Effect of Natural Zeolite and Cement Additive on the Strength of Sand

It is widely known and well emphasized that the cemented sand is one of economic and environmental topics in soil stabilization. In some instances, a blend of sand, cement and other materials such as fiber, glass, nano particle and zeolite can commercially be available and effectively used in soil stabilization especially in road construction. In regard to zeolite, its influence and effectiveness on the properties of cemented sands systems has not been completely explored. Hence, in this study, based on an experimental program, it has been tried to investigate the potential of a zeolite stabilizer known as additive material to improve the properties of cemented sands. A total number of 216 unconfined compression tests were carried out on cured samples in 7, 28 and 90 days. Results show unconfined compression strength and failure properties improvements of cement sand specimens when cement replaced by zeolite at optimum proportions of 30 % after 28 days due to pozzolanic reaction. The rate of strength improvement is approximately 20–78 and 20–60 % for 28 and 90 days curing times respectively. The efficiency of using zeolite has been enhanced by increasing the cement content and porosity of the compacted mixture. The replacement of cement by natural zeolite led to an increase of the pH after 14 days. Chemical oxygen demand (COD) tests demonstrate that the materials with the zeolite mixture reveal stronger adsorptive capacity of COD in compare to cemented mixture. Scanning electron microscope images show that adding zeolite in cemented sand changes the microstructure (filling large porosity and pozzolanic reaction) that results in increasing strength.     

Petrography and geochemistry of Silurian Niur sandstones,Derenjal Mountains,East Central Iran: implications for tectonic setting,provenance and weathering

Petrographical and geochemical studies of Silurian Niur sandstones, Derenjal Mountains, Central Iran, were carried out to infer their provenance and tectonic setting. Modal analysis data of 37 medium sand size and well-sorted samples revealed that most quartz is composed of monocrystalline grains with straight to slightly undulos extinction and about 3 % polycrystalline quartz has inclusions, such as rutile needles. The sandstones are classified as quartzarenite, sublitharenite, and subarkose types based on framework composition and geochemistry. Petrographic studies reveal that these sandstones contain quartz, feldspars, and fragments of sedimentary rocks. The detrital modes of these sandstones indicate that they were derived from recycled orogen and stable cratonic source. Major and trace element contents of them are generally depleted (except SiO₂) relative to upper continental crust which is mainly due to the presence of quartz and absence of Al-bearing minerals. Modal composition (e.g., quartz, feldspar, and lithic fragments) and discrimination diagrams based on major elements, trace elements (Ti, La, Th, Sc, and Zr), and also such ratios as La/Sc, Th/Sc, La/Co, and Th/Co, in sandstones suggest a felsic igneous source rock and quartzose polycyclic sedimentary provenance in a passive continental margin setting. Furthermore, high Zr/Sc values in these sandstones are considered as a sign of recycling. We indicated paleo-weathering conditions by modal compositions, the CIA index and Al₂O₃?+?K₂O?+?Na₂O% vs. SiO₂% bivariate for these sandstones. Based on these results, although recycling is important to increase the maturity of the Niur sandstones, humid climate conditions in the source area have played a decisive role.     

Comparison of linear and nonlinear forms of isotherm models for Zn(II) and Cu(II) sorption on a kaolinite

The most appropriate method in designing the adsorption systems and assessing the performance of the adsorption systems is to have an idea on adsorption isotherms. Comparison analysis of linear least square method and nonlinear method for estimating the isotherm parameters was made using the experimental equilibrium data of Zn(II) and Cu(II) onto kaolinite. Equilibrium data were fitted to Freundlich, Langmuir, and Redlich–Peterson isotherm equations. In order to confirm the best-fit isotherms for the adsorption system, the data set using the chi-square (χ ²), combined with the values of the determined coefficient (r ²) was analyzed. Nonlinear method was found to be a more appropriate method for estimating the isotherm parameters. The best fitting isotherm was the Langmuir and Redlich–Peterson isotherm. The Redlich–Peterson is a special case of Langmuir when the Redlich–Peterson isotherm constant g was unity. The sorption capacity of kaolinite to uptake metal ions in the increasing order was given by Cu (4.2721 mg/g)?<?Zn (4.6710 mg/g).     

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