Classification and rating of disintegrated dolomite strata for slope stability analysis

Although disintegrated dolomite,widely distributed across the globe,has conventionally been a focus of research in underground engineering,the issue of slope stability issues in disintegrated dolomite strata is gaining increasing prominence.This is primarily due to their unique properties,including low strength and loose structure.Current methods for evaluating slope stability,such as basic quality(BQ)and slope stability probability classification(SSPC),do not adequately account for the poor integrity and structural fragmentation characteristic of disintegrated dolomite.To address this challenge,an analysis of the applicability of the limit equilibrium method(LEM),BQ,and SSPC methods was conducted on eight disintegrated dolomite slopes located in Baoshan,Southwest China.However,conflicting results were obtained.Therefore,this paper introduces a novel method,SMRDDS,to provide rapid and accurate assessment of disintegrated dolomite slope stability.This method incorporates parameters such as disintegrated grade,joint state,groundwater conditions,and excavation methods.The findings reveal that six slopes exhibit stability,while two are considered partially unstable.Notably,the proposed method demonstrates a closer match with the actual conditions and is more time-efficient compared with the BQ and SSPC methods.However,due to the limited research on disintegrated dolomite slopes,the results of the SMRDDS method tend to be conservative as a safety precaution.In conclusion,the SMRDDS method can quickly evaluate the current situation of disintegrated dolomite slopes in the field.This contributes significantly to disaster risk reduction for disintegrated dolomite slopes.

Intelligent prediction of slope stability based on visual exploratory data analysis of 77 in situ cases

Slope stability prediction research is a complex non-linear system problem.In carrying out slope stability prediction work,it often encounters low accuracy of prediction models and blind data preprocessing.Based on 77 field cases,5 quantitative indicators are selected to improve the accuracy of prediction models for slope stability.These indicators include slope angle,slope height,internal friction angle,cohesion and unit weight of rock and soil.Potential data aggregation in the prediction of slope stability is analyzed and visualized based on Six-dimension reduction methods,namely principal components analysis(PCA),Kernel PCA,factor analysis(FA),independent component analysis(ICA),non-negative matrix factorization(NMF)and t-SNE(stochastic neighbor embedding).Combined with classic machine learning methods,7 prediction models for slope stability are established and their reliabilities are examined by random cross validation.Besides,the significance of each indicator in the prediction of slope stability is discussed using the coefficient of variation method.The research results show that dimension reduction is unnecessary for the data processing of prediction models established in this paper of slope stability.Random forest(RF),support vector machine(SVM)and k-nearest neighbour(KNN)achieve the best prediction accuracy,which is higher than 90%.The decision tree(DT)has better accuracy which is 86%.The most important factor influencing slope stability is slope height,while unit weight of rock and soil is the least significant.RF and SVM models have the best accuracy and superiority in slope stability prediction.The results provide a new approach toward slope stability prediction in geotechnical engineering.

Malnutrition as a predictor of prolonged length of hospital stay in patients with gynecologic malignancy: A comparative analysis

Objective To explore the consistency of the Patient-generated Subjective Global Assessment(PG-SGA)and Nutritional Risk Screening-2002(NRS-2002)for nutritional evaluation of patients with gynecologic malignancy and their predictive effect on the length of hospital stay(LOS).Methods We recruited 147 hospitalized patients with gynecologic malignancy from Nanfang Hospital in 2017.Their nutritional status was assessed using the PG-SGA and NRS-2002.The consistency between the two assessments was compared via the Kappa test.The relationship between malnutrition and LOS was analyzed using crosstabs and Spearman’s correlation.Results The PG-SGA demonstrated that 66.7%and 54.4%of patients scoring≥2 and≥4 were malnourished,respectively.Furthermore,the NRS-2002 indicated that 55.8%of patients were at nutritional risk.Patients with ovarian cancer had a relatively high incidence of malnutrition.However,this was only significant for patients who scored≥4 in the PG-SGA(P=0.001 and P=0.019 for endometrial carcinoma and cervical cancer,respectively).The PG-SGA and NRS-2002 showed good consistency in evaluating the nutritional status of patients with gynecologic malignancy(0.689,0.643 for PG-SGA score≥2,score≥4 and NRS-2002,respectively).Both the scores of PG-SGA and NRS-2002 were positively correlated with LOS.Furthermore,prolonged LOS was higher in patients with malnutrition than in those with adequate nutrition.Conclusion The PG-SGA and NRS-2002 shared a good consistency in evaluating the nutritional status of patients with gynecologic malignancy.Both assessments could be used as predictors of LOS.