Water/Cement/Bentonite Ratio Selection Method for Artificial Groundwater Barriers Made of Cutoff Walls

As urban development requires groundwater table isolation of various historically polluted sources, the necessity of building effective, strong, flexible, and low-permeability cutoff walls raises the question of choosing optimum construction materials. Various authors have proposed water–cement–bentonite mixtures, which are often chosen by experience or a trial-and-error approach, using classical methods for testing (Marsh funnel) and representation of results (water–cement ratio, water–bentonite ratio). The paper proposes a more precise approach for assessing the viscosity and global representation of the three components. Moreover, this approached is exemplified with a better documented recipe for the choice of materials based on laboratory results. The representation of the mixtures was undertaken on a limited domain of a ternary diagram, where the components are given in terms of mass percentage. The derived properties (viscosity, permeability, and compressive strength) are presented on a grid corresponding to the physically possible mixtures. Based on this representation, the most efficient recipes are chosen. Because the mixture contains only fine aggregates, the viscosity was determined using a laboratory viscosimeter

Water/Cement/Bentonite Ratio Selection Method for Artificial Groundwater Barriers Made of Cutoff Walls

As urban development requires groundwater table isolation of various historically polluted sources, the necessity of building effective, strong, flexible, and low-permeability cutoff walls raises the question of choosing optimum construction materials. Various authors have proposed water–cement–bentonite mixtures, which are often chosen by experience or a trial-and-error approach, using classical methods for testing (Marsh funnel) and representation of results (water–cement ratio, water–bentonite ratio). The paper proposes a more precise approach for assessing the viscosity and global representation of the three components. Moreover, this approached is exemplified with a better documented recipe for the choice of materials based on laboratory results. The representation of the mixtures was undertaken on a limited domain of a ternary diagram, where the components are given in terms of mass percentage. The derived properties (viscosity, permeability, and compressive strength) are presented on a grid corresponding to the physically possible mixtures. Based on this representation, the most efficient recipes are chosen. Because the mixture contains only fine aggregates, the viscosity was determined using a laboratory viscosimeter

Automated versus Manual Mapping of Gravel Pit Lakes from South-Eastern Romania for Detailed Morphometry and Vegetation

In recent years, the accelerated development of the remote sensing domain and the improvement of the resolution and frequency of satellite images allowed the increase in the accuracy of the evaluation of morphometric characteristics and the spatiotemporal distribution of pit lakes, including the small ones. Our study quantitatively analyzes small-scale pit lakes in the piedmont and subsidence plains from contact with the Getic and Curvature Subcarpathians from Romania using the normalized difference water index (NDWI) and data series, with different resolutions, from Landsat 8, Google Earth, and Sentinel 2A. The problems encountered in extracting the contours of the gravel pit lakes were determined by the different resolution of the images, the uneven quality of the images exported from Google Earth, and an additional challenge was given by the diversity of the analyzed land surfaces, the land use, and the optical properties of the lakes. A comparison of the obtained NDWI values using data series from Sentinel 2A and Landsat 8 highlighted the importance of resolution and also showed a larger spectral difference between the identified water bodies and the surrounding land in favor of Sentinel 2A. Regarding the vegetation-derived indices, superior leaf area index (1.8–3) was recorded in low-lying plains and mixed areas (tall shrubs, wetlands, etc.) because the river banks have increased moisture that supports taller species with denser foliage and the sparsely vegetated areas are located in agricultural crops and in/near villages. Changes in vegetation richness and abundance can be spatiotemporally monitored using indices derived from the spectral bands of satellite imagery