In certain field conditions such as offshore projects under wave loads or embankments under traffic loads, both the vertical and horizontal stresses are variable. However, previous investigations rarely considered the variation in horizontal stress. To better understand the characteristics of natural saturated soft clay, a series of monotonic and cyclic triaxial tests with a K0-consolidation state were carried out under a variable confining pressure (VCP) stress path. The development of axial strain, pore water pressure and effective stress path is analysed. The results show that with the increase in η (the ratio of the variation in the mean effective principal stress to that of the deviatoric stress), the undrained shear strength (qf) decreases continuously. The pore water pressure generation is slightly improved under a stress path with increasing confining pressure. Based on the test results, a unified formula was established to predict the pore water pressure under VCP stress paths. The unique p–q–e relationship of normally consolidated clay in monotonic VCP triaxial tests was also demonstrated. Under VCP stress paths, the amplitude of the pore pressure increases, and the effective stress path tilts more sharply to the right. Moreover, a unified formula was established that can provide a good reference for predicting effective stress paths under cyclic VCP triaxial tests.
Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments,but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT)model and the K-means cluster algorithm to produce a regional landslide susceptibility map.Yanchang County,a typical landslide-prone area located in northwestern China,was taken as the area of interest to introduce the proposed application procedure.A landslide inventory containing 82 landslides was prepared and subse-quently randomly partitioned into two subsets:training data(70%landslide pixels)and validation data(30%landslide pixels).Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means clus-ter algorithm.The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC)curve)of the proposed model was the highest,reaching 0.88,compared with traditional models(support vector machine(SVM)=0.85,Bayesian network(BN)=0.81,frequency ratio(FR)=0.75,weight of evidence(WOE)=0.76).The landslide frequency ratio and fre-quency density of the high susceptibility zones were 6.76/km2 and 0.88/km2,respectively,which were much higher than those of the low susceptibility zones.The top 20%interval of landslide occurrence probability contained 89%of the historical landslides but only accounted for 10.3%of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without contain-ing more"stable"pixels.Therefore,the obtained susceptibility map is suitable for application to landslide risk management practices. 相似文献
Acta Geotechnica - In this paper, the strength, ductility and microstructure behavior of cement-treated silt with polypropylene fiber was studied by a host of experimental studies. The influence of... 相似文献
Hydrogeochemical processes that would occur in polluted groundwater and aquifer system, may reduce the sensitivity of Sr isotope being the indicator of hydraulic fracturing flowback fluids(HFFF) in groundwater. In this paper, the Dameigou shale gas field in the northern Qaidam Basin was taken as the study area, where the hydrogeochemical processes affecting Sr isotope was analysed. Then, the model for Sr isotope in HFFF-polluted groundwater was constructed to assess the sensitivity of Sr isotope as HFFF indicator. The results show that the dissolution can release little Sr to polluted groundwater and cannot affect the εSr(the deviation of the 87 Sr/86 Sr ratio) of polluted groundwater. In the meantime, cation exchange can considerably affect Sr composition in the polluted groundwater. The Sr with low εSr is constantly released to groundwater from the solid phase of aquifer media by cation exchange with pollution of Quaternary groundwater by the HFFF and it accounts for 4.6% and 11.0% of Sr in polluted groundwater when the HFFF flux reaches 10% and 30% of the polluted groundwater, respectively. However, the Sr from cation exchange has limited impact on Sr isotope in polluted groundwater. Addition of Sr from cation exchange would only cause a 0.2% and 1.2% decrease in εSr of the polluted groundwater when the HFFF flux reaches 10% and 30% of the polluted groundwater, respectively. These results demonstrate that hydrogeochemical processes have little effect on the sensitivity of Sr isotope being the HFFF indicator in groundwater of the study area. For the scenario of groundwater pollution by HFFF, when the HFFF accounts for 5%(in volume percentage) of the polluted groundwater, the HFFF can result in detectable shifts of εSr(ΔεSr=0.86) in natural groundwater. Therefore, after consideration of hydrogeochemical processes occurred in aquifer with input of the HFFF, Sr isotope is still a sensitive indicator of the Quaternary groundwater pollution by the HFFF produced in the Dameigou shale of Qaidam Basin. 相似文献
We present an overview of the El Ni?o–Southern Oscillation(ENSO) stability simulation using the Chinese Academy of Meteorological Sciences climate system model(CAMS-CSM). The ENSO stability was quantified based on the Bjerknes(BJ) stability index. Generally speaking, CAMS-CSM has the capacity of reasonably representing the BJ index and ENSO-related air–sea feedback processes. The major simulation biases exist in the underestimated thermodynamic damping and thermocline feedbacks. Further diagnostic analysis reveals that the underestimated thermodynamic feedback is due to the underestimation of the shortwave radiation feedback, which arises from the cold bias in mean sea surface temperature(SST) over central–eastern equatorial Pacific(CEEP). The underestimated thermocline feedback is attributed to the weakened mean upwelling and weakened wind–SST feedback(μ_a) in the model simulation compared to observation. We found that the weakened μ_a is also due to the cold mean SST over the CEEP.The study highlights the essential role of reasonably representing the climatological mean state in ENSO simulations. 相似文献