In order to evaluate and project the quality of groundwater utilized for irrigation in the Sahara aquifer in Algeria, this research employed irrigation water quality indices (IWQIs), artificial neural network (ANN) models, and Gradient Boosting Regression (GBR), alongside multivariate statistical analysis and a geographic information system (GIS), to assess and forecast the quality of groundwater used for irrigation in the Sahara aquifer in Algeria. Twenty-seven groundwater samples were examined using conventional analytical methods. The obtained physicochemical parameters for the collected groundwater samples showed that Ca2+ > Mg2+ > Na+ > K+, and Cl− > SO42− > HCO3− > NO3−, owing to the predominance of limestone, sandstone, and clay minerals under the effects of human activity, ion dissolution, rock weathering, and exchange processes, which indicate a Ca-Cl water type. For evaluating the quality of irrigation water, the IWQIs values such as irrigation water quality index (IWQI), sodium adsorption ratio (SAR), Kelly index (KI), sodium percentage (Na%), permeability index (PI), and magnesium hazard (MH) showed mean values of 47.17, 1.88, 0.25, 19.96, 41.18, and 27.87, respectively. For instance, the IWQI values revealed that 33% of samples were severely restricted for irrigation, while 67% of samples varied from moderate to high restriction for irrigation, indicating that crops that are moderately to highly hypersensitive to salt should be watered in soft soils without any compressed layers. Two-machine learning models were applied, i.e., the ANN and GBR for IWQI, and the ANN model, which surpassed the GBR model. The findings showed that ANN-2F had the highest correlation between IWQI and exceptional features, making it the most accurate prediction model. For example, this model has two qualities that are critical for the IWQI prediction. The outputs’ R2 values for the training and validation sets are 0.973 (RMSE = 2.492) and 0.958 (RMSE = 2.175), respectively. Finally, the application of physicochemical parameters and water quality indices supported by GIS methods, machine learning, and multivariate modeling is a useful and practical strategy for evaluating the quality and development of groundwater.

This study aims to analyze the chemical composition of Lioua’s groundwater in order to determine the geological processes influencing the composition and origin of its chemical elements. Therefore, chemometrics techniques, such as multivariate statistical analysis (MSA) and time series methods (TSM) are used. Indeed, MSA includes a component analysis (PCA) and a cluster analysis (CA), while autocorrelation analysis (AA), supplemented by a simple spectral density analysis (SDA), is used for the TMS. PCA displays three main factors explaining a total variance (TV) of 85.01 %. Factors 1, 2, and 3 are 68.72%, 11.96%, and 8.89 % of TV, respectively. In the CA, total dissolved solids (TDS) and electrical conductivity (EC) controlled three groups. The elements SO42−, K+, and Ca2+ are closely related to TDS, the elements Na+, Cl−, and Mg2+ are closely related to CE, while HCO3− and NO3− indicate the dissociation of other chemical elements. AA shows a linear interrelationship of EC, Mg2+, Na+, K+, Cl−, and SO42−. However, NO3− and HCO3− indicate uncorrelated characteristics with other parameters. For SDA, the correlograms of Mg2+, Na+, K+, Cl−, and SO42− have a similar trend with EC. Nonetheless, pH, Ca2+, HCO3− and NO3− exhibit multiple peaks related to the presence of several distinct cyclic mechanisms. Using these techniques, the authors were able to draw the following conclusion: the geochemical processes impacting the chemical composition are (i) dissolution of evaporated mineral deposits, (ii) water–rock interaction, and (iii) evaporation process. In addition, the groundwater exhibits two bipolar characteristics, one recorded with negative and positive charges on pH and Ca+ and another recorded only with negative charges on HCO3− and NO3−. On the other hand, SO42−, K+, Ca2+, and TDS are the major predominant elements in the groundwater’s chemical composition. Chloride presence mainly increases the electrical conductivity of water. The lithological factor is dominant in the overall mineralization of the Plio Quaternary surface aquifer waters. The origins of HCO3− and NO3− are as follows: HCO3− has a carbonate origin, whereas NO3– has an anthropogenic origin. The salinity was affected by Mg2+, SO42−, Cl−, Na+, K+, and EC. Ca2+, HCO3−, and NO3− result from human activity such as the usage of fertilizers, the carbonate facies outcrops, and domestic sewage.

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This paper addresses the issues in making wood–concrete composites more resilient to environmental conditions and to improve their compressive strength. Tests were carried out on cubic specimens of 10 × 10 × 10 cm3 composed of ordinary concrete with a 2% redwood- and hardwood-chip dosage. Superficial treatments of cement and lime were applied to the wood chips. All specimens were kept for 28 days in the open air and for 12 months in: the open air, drinking water, seawater, and an oven. Consequently, the compressive strength of ordinary concrete is approximately 37.1 MPa. After 365 days of exposure to the open air, drinking water, seawater, and the oven, a resistance loss of 35.84, 36.06, 42.85, and 52.30% were observed, respectively. In all environments investigated, the untreated wood composite concrete’s resistance decreased significantly, while the cement/lime treatment of the wood enhanced them. However, only 15.5 MPa and 14.6 MPa were attained after the first 28 days in the cases of the redwood and the hardwood treated with lime. These findings indicate that the resistance of wood–concrete composites depends on the type of wood used. Treating wood chips with cement is a potential method for making these materials resistant in conservation situations determined by the cement’s chemical composition. The current study has implications for researchers and practitioners for further understanding the impact of these eco-friendly concretes in the construction industry.

Glass fibers slowly dissolve and age when exposed to water molecules. This phenomenon also occurs when glass fibers are inside fiber-reinforced composites protected by the matrix. This environmental aging results in the deterioration of the mechanical properties of the composite. In structural applications, GFRPs are continuously exposed to water environments for decades (typically, the design lifetime is around 25 years or even more). During their lifetime, these materials are affected by various temperatures, pH (acidity) levels, mechanical loads, and the synergy of these factors. The rate of the degradation process depends on the nature of the glass, sizing, fiber orientation, and environmental factors such as acidity, temperature, and mechanical stress. In this work, the degradation of typical industrial-grade R-glass fibers inside an epoxy fiber-reinforced composite was studied experimentally and computationally. A Dissolving Cylinder Zero-Order Kinetic (DCZOK) model was applied and could describe the long-term dissolution of glass composites, considering the influence of fiber orientation (hoop vs. transverse), pH (1.7, 4.0, 5.7, 7.0, and 10.0), and temperature (20, 40, 60, and 80 °C). The limitations of the DCZOK model and the effects of sizing protection, the accumulation of degradation products inside the composite, and water availability were investigated. Dissolution was experimentally measured using ICP-MS. As in the case of the fibers, for GFRPs, the temperature showed an Arrhenius-type influence on the kinetics, increasing the rate of dissolution exponentially with increasing temperature. Similar to fibers, GFRPs showed a hyperbolic dependence on pH. The model was able to capture all of these effects, and the limitations were addressed. The significance of the study is the contribution to a better understanding of mass loss and dissolution modeling in GFRPs, which is linked to the deterioration of the mechanical properties of GFRPs. This link should be further investigated experimentally and computationally.