Properties of sediments deposited in a fluid with different pH     

A. R. Estabragh  A. H. Amini  A. A. Javadi  M. Kouchakzadeh 《Marine Georesources & Geotechnology》2013,31(6):643-650

Abstract

The effect of pH on the physical and mechanical properties of a sediment was investigated through a set of experimental tests. The sediment was formed from deposition of suspended particles in a fluid. Two different types of clay soil were suspended in fluids with different pH (2, 4, 7, 9 and 11) in cylindrical tubes with volume of 1?liter and also in special cylindrical reservoirs. The height of the sediment was measured in the cylindrical tube until equilibrium was achieved. The sediment deposited in the reservoirs was dried in air and then Atterberg limit, compaction and unconfined compressive strength (UCS) tests were conducted on samples prepared from each sediment. The results showed that the final height of the settled sediment is a function of pH; the height of sediment is increased with increasing the pH. Also, the Atterberg limits increased with increasing the pH. The maximum dry unit weight and optimum water content decreased and increased with increasing the pH. The final strength of the sediment decreased with increasing pH. Based on the SEM analysis, it was found that the values of pH influence the properties of the formed sediments.  相似文献   

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

Bordbar  Mojgan  Neshat  Aminreza  Javadi  Saman  Pradhan  Biswajeet  Dixon  Barnali  Paryani  Sina 《Natural Hazards》2022,110(3):1799-1820

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

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