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Emparanza, A. R., Kampmann, R., Caso, F. D., Morales, C., & Nanni, A. (2022). Durability assessment of GFRP rebars in marine environments. Construction and Building Materials, 329, 127028.
Abstract: Technologies developed over the last two decades have facilitated the use of glass fiber reinforced polymer (GFRP) bars as internal reinforcement for concrete structures, specially in coastal environments, mainly due to their corrosion resistance. To-date, most durability studies have focused on a single mechanical parameter (tensile strength) and a single aging environment (exposure to high alkalinity). However, knowledge gaps exists in understanding how other mechanical parameters and relevant conditioning environments may affect the durability of GFRP bars. To this end, this study assesses the durability for different physio-mechanical properties of GFRP rebars, post exposure to accelerated conditioning in seawater. Six different GFRP rebar types were submerged in seawater tanks, at various temperatures (23°C, 40°C and 60°C) for different time periods (60, 120, 210 and 365 days). In total six different physio-mechanical properties were assessed, including: tensile strength, E-modulus, transverse and horizontal shear strength, micro-structural composition and lastly, bond strength. It was inferred that rebars with high moisture absorption resulted in poor durability, in that it affected mainly the tensile strength. Based on the Arrhenius model, at 23°C all the rebars that met the acceptance criteria by ASTM D7957 are expected to retain 85% of the tensile strength capacity.
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Benites Lazaro, L. L., Bellezoni, R., Puppim de Oliveira, J., Jacobi, P. R., & Giatti, L. (2022). Ten Years of Research on the Water-Energy-Food Nexus: An Analysis of Topics Evolution. Frontiers in Water, 4.
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Illgen, M., & Ackermann, H. (2019). In S. Köster, M. Reese, & J. ’e Zuo (Eds.), Urban Flood Prevention: Technical and Institutional Aspects from Chinese and German Perspective (pp. 173–193). Cham: Springer International Publishing.
Abstract: Today’s cities face the challenge of climate change adaptation worldwide. In this context, prevention of damage caused by flash floods plays an important role. This requires a cooperative pluvial flood risk management approach, which includes planning, technical, and administrative measures and involves preliminary flood risk analyses. This article outlines the main components of this risk management approach, which has proven its effectiveness in Europe. The recommendations formulated for this purpose are applicable or adaptable to regions with other constraints, such as China, for example.
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Röttcher, K. (2018). In A. Michalke, M. Rambke, & S. Zeranski (Eds.), Risikomanagement und Nachhaltigkeit in der Wasserwirtschaft: Erfolgreiche Navigation durch die Komplexität und Dynamik des Risikos (pp. 165–174). Wiesbaden: Springer Fachmedien Wiesbaden.
Abstract: Im vorliegenden Beitrag werden beispielhaft unterschiedliche Ansätze des Risikomanagements und das Verständnis von Nachhaltigkeit in der Wasserwirtschaft dargelegt. Die Darstellung richtet sich insbesondere an Leser aus anderen Fachdisziplinen, wie das Rechts- und Finanzwesen, den Fahrzeug- und Maschinenbau oder auch die sozialen Berufe. Die Zusammenhänge werden überblicksartig mit einzelnen konkreten Beispielen dargestellt mit dem Fokus auf die grundsätzlichen Denk- und Vorgehensweisen.
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Stone, A. E. C., & Edmunds, W. M. (2014). Naturally-high nitrate in unsaturated zone sand dunes above the Stampriet Basin, Namibia. Journal of Arid Environments, 105, 41–51.
Abstract: Elevated groundwater nitrate levels are common in drylands, often in excess of WHO guidelines, with concern for human and animal health. In light of recent attempts to identify nitrate sources in the Kalahari this paper presents the first unsaturated zone (USZ) nitrate profiles and recharge rate estimates for the important transboundary Stampriet Basin, alongside the first rainfall chemistry records. Elevated subsurface nitrate reaches 100–250 and 250–525 mg/L NO3–N, with NO3–N/Cl of 4–12, indicating input above evapotranspiration. Chloride mass balance recharge rates range from 4 to 27 mm/y, indicating a vertical movement of these nitrate pulses toward the water table over multi-decadal timescales. These profiles are sampled from dune crests, away from high concentrations of animals and without termite mounds. Given low-density animal grazing is unlikely to contribute consistent spot-scale nitrate over decades, these profiles give an initial estimate of naturally-produced concentrations. This insight is important for the management of the Stampriet Basin and wider Kalahari groundwater. This study expands our knowledge about elevated nitrate in dryland USZs, demonstrating that it can occur as pulses, probably in response to transient vegetation cover and that it is not limited to long-residence time USZs with very limited downward moisture flux (recharge).
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Aderemi, B. A., Olwal, T. O., Ndambuki, J. M., & Rwanga, S. S. (2023). Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa. Systems and Soft Computing, 5, 200049.
Abstract: The crucial role which groundwater resource plays in our environment and the overall well-being of all living things can not be underestimated. Nonetheless, mismanagement of resources, over-exploitation, inadequate supply of surface water and pollution have led to severe drought and an overall drop in groundwater resources’ levels over the past decades. To address this, a groundwater flow model and several mathematical data-driven models have been developed for forecasting groundwater levels. However, there is a problem of unavailability and scarcity of the on-site input data needed by the data-driven models to forecast the groundwater level. Furthermore, as a result of the dynamics and stochastic characteristics of groundwater, there is a need for an appropriate, accurate and reliable forecasting model to solve these challenges. Over the years, the broad application of Machine Learning (ML) and Artificial Intelligence (AI) models are gaining attraction as an alternative solution for forecasting groundwater levels. Against this background, this article provides an overview of forecasting methods for predicting groundwater levels. Also, this article uses ML models such as Regressions Models, Deep Auto-Regressive models, and Nonlinear Autoregressive Neural Networks with External Input (NARX) to forecast groundwater levels using the groundwater region 10 at Karst belt in South Africa as a case study. This was done using Python Mx. Version 1.9.1., and MATLAB R2022a machine learning toolboxes. Moreover, the Coefficient of Determination (R2);, Root Mean Square Error (RMSE), Mutual Information gain, Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and the Mean Absolute Scaled Error (MASE)) models were the forecasting statistical performance metrics used to assess the predictive performance of these models. The results obtained showed that NARX and Support Vector Machine (SVM) have higher performance metrics and accuracy compared to other models used in this study.
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Wolfe, P. (1959). The Simplex Method For Quadratic Programming. Econometrica, 27, 170.
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United Nations. (1998). Stampriet Transboundary Aquifer System Assessment: governance of Groundwater resources in Transboundary Aquifers (GGRETA), phase 1: technical report.
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Krüger, N., Külls, C., Bruggeman, A., Eliades, M., Christophi, C., Rigas, M., et al. (2020). Groundwater recharge estimates with soil isotope profiles-is there a bias on coarse-grained hillslopes? In EGU General Assembly Conference Abstracts (9840).
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Tziritis, E., Aschonitis, V., Balacco, G., Daras, P., Doulgeris, C., Fidelibus, M. D., et al. (2020). MEDSAL Project-Salinization of critical groundwater reserves in coastal Mediterranean areas: Identification, risk assessment and sustainable management with the use of integrated modelling and smart ICT tools. In EGU General Assembly Conference Abstracts (2326).
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