EM - Escola de Minas
URI permanente desta comunidadehttp://www.hml.repositorio.ufop.br/handle/123456789/6
Notícias
A Escola de Minas de Ouro Preto foi fundada pelo cientista Claude Henri Gorceix e inaugurada em 12 de outubro de 1876.
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Resultados da Pesquisa
Item Blending Linz–Donawitz and Blast Furnace slags with the Kambara reactor byproduct to improve their reuse in roadworks.(2022) Schumacher, Aécio Guilherme; Gomes, Guilherme José Cunha; Schneider, Denise S. G.; Pires, Patrício José Moreira; Gomes, Ruan Gonçalves de SouzaThe use of industrial byproducts as replacement of natural aggregates has been extensively investigated to design eco-friendly roads. One of the most examined byproducts for this purpose is steel slag. However, existing studies do not explore the blending of different slags to enhance the engineering performance of base layers. The applicability of Linz–Donawitz (LD), Blast Furnace (BF) and Kambara Reactor (KR) steel byproducts is evaluated as a single base layer for rural (unsurfaced) roads in Brazil. A series of laboratory experiments were performed to assess the characteristics of eight soil-byproduct mixtures with 50% and 75% byproduct contents, including new blends of LD/KR and BF/KR slags. Additionally, the most suitable mixture was proposed considering different performance indicators. Results demonstrate the coarse-grained, nonplastic and non-expansive nature of the byproducts, with CBR values higher than 100%. The more byproduct added to soil, the larger is the strength and the lower the expansion. The mixture with 75% of the proposed LD/KR blend and 25% of a clayey soil was considered as optimum, based on a trade-off between engineering properties, environmental impacts, and material costs. Findings and discussions are relevant to reduce waste stockpiles of steel companies, helping engineers and policy makers reuse blended slag byproducts.Item A hybrid multi-step sensitivity-driven evolutionary polynomial regression enables robust model structure selection.(2022) Gomes, Ruan Gonçalves de Souza; Gomes, Guilherme José Cunha; Vrugt, Jasper A.Evolutionary Polynomial Regression (EPR) has found widespread application and use for model structure development in engineering and science. This hybrid evolutionary approach merges real world data and explanatory variables to generate well-structured models in the form of polynomial equations. The simple and transparent models produced by this technique enable us to explore, via sensitivity analysis, the robustness of the derived models. Yet, existing EPR frameworks do not make explicit use of sensitivity analysis in the selection of robust and high-fidelity model structures. In this paper, we develop a multi-step sensitivity-driven method which combines the strengths of differential evolution and model selection via Monte Carlo simulation to explore the input–output relationships of model structures. In the first step, our hybrid approach automatically determines the optimum number of terms of the polynomial equations. In a subsequent step, our algorithm explores the mean parametric response of each explanatory variable used in the mathematical formulation to select a final model structure. Finally, in our selection of the most robust mathematical structure, we take explicit consideration of the prediction uncertainty of the simulated output. We illustrate and evaluate our EPR method for different engineering problems involving modeling and prediction of the moisture content and creep index of soils. Altogether, our results demonstrate that the use of sensitivity analysis as an integral part of model structure search and selection will lead to robust models with high predictive ability.Item Field-scale assessment of the unsaturated hydraulic properties of residual soils in southeastern Brazil.(2022) Gomes, Ruan Gonçalves de Souza; Gomes, Guilherme José Cunha; Vargas Júnior, Eurípedes do Amaral; Genuchten, Martinus Theodorus van; Pinto, João T. M. G.; Rosa, Felipe A.Field tests were carried out to estimate effective unsaturated soil hydraulic properties of layered residual soilsin Rio de Janeiro, southeastern Brazil. Data of this type are important for understanding the initiation of rainstorm-induced soil landslides, which often occur in the state of Rio de Janeiro as well as other areas having similar geologicsettings and climate conditions. Tests were carried out using a simplified field approach, referred to as the MonitoredInfiltration Test, which requires only a tensiometer to measure pressure heads below the wetting front, triggered by flowfrom a Mariotte bottle which maintains a constant pressure at the top edge of the soil profile. The data can then beanalyzed by numerical inversion using the HYDRUS-2D software package. The test is relatively fast since no steady-state flow conditions are needed, and versatile since the test can be carried out quickly on steep slopes with the help of amanual auger. Soil water retention and the unsaturated hydraulic conductivity functions were obtained for a range ofyoung, mature and saprolitic residual soils. The effective hydraulic properties of the distinct residual soil layers can bequite large, reflecting a need to provide a careful analysis of field-scale hydraulic heterogeneity in geotechnical analyses.Item A dual search‐based EPR with self‐adaptive ofspring creation and compromise programming model selection.(2021) Gomes, Guilherme José Cunha; Gomes, Ruan Gonçalves de Souza; Vargas Júnior, Eurípedes do AmaralEvolutionary polynomial regression (EPR) is extensively used in engineering for soil properties modeling. This grey-box technique uses evolutionary computing to produce simple, transparent and well-structured models in the form of polynomial equations that best explain the observed data. A key task is then to determine mathematical structures for modeling physical phenomena and to select the optimal EPR model. This requires an algorithm to search through the model structure space and successfully produce feasible solutions that honor a set of statistical metrics. The complexity of EPR models increases greatly, however, with the number of polynomial terms used to tune these models. In this paper, we propose an alternative EPR for modeling complex soil properties. We implement a dual search-based EPR with self-adaptive ofspring creation as model structure search strategy and couple a compromise programming tool to select a model that is preferred statistically relative to models with diferent polynomial terms. We illustrate our method using real-world data to improve predictions of optimal moisture content and creep index for soils. Our results demonstrate that the models derived using the proposed methodology can predict soil properties with adequate accuracy, physical meaning and lower number of parameters and input variables.