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Uncertainty Quantification and Sensitivity Analysis of Concrete Structure Using Multi-Linear Regression Technique
Get PDFThe dynamic analysis of structures with uncertain parameters presents an attractive field of structural health monitoring in many cases of technological interest. In the dynamic analysis of hydraulic structures, such as existing dams, modeling assumptions, resulting inaccuracies, and changes in seismic loading are typically the main sources of uncertainties. Many hydraulic structures of concrete can be subjected to seismic loads. However, it is necessary to take haphazard or random phenomena as crucial considerations when assessing the security of these structures or planning new ones. This paper shows computational analysis for the characterization of the behavior of a concrete gravity dam under seismic loads, which are considered sources of uncertainties. The multi-linear regression methodology was performed and applied to evaluate the dynamic response of the considered structure. Numerous nonlinear time history analyses based on Latin Hypercube Sampling were realized to investigate the effect of uncertain parameters on the dynamic response. These analyses were applied to two types of seismic actions, the near and far earthquakes, which act on a concrete gravity dam. Then, a sensitivity analysis was used for each random variable to quantify its risk and clarify its influence on the dynamic behavior of the dam. Results divulge that for near-fault cases, major variables affecting the global sensitivity across all limit states are the Young’s modulus of soil and concrete. On the other hand, for far-fault cases, the important variables influencing the global sensitivity index include the compressive strength of concrete, Young’s modulus of soil, and cohesion
Performances improvement of DC Motor using a Fractional Order Adaptive PID Controller optimized by Genetic Algorithm
Get PDFIn the past 20 years, scientists and engineers have rediscovered fractional calculus and have begun using it in more and more domains, most notably control theory. This study introduces a fractional adaptive PID (FAPID) controller which incorporates an additional parameter to enhance the performance of a conventional adaptive PID (APID) controller. A comparative analysis is conducted between the APID and FAPID controllers optimized using the metaheuristic Genetic Algorithm (GA). The evaluation uses a linearized model of the DC motor control system. The results demonstrate that FAPID controllers significantly outperform conventional APID controllers, particularly regarding rise time, settling time, overshoot, and mean absolute error. Among the proposed designs, the integration of FAPID proves to be the most effective in achieving a balance between responsiveness and stability, exhibiting exceptional robustness and adaptability to variations in DC motor and environmental conditions. This method can be extended to various fractional and integer systems to enhance their efficiency and reduce noise disturbance
Optimization of Operational Parameters to Minimize Cutting Energy during Kenaf Harvesting
Get PDFThis study evaluated the cutting and specific cutting energy of different kenaf varieties at different maturity stages during mechanical harvesting, while evaluating an improved kenaf harvester. The effects of operation parameters (related to the crop\u27s biological properties; crop varieties "Cuba 108,” "Ifeken 400,” and "Ifeken Di 400"; and crop maturity: 10 to 16 weeks after planting) on the machine performance were optimized in a 3 × 4 factorial experiment using randomized response surface methodology (optimal custom design) to optimize the energy consumption. The results showed that the cutting energy required for harvesting kenaf increased from 1.8 to 3.3 joules as the crop matured from 10 to 16 weeks after planting (WAP). Among the tested crop varieties, "Ifeken 400" consistently required the highest cutting energy, followed by "Cuba 108", while "Ifeken Di 400" required the least energy. The specific cutting energy increased from 5000 to 11661 J.m⁻² as the crop matured, indicating differences in energy demand among the kenaf cultivars. These findings imply that the physical and mechanical properties of kenaf directly affect its stiffness. The stiffness factor of the crop is determined by crop maturity (weeks after planting) and crop variety. Therefore, the proper estimation of these operational parameters is vital for increasing the energy efficiency during the harvesting of kenaf
Thermo-Mechanical Assessment of Bio-based Insulating Material Using Phase Change Materials and Date Palm Fibers
Get PDFTo mitigate extreme temperature fluctuations in arid southern Algerian cities, developing an internal thermal environment that is either independent of or only marginally influenced by external conditions is a viable solution. In this study, we successfully created a eutectic mixture of animal and plant fatty acids, excluding petroleum sources, consisting of 70% myristic acid (MA) and 30% stearic acid (SA). This phase change material (PCM) was then impregnated into date palm fiber waste (DPF) using a vacuum technique. The melting points of both the eutectic mixture and the impregnated date palm fiber were measured at 35°C and 34.5°C, respectively. Clay bricks, which are widely used in Algerian construction, were prepared with 75% dune sand and 10% lime. Date palm fibers impregnated with PCM were added in varying proportions (0.5%, 1%, 1.5%, 2%) to test the mechanical and thermal properties of the bricks. The results showed an improvement in thermal insulation, with a reduction in thermal conductivity by 11% for bricks containing 2% impregnated palm fibers. The compressive strength of these bricks remained within acceptable limits, regardless of whether the PCM was in a solid or liquid state. Numerical simulations showed that adding MA-SA/DPF to clay bricks contributed to a 30% reduction in outward heat flow in winter and a 25% reduction in inward heat flow in summer. This, in turn, resulted in a corresponding decrease in energy consumption
Modeling A Reverse Osmosis Desalination Plant: A Practical Framework Using Wave Software
Get PDFSeawater desalination is a highly successful and effective method of obtaining fresh water from saline water sources. Reverse osmosis (RO) is a key and pivotal technology in seawater desalination as it produces high-quality freshwater from seawater with low energy consumption, in comparison to alternative technologies. However, the practical modelling of a comprehensive full-scale RO system is challenging due to fluctuating operating conditions stemming from seasonal variations and progressive fouling of the membrane during prolonged filtration operation. This study presents a comprehensive modeling framework for a seawater reverse osmosis (SWRO) desalination plant using DuPont’s Water Application Value Engine (WAVE) software. The modeled system integrates ultrafiltration (UF) for pretreatment and ion exchange (IX) polishing for post-treatment, which reflects the actual operational structure of the Victoria & Alfred Waterfront desalination plant in Cape Town, South Africa. The model simulates the hydraulic and separation performance under steady-state conditions, using plant-specific data for feed salinity, pressure, flow rates, and membrane configuration. Results demonstrate the WAVE model’s capability to accurately predict key performance parameters, including permeate flow, energy consumption, recovery rate, and total dissolved solids (TDS) removal. Simulated results indicate improved recovery (45.7% vs. 31%) and reduced specific energy consumption (5.91 kWh/m³ vs. 6.58 kWh/m³) compared to actual plant data. The study validates the model\u27s predictive accuracy and highlights its application in optimizing system design, minimizing operational costs, and guiding future desalination infrastructure development under varying operational conditions
Advances in the Development, Processing, and Application of Locally Sourced Clay-based Refractory Materials for Furnace Application in Nigeria
Get PDFRefractories are ceramic materials that are used in high-temperature applications, often above 1100oC. These materials find applications in reactors, kilns, ovens, and furnace linings. The need for refractory materials is consistently increasing to accommodate the ongoing development of different industries and factories. Local clays are currently under investigation to address the insufficient supply of refractory materials. Although the geo-morphology of the various locations where deposits of clay are found has been examined, comparatively little work has been done to fully evaluate these deposits to ascertain their suitability for use. This paper includes a review of the refractories produced from locally sourced clay deposits in Nigeria, followed by an evaluation of their usability for furnace lining. Generally, most of the findings reiterate the fact that the local clay has excellent properties for refractories; however, the local manufacturing of refractories in Nigeria has been very low, hence the nation still depends largely on imported refractories. Adequate funding for the local manufacturing of refractories would be needed to access and explore about 8 billion tonnes of clay deposits across Nigeria, thereby reducing imports and maximizing local production
Optimal Placement and Sizing of Renewable Distributed Generators for Power Loss Reduction in Microgrid using Swarm Intelligence and Bio-inspired Algorithms
Get PDFTo responsibly fulfill the world\u27s expanding electrical energy needs, renewable energy sources are now essential. Future energy policies must include these sources—like solar and wind energy—because they lower carbon emissions and save the environment. The optimal location and sizing of renewable distributed generators (OLSRDG) in the microgrid are determined in this study by applying one of the universal bio-inspired techniques and one of the swarms’ algorithms. With lower power losses, an improved voltage profile, increased dependability, and stability, the goal is to improve energy efficiency and lessen reliance on the main grid while also enhancing the grid\u27s overall performance and stability. The acquired results are promising and show the efficacy and resilience of the suggested technique in solving OLSRDG problems compared to recently published results. The results showed that the optimization process led to loss reduction, with the percentage of power loss reduction ranging from 45.387% to 73.89% using the PSO. While the percentage of loss reduction using the BAT ranged from 51.78% to 71.57%
An Overview on Blockchain-based Social Media
Get PDFSocial Media (SM) platforms allow users to create and share multimedia content characterized by interaction among the users through profile creation, messaging systems, communities, etc. Blockchains can improve the security and ethical aspects of SM due to their innate security characteristics. We overlook countless blockchain-based SM schemes, where we perceive 9 duties of blockchain-based SM abstraction and dissect them intensively, rooted in SM- and blockchain-linked traits in contrast to existing reviews that do not review in broad scope and lack a critical analysis to show ways to focus on practical implementation. We heaped a preparatory sample of 93 works by sieving the studies for leaching rules and delving into E-repositories by employing a mixed-method systematic review with a narrative synthesis and quality assessment approach. Built upon the scrutiny, in blockchain-based SM, blockchain can assist by providing social media platforms (G1), social DApps (G2), copyright protection (G3), harassment prevention (G4), ensuring privacy and security (G5), fake/vicious news prevention (G6), data/user behavior/event processing and analysis (G7), proper incentivization (G8), and user migration (G9). Critical analysis uncovers that from blockchain-based social media, an overall 40% harness 20% from G6 and G7 each, 82.5% harness conventional blockchain, and 22.5% harness DPoS consensus. Next, we critically analyze the strengths and weaknesses of the reviewed works, focusing on performance and characteristics. Furthermore, we identified lack of empirical validation, under-exploration of ethical concerns and biases in detection models, and non-assessment of decentralization risks as gaps in the study. Eventually, we reveal the opportunities and discomforts of the abstraction of blockchain-based SM, state the study’s limitations, and then bestow solutions to resist them with future directions emphasizing practical implications
Assessment of Refuse Dumpsite Impact on Groundwater Quality: A Case Study from Awotan, Ibadan, Oyo State, Nigeria
Get PDFGroundwater, an essential freshwater resource, is critical to life and supports various industries. Unrestrained urban expansion and inefficient waste management practices endanger groundwater quality. This research examines the groundwater quality and impacts of the Refuse Waste Dumpsite located at Awotan, Ibadan. Additionally, this study provides a framework for assessing dumpsite impacts on groundwater, which can be applied to similar urban contexts worldwide, contributing to global efforts to ensure safe water quality. Samples of residential Well water near the dumpsite were collected to determine their physical, chemical, and bacteriological characteristics. The physical and chemical parameters investigated include Temperature, pH, Electrical conductivity, Nitrate, and Chloride, which were determined using the standard analytical methods. In contrast, bacteriological parameters such as Total Coliform and E.Coli Count were determined. Trace metals such as Pb and Fe were also determined to ascertain the relationship between pollutant levels and distances from the dumpsite and to evaluate compliance with WHO and NSDWQ water quality standards. Regression analysis revealed a strong correlation (R = 0.999, R² = 0.998) between dumpsite distance and chemical water quality, but the results were not statistically significant (p = 0.066). Chemical parameters like alkalinity (p = 0.484) and nitrate (p = 0.338) showed no significant impact from distance. For bacteriological quality, the model was statistically significant (p = 0.009) with an R² of 0.994. Total coliform (p = 0.055) was nearly significant, indicating potential distance-related bacterial contamination. The findings underscore the pressing need for sustainable waste management to safeguard groundwater resources
A Comparison of Machine Learning Algorithms for Predicting Alzheimer’s Disease Using Neuropsychological Data
Get PDFAlzheimer’s disease (AD) is a gradient degeneration of essential cognitive activities such as memory, thinking, and cognition. AD mainly affects elderly individuals and is recognized as the most common cause of dementia. This study investigates the predictive performance of nine supervised machine learning algorithms—Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Machine, Gaussian Naïve Bayes, Multi-Layer Perceptron, eXtreme Gradient Boost, and Gradient Boosting—using neuropsychological assessment data. We applied two classification techniques—binary and multiclass—to classify 1761 subjects into three categories: cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer\u27s disease (AD). Binary classification tasks focused on CNvsAD and CNvsMCI subsets, while multiclass classification used the full dataset (TriClass). Hyperparameter tuning was performed to optimize model performance. The results indicate that ensemble learning models, particularly Gradient Boosting (GB) and Random Forest (RF), exhibited superior accuracy compared to other algorithms. Most models for the CNvsAD subset achieved the highest accuracy (97.74%), while GB achieved the best performance (94.98%) for the CNvsMCI subset. For multiclass classification, RF achieved the highest accuracy at 84.70%. These findings highlight the robustness and efficiency of ensemble learning algorithms, especially in handling complex, non-linear data structures. This study underscores the potential of RF and GB as reliable tools for early detection and classification of Alzheimer’s disease using neuropsychological data