients Loss Based on Artificial Neural Network

On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian-River basin. The results by calculating show that the solution based on BP algorithms are consis- tent with those based multiple - variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.

Predicting microbial extracellular electron transfer activity in paddy soils with soil physicochemical properties using machine learning

Soil extracellular electron transfer(EET)is a pivotal biological process within the realm of soil.Unfortunately,EET suffers from a lack of predictive models.Herein,an intricately crafted machine learning model has been developed for the purpose of predicting soil EET by using the physicochemical properties of soil as independent input variables and the EET capabilities in terms of current density(j_(max))and Coulombic charge(C_(out))as dependent output variables.An autoencoder ensemble stacking(AES)model was developed to address the aforementioned issue by integrating support vector machine,multilayer perceptron,extreme gradient boosting,and light gradient boosting machine algorithms as the stacking algorithms.With 10-fold crossvalidation,the AES model exhibited notable improvements in predicting j_(max)and C_(out),with average test R^(2)values of 0.83 and 0.84,respectively,surpassing those of single machine learning(ML)models and the basic ensemble model.By utilizing partial correlation plots(PDPs),Shapley Additive explanations(SHAP)values,and SHAP decision plots,we quantitatively explained the impact and contribution of the input molecules on the AES model’s predictions of j_(max)and C_(out).In the context of the SHAP method for the AES model,total carbon(TC)was identified as the most correlated descriptor for j_(max),while total organic carbon(TOC)stood out as the most relevant descriptor for C_(out).In the prediction tasks of j_(max)and C_(out)within the AES model,employing a multitask ML approach allowed the model to benefit from the shared information of input variables,thereby enhancing its overall generalizability.This study provides a feasible tool for the prediction of soil EET from soil physiochemical properties and an advanced understanding of the relationship between soil physiochemical properties and EET capability.

Recent progress and future prospect of digital soil mapping： A review

To deal with the global and regional issues including food security, climate change, land degradation, biodiversity loss, water resource management, and ecosystem health, detailed accurate spatial soil information is urgently needed. This drives the worldwide development of digital soil mapping. In recent years, significant progresses have been made in different aspects of digital soil mapping. The main purpose of this paper is to provide a review for the major progresses of digital soil mapping in the last decade. First, we briefly described the rise of digital soil mapping and outlined important milestones and their influence, and main paradigms in digital soil mapping. Then, we reviewed the progresses in legacy soil data, environmental covariates, soil sampling, predictive models and the applications of digital soil mapping products. Finally, we summarized the main trends and future prospect as revealed by studies up to now. We concluded that although the digital soil mapping is now moving towards mature to meet various demands of soil information, challenges including new theories, methodologies and applications of digital soil mapping, especially for highly heterogeneous and human-affected environments, still exist and need to be addressed in the future.

Relationships between Soil Depth and Terrain Attributes in a Semi Arid Hilly Region in Western Iran

Soil depth generally varies in mountainous regions in rather complex ways.Conventional soil survey methods for evaluating the soil depth in mountainous and hilly regions require a lot of time,effort and consequently relatively large budget to perform.This study was conducted to explore the relationships between soil depth and topographic attributes in a hilly region in western Iran.For this,one hundred sampling points were selected using randomly stratified methodology,and considering all geomorphic surfaces including summit,shoulder,backslope,footslope and toeslope;and soil depth was actually measured.Eleven primary and secondary topographic attributes were derived from the digital elevation model(DEM) at the study area.The result of multiple linear regression indicated that slope,wetness index,catchment area and sediment transport index,which were included in the model,could explain about 76 % of total variability in soil depth at the selected site.This proposed approach may be applicable to other hilly regions in the semi-arid areas at a larger scale.

Cyclic Lateral Responses of Monopiles Considering the Influence of Pile−Soil Relative Stiffness in Sand

The existing studies have primarily focused on the effect of cyclic load characteristics(namely,cyclic load ratio and amplitude ratio)on cyclic lateral response of monopiles in sand,with little attention paid to the effect of pile−soil relative stiffness(K_(R)).This paper presents a series of 1-g cyclic tests aimed at improving understanding of the cyclic lateral responses of monopiles under different pile−soil systems.These systems are arranged by two model piles with different stiffness,including four different slenderness ratios(pile embedded length,L,normalized by diameter,D)under medium dense sand.The K_(R)-values are calculated by a previously proposed method considering the real soil stress level.The test results show that the lateral accumulation displacement increases significantly with the increment of the K_(R)-value,while the cyclic secant stiffness performs inversely.The maximum pile bending moment increases with the cycle number for the rigid pile−soil system,but shows a decreasing trend in the flexible system.For an uppermost concern,an empirical model is proposed to predict the accumulated displacement of arbitrary pile−soil systems by combining the results from this study with those from previous experimental investigations.The validity of the proposed model is demonstrated by 1-g and centrifuge tests.

Soil geochemical prospecting prediction method based on deep convolutional neural networks-Taking Daqiao Gold Deposit in Gansu Province, China as an example

A method is proposed for the prospecting prediction of subsurface mineral deposits based on soil geochemistry data and a deep convolutional neural network model.This method uses three techniques(window offset,scaling,and rotation)to enhance the number of training data for the model.A window area is used to extract the spatial distribution characteristics of soil geochemistry and measure their correspondence with the occurrence of known subsurface deposits.Prospecting prediction is achieved by matching the characteristics of the window area of an unknown area with the relationships established in the known area.This method can efficiently predict mineral prospective areas where there are few ore deposits used for generating the training dataset,meaning that the deep-learning method can be effectively used for deposit prospecting prediction.Using soil active geochemical measurement data,this method was applied in the Daqiao area,Gansu Province,for which seven favorable gold prospecting target areas were predicted.The Daqiao orogenic gold deposit of latest Jurassic and Early Jurassic age in the southern domain has more than 105 t of gold resources at an average grade of 3-4 g/t.In 2020,the project team drilled and verified the K prediction area,and found 66 m gold mineralized bodies.The new method should be applicable to prospecting prediction using conventional geochemical data in other areas.

Resistivity is used as a tool to evaluate the variability of soil water content

Resistivity is used to evaluate soil water content(SWC),which has the advantages of not causing soil disturbance and in low price.It is an effective way to assess the SWC variability.This paper aims to evaluate the variability of loess slope SWC through the change of resistivity.It provides a simple way for long term SWC monitoring to solve the expensive cost of deploying moisture sensors.In this context,geoelectric and environmental factors such as soil temperature and SWC were monitored for three years.The prediction model of apparent resistivity and SWC was calibrated.The post processing of geoelectric data was introduced.In addition,the SWC collected by Time-Domain Reflectometry(TDR)was used to verify the feasibility of electrical resistivity tomography(ERT)data.The SWC variability in the process of rainfall,the evolution of four seasons,and the alternation of drying and wetting were evaluated.The research results show that:i)the SWC monitored by ERT and TDR can reflect the response and hysteretic effect of water content at 0.5-3.0 m depth.ii)The moisture content monitored by ERT reflects that the soil is relatively wet in summer and autumn and dry in winter and spring.iii)From 2017 to 2020,the SWC increased in August,and the soil became dry in January.iv)Two areas with high SWC and three areas with low SWC on loess slope are reflected by resistivity.The outcome can provide the change information of SWC to a great extent without excavating boreholes.

A Brief Study on Using pH_H₂Oto Predict pH_KClfor Acid Soils

pHKCl and pHH2O are two basic necessary indexes to reflect the acidity of asoil. Predicting pHKCl?directly from pHH2O?could save the cost of laboratory work. In this study, the values of pHKCl and of 442 and 310 horizon samples from 126 and 98 soil profiles (0 - 120 cm in depth) surveyed from 2014 to 2015 in Guangxi and Yunnan were used to establish the optimal correlation model between pHKCl and pHH2O. The results showed that: 1) pHKCl is lower than pHH2O, pHKCl?was 0.07 - 1.99 units with a mean of 0.99 units lower than for Guangxi, while 0.03 - 1.90 units with a mean of 0.89 lower than pHH2O?for Yunan. 2) There is significant positive correlation between pHKCl?and pHH2O, the optimal correlation models between pHKCl?(y) and pHH2O?(x) for Guangxi and Yunnan are y = 0.1963x2 &minus;1.0512x + 4.338, R2 = 0.836, p 0.1859x, R2 = 0.769, p pHKCl?with exchangeable H+ and Al3+ (R2 = 0.487, 0.716, p pHKCl?is dominated by exchangeable Al3+, followed by exchangeable H+, and their contribution to pHKCl?were 71.1% and 28.7%, respectively.

A Quantitative LC-MS/MS Study of the Partitioning, Transport, and Fate of Pesticide Residues on Soil

Titration of pesticides onto sorption sites can determine sorption capacities on soils. Previous studies have tracked the sorption capacities and detailed kinetics of the uptake of atrazine and its decomposition byproduct hydroxyatrazine on different soils, including measurements made using LC-MS/MS. These studies have now been extended to explore sorption-desorption equilibria for a mixture of pesticides from soil using LC-MS/MS. Desorption of sorbed pesticide residues has environmental regulatory implications for pesticide levels in runoff, or for longer term sequestration, partitioning, and transport. The uptake of pesticides by the soil at equilibrium was measured for a number of different concentrations, and sorption capacities were estimated. Pesticide-soil interaction studies were conducted by exposing standard stock solutions of pesticide mixtures to a characterized Nova Scotia soil. The mixture contained atrazine and dicamba. Initial aqueous mixture concentrations ranging from 5 × 10-9 to 10-5 M or greater were exposed to 25 mg aliquots of soil and allowed to reach equilibrium. The total uptake of each pesticide was measured indirectly, by measuring the concentration remaining in solution using an IONICS 3Q 120 triple quadrupole mass spectrometer. These sorption capacities have been supplemented by studies examining equilibrium recovery rates from soil aliquots with different initial uptakes. This gives insight into the fraction of easily recoverable (reversibly sorbed) pesticides on the soil. Proper quantification of equilibrium constants and kinetic rate coefficients using high performance LC-MS/MS facilitates the construction of accurate, predictive models. Predictive kinetic models can successfully mimic the experimental results for solution concentration, labile sorption, and intra-particle diffusion, and could be used to guide regulatory practices.

植被根系含量对膨胀土持水和渗透特性的影响

为探究植被根系含量对膨胀土持水和渗透特性的影响规律,选取南宁市膨胀土地区的膨胀土,掺入狗牙根根系制备不同含根率的试样,进行室内压力板仪试验和变水头渗透试验,采用Van Genuchten-Mualem模型预测不同含根率的膨胀土非饱和渗透系数。结果表明:掺入根系后,膨胀土持水能力减弱,根系掺入增加膨胀土内部大孔隙的比例,含根率越高,大孔隙体积占总孔隙体积比例越大,持水能力相较于纯土下降的幅度越大;饱和状态下,膨胀土渗透系数随含根率增大而增大;非饱和状态下,低吸力阶段(0~25 kPa)的膨胀土渗透性随含根率的增大而增大,随着基质吸力增加到高吸力阶段(200~1 000 kPa),根系优势流效果减弱,含根膨胀土渗透性逐渐趋近低于纯土;细观结果表明根系的掺入使膨胀土内部产生贯通裂缝,是影响膨胀土持水和渗透能力的关键因素。研究进一步揭示了植被根系对膨胀土增渗作用的机理和规律,为综合评价植被防护膨胀土边坡效果提供参考。