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A spillway is a significant structure that releases the exceeded water during extreme flood events. It is considered as a safety valve during dams operating. Spillway location at dam body is one of the most important requirements of dam design. Different locations may influence the flow patterns which lead to change in the hydraulic performance of the spillway. In this paper, a numerical model was presented utilizing the Ansys fluent software using the realizable k-ɛ turbulence model to investigate the flow patterns of Mandali Dam's spillway at different locations. The interaction between air and water phases was simulated by using volume of fluid (VOF) model. Moreover, cavitation damage was studied for different flow discharges values. the validation of the numerical model was executed in compression with the measurement of the physical model. A discharge of 1800 m3/s was utilized for validation, while five different discharges; 1800, 1300, 1040, 440, and 67 m3/s were employed to investigate the hydraulic properties. The results show an agreement between numerical model and the physical model. According to the hydraulic properties, the spillway location at the center of the dam is better than its location at the edge of the dam. The discharge coefficient value in scenario one was found to be closer to the physical model value compared to the second scenario. At discharge 1800 and 1300, the discharge coefficient was 2.02 and 1.72, while in scenario two it was 1.46 and 1.21 in comparison with physical model which was 2.06 and 1.99. For cavitation investigations, the numerical model shows that the spillway is safe with no cavitation effects for the whole applied discharge values. [ABSTRACT FROM AUTHOR]

Water is the most basic need for humans and a source of livelihood for humans. Lack of human awareness to maintain water quality, causing water to become polluted, by both industrial and household waste, impacts on human health and material loss. Thus, it is important to create technology that can monitor water pollution automatically and quickly. This research aims to create a system which utilizes the Internet of Things (IoT) technology that can facilitate quality of water by measuring parameters such as pH and turbidity. The methodology of the system progresses by the usage of a controller which is ATmega328P-AU, pH sensor to measure acidity, turbidity sensor to measure turbidity level, LPWAN LoRa works like a communication of data transmission as well as cloud service, namely Antares, to store data that are sent via Android. Based on the outcomes, the proposed system has achieved a reliable accuracy with percentage error of 99.73% in pH sensor and 99.41% in the turbidity sensor. Also, 2.6 s is the average required time to deliver the results to the cloud service. [ABSTRACT FROM AUTHOR]

The objective of this review article is to analyze published data encompassing compressive strength, tensile strength, elastic modulus, and flexural strength, as well as the utilization of scanning electron microscopy (SEM), energy dispersive x-ray spectroscopy (EDS), and x-ray diffraction (XRD) for Ultra High-Performance Geopolymer Concrete (UHP-GC), with the focus of establishing the current research trends regarding its mechanical, microstructural, and chemical characteristics. After a critical evaluation of the published data from the literature findings, it became evident that UHP-GC can attain a remarkably high level of engineering performance. In UHP-GC, the optimum percentage of silica fume as a slag partial replacement to achieve high compression, tensile, and elastic modulus were traced to be 25, 30, and 35%, respectively. The optimum ratio of sodium silicate to sodium hydroxide and sodium hydroxide molarity for UHP-GC were identified to be 3.5 and 16, respectively. All in all, the review provides a thorough understanding of the review gap and distinct functions of different raw materials in decreasing porosity and enhancing the formation of geopolymeric gels that not only bond but also strengthen UHP-GC. UHP-GC stands as an energy-saving material in concrete technology, poised to forge a path towards a sustainable future for the building sector. [ABSTRACT FROM AUTHOR]

Dam reservoir operations are a critical issue for decision-makers in maximizing the use of water resources. Artificial Intelligence and Machine Learning models (AI & ML) approaches are increasingly popular for reservoir inflow predictions. In this study, the multilayer perceptron neural network (MLP), Support Vector Regression (SVR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and the Extreme Gradient Boosting (XG-Boost), were adopted to forecast reservoir inflows for the monthly and daily timeframes. Results showed that: (1) For the monthly timeframe, all the four models were proficient in obtaining efficient monthly reservoir inflows by scoring at least an R² of 0.5; with the XG-Boost ranked as the best model, followed by the MLPNN, SVR, and lastly ANFIS. (2) the XG-Boost still outperforms all other models for forecasting daily inflow; but however, with reduced performance. The models were still ranked in the same order, with the ANFIS showing very poor performance in scenario-2, scenario-3, and scenario-4. (3) For daily inflows, the best scenarios are scenario-5, scenario-6, scenario-7 as the models were trained based on the 1,3,5, days-lag forecasted inflow, and overall, the XG-Boost outperforms all the other models. [ABSTRACT FROM AUTHOR]