Influence of model type,bias and input parameter variability on reliability analysis for simple limit states in soil–structure interaction problems

ABSTRACT

A general closed-form solution for the true probability of failure of a simple limit state function with one load term and one resistance term is derived. The formulation considers contributions due to model type, uncertainty in method bias values, bias dependencies, uncertainty in estimates of nominal values for correlated and uncorrelated load and resistance terms, and average margin of safety expressed as the operational factor of safety. Example calculations are presented using different load and resistance models for the pullout internal stability limit state of steel strip reinforced soil walls together with matching bias data reported in the literature.     

Probabilistic analysis of simple slopes with cohesive soil strength using RLEM and RFEM

The 2D random finite element method and the one-dimensional and 2D random limit equilibrium method are used to investigate the influence of spatial variability of soil strength parameters on the probability of failure of simple soil slopes with cohesive undrained shear strength. The combined influence of spatial variability of soil properties and cross-correlation between undrained soil strength and unit weight on the computed probability of failure is explored. The paper identifies conditions where numerical outcomes are similar and where they are not. The limitations of each analysis method are described and implications to analysis and design are identified.

Abbreviations: FEM: finite element method; LEM: limit equilibrium method; RFEM: random finite element method; RLEM: random limit equilibrium method     

Effect of silica fume on compressive strength of oil-polluted concrete in different marine environments

In the present research, effect of silica fume as an additive and oil polluted sands as aggregates on compressive strength of concrete were investigated experimentally. The amount of oil in the designed mixtures was assumed to be constant and equal to 2% of the sand weight. Silica fume accounting for 10%, 15% and 20% of the weight is added to the designed mixture. After preparation and curing, concrete specimens were placed into the three different conditions: fresh, brackish and saltwater environments (submerged in fresh water, alternation of exposed in air & submerged in sea water and submerged in sea water). The result of compressive strength tests shows that the compressive strength of the specimens consisting of silica fume increases significantly in comparison with the control specimens in all three environments. The compressive strength of the concrete with 15% silica fume content was about 30% to 50% higher than that of control specimens in all tested environments under the condition of using polluted aggregates in the designed mixture.     

Experimental and numerical investigation for energy dissipation of supercritical flow in sudden contractions

Dealing with kinetic energy is one of the most important problems in hydraulic structures, and this energy can damage downstream structures. This study aims to study energy dissipation of supercritical water flow passing through a sudden contraction. The experiments were conducted on a sudden contraction with 15 cm width. A 30 cm wide flume was installed. The relative contraction ranged from 8.9 to 9.7, where relative contraction refers to the ratio of contraction width to initial flow depth. The Froude value in the investigation varied from 2 to 7. The contraction width of numerical simulation was 5~15 cm, the relative contraction was 8.9~12.42, and the Froude value ranged from 8.9~12.42. In order to simulate turbulence, the k-ε RNG model was harnessed. The experimental and numerical results demonstrate that the energy dissipation increases with the increase of Froude value. Also, with the sudden contraction, the rate of relative depreciation of energy is increased due to the increase in backwater profile and downstream flow depth. The experimentation verifies the numerical results with a correlation coefficient of 0.99 and the root mean square error is 0.02.     

Improving the coastal aquifers’ vulnerability assessment using SCMAI ensemble of three machine learning approaches

The main objective of this study is to integrate adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural network (ANN) to design an integrated supervised committee machine artificial intelligence (SCMAI) model to spatially predict the groundwater vulnerability to seawater intrusion in Gharesoo-Gorgan Rood coastal aquifer placed in the northern part of Iran. Six hydrological GALDIT parameters (i.e., G groundwater occurrence, A aquifer hydraulic conductivity, L level of groundwater above sea level, D distance from the shore, I impact of the existing status of seawater intrusion in the region, and T thickness of the aquifer) were considered as inputs for each model. In the training step, the values of GALDIT’s vulnerability index were conditioned by using the values of TDS concentration in order to obtain the conditioned vulnerability index (CVI). The CVI was considered as the target for each model. After training the models, each model was tested using a separate TDS dataset. The results indicated that the ANN and ANFIS algorithms performed better than the SVM algorithm. The values of correlation were obtained as 88, 87, and 80% for ANN, ANFIS, and SVM models, respectively. In the testing step of the SCMAI model, the values of RMSE, R², and r were obtained as 6.4, 0.95, and 97%, respectively. Overall, SCMAI model outperformed other models to spatially predicting vulnerable zones. The result of the SCMAI model confirmed that the western zones along the shoreline had the highest vulnerability to seawater intrusion; therefore, it seems critical to consider emergency protection plans for study area.

Graphic abstract