To assess Mars’ potential for both harboring life and providing useable resources for future human exploration, it is of paramount importance to comprehend the water situation on the planet. Therefore, studies have been conducted to determine any evidence of past or present water existence on Mars. While the presence of abundant water on Mars very early in its history is widely accepted, on its modern form, only a fraction of this water can be found, as either ice or locked into the structure of Mars’ plentiful water-rich materials. Water on the planet is evaluated through various evidence such as rocks and minerals, Martian achondrites, low volume transient briny outflows (e.g., dune flows, reactivated gullies, slope streaks, etc.), diurnal shallow soil moisture (e.g., measurements by Curiosity and Phoenix Lander), geomorphic representation (possibly from lakes and river valleys), and groundwater, along with further evidence obtained by probe and rover discoveries. One of the most significant lines of evidence is for an ancient streambed in Gale Crater, implying ancient amounts of “vigorous” water on Mars. Long ago, hospitable conditions for microbial life existed on the surface of Mars, as it was likely periodically wet. However, its current dry surface makes it almost impossible as an appropriate environment for living organisms; therefore, scientists have recognized the planet’s subsurface environments as the best potential locations for exploring life on Mars. As a result, modern research has aimed towards discovering underground water, leading to the discovery of a large amount of underground ice in 2016 by NASA, and a subglacial lake in 2018 by Italian scientists. Nevertheless, the presence of life in Mars’ history is still an open question. In this unifying context, the current review summarizes results from a wide variety of studies and reports related to the history of water on Mars, as well as any related discussions on the possibility of living organism existence on the planet.

The performance of both non-spliced and spliced steel bars significantly affects the overall performance of structural reinforced concrete elements. In this context, the mechanical properties of reinforcement bars (i.e., their ultimate strength and strain) should be determined in order to evaluate their reliability prior to the construction procedure. In this study, the application of Tree-Based machine learning techniques is implemented to analyze the ultimate strain of non-spliced and spliced steel reinforcements. In this regard, a database containing the results of 225 experimental tests was collected based on the research investigations available in peer-reviewed international publications. The database included the mechanical properties of both non-spliced and mechanically spliced bars. For better accuracy, the databases of other splicing methods such as lap and welded-spliced methods were excluded from this research. The database was categorized as two sub-databases: training (85%) and testing (15%) of the developed models. Various effective parameters such as splice technique, steel grade of the bar, diameter of the steel bar, coupler geometry—including length and outer diameter along with the testing temperatures—were defined as the input variables for analyzing the ultimate strain using tree-based approaches including Decision Trees and Random Forest. The predicted outcomes were compared to the actual values and the precision of the prediction models was assessed via performance metrics, along with a Taylor diagram. Based on the reported results, the reliability of the proposed ML-based methods was acceptable (with an R² ≥ 85%) and they were time-saving and cost-effective compared to more complicated, time-consuming, and expensive experimental examinations. More importantly, the models proposed in this study can be further considered as a part of a comprehensive prediction model for estimating the stress-strain behavior of steel bars.

This paper deals with analyzing the structural responses of glass-fiber-reinforced polymer (GFRP) tubes filled with recycled and concrete material for developing composite piles, as an alternative to traditional steel reinforced piles in bridge foundations. The full-scale GFRP composite piles included three structural layers, using a fiber-oriented material that was inclined longitudinally. Almost 60% of the fibers were orientated at 35° from the longitudinal direction of the pile and the rest 40 percent were oriented at 86° from the horizontal axis. The segment between the inner and outer layers was inclined 3° from the hoop direction in the tube. The behavior of the filled GFRP tubes was semi-linear and resulted in increasing the total ductility and strength of the piles. Adjusting the material’s properties, such as the E_Axial, E_Hoop, and Poisson ratios, optimized the results. The lateral strength of the GFRP composite pile and pre-stressed piles are investigated under both axial compression and bending moment loads. Based on the conducted parametric study, the required axial and bending capacities of piles in different ranges of eccentricities can be reached using the combination of tube wall thickness and GFRP fiber percentages.

The total phosphorus (TP) concentration, as the primary limiting eutrophication factor in the Mahabad Dam reservoir in Iran, was studied, considering the combined impacts of climate change, as well as the scenarios on changes in upstream TP loadings and downstream dam water allocations. Downscaled daily projected climate data were obtained from the Beijing Normal University Earth System Model (BNU-ESM) under moderate (RCP4.5) and extreme (RCP8.5) scenarios. These data were used as inputs of a calibrated Soil and Water Assessment Tool (SWAT) model of the watershed in order to determine the effects of climate change on runoff yields in the watershed from 2020 to 2050. The SWAT model was calibrated/validated using the SUFI-2 algorithm in the SWAT Calibration Uncertainties Program (SWAT-CUP). Moreover, to model TP concentration in the reservoir and to investigate the effects of upstream/downstream scenarios, along with forecasted climate-induced changes in streamflow and evaporation rates, the System Dynamics (SD) model was implemented. The scenarios covered a combination of changes in population, agricultural and livestock farming activities, industrialization, water conservation, and pollution control. Relative to the year 2011 in which the water quality data were available, the SD results showed the highest TP concentrations in the reservoir under scenarios in which the inflow to the reservoir had decreased, while the upstream TP loadings and downstream dam water allocations had increased (+29.9%). On the other hand, the lowest TP concentration was observed under scenarios in which upstream TP loadings and dam water allocations had decreased (−18.5%).