Resumo em Inglês:

Abstract In Brazil, many cities have challenges in managing road infrastructure. Pavement management is an important aspect of municipal public administration, since it guides strategic planning, organizes processes, and sets goals to optimize available resources for ensuring service quality and city dynamics. However, there is a lack of studies focusing on the context of large cities. This study aimed to fill this gap by proposing a model for urban pavement management in large cities, using Belo Horizonte, in Minas Gerais, Brazil, as a case study. Based on the history of pavement maintenance between 2009 and 2021, the methodology characterized the municipal road network and maintenance actions practiced, enabling a comprehensive analysis of the maintenance diagnosis, planning, and operationalization steps. The results showed the life cycle of pavements based on their functional evaluation and respective road classification. Guidelines were presented for maintenance and rehabilitation strategies for each stage of deterioration.

Resumo em Inglês:

Abstract Predicting the pile’s bearing capacity still represents a significant challenge in Geotechnical Engineering, since it requires the knowledge of soil parameters and the soil-pile interaction mechanism during and after foundation execution. This study developed a model based on multilayer perceptron Artificial Neural Networks (ANN) with the Levenberg-Marquardt training algorithm to predict the bearing capacity of driven and bored piles. For this purpose, results from Standard Penetration Tests (SPT) and static load tests performed on 343 piles were compiled. The input variables adopted for the model were: pile diameter and length, penetration resistance (NSPT), and soil type. Different models were then trained and validated with the aid of MATLAB® software. Comparison of the results obtained allowed the selection of the best-performing neural model. The coefficient of determination (R2) and the root mean square error (RMSE) in the validation phase were 0.96 and 0.02, respectively. These values were satisfactory, given the phenomenon’s complexity. The case study, which sought to analyze the generalization capacity and applicability of the neural model, showed that the model presented a better performance than the semi-empirical methods of Aoki-Velloso (1975) and Décourt-Quaresma (1978). These results suggest that the multilayer perceptron ANN is a promising tool for predicting the bearing capacity of piles. This work contributes by providing a neural model trained with a comprehensive dataset representative of diverse soil conditions in Brazil, covering both driven and bored piles, and by making its parameters available for practical application in geotechnical design, a step often overlooked in previous studies.

Resumo em Inglês:

Abstract Recent research has increasingly focused on the inelastic behavior of steel frames under severe dynamic forces, which can lead to plastic deformation, damage accumulation, and potential collapse. In such conditions, structures must dissipate significant energy - a property facilitated by steel’s ductility, enabling large deformations and force redistribution beyond yield limits. This study evaluates the nonlinear dynamic response of planar steel frames, particularly highlighting the role of steel-yielding in inducing hysteretic damping. The analysis employs an elastoplastic material model with plasticity monitored using the refined plastic-hinge method (RPHM). The model incorporates residual stresses, cross-sectional plastic resistance, and second-order effects from large displacements and rotations. The numerical approach integrates the finite element method (FEM) with the Newmark scheme and Newton-Raphson iterations, providing insights into material nonlinearity, energy dissipation, and structural stability under dynamic excitation. Numerical simulations of two steel frames clearly illustrate the influence of hysteretic damping caused by material yielding, demonstrating its critical impact on dynamic structural response.

Resumo em Inglês:

Abstract This article presents a numerical analysis of large deflections (elastica) in prismatic and non-prismatic cantilever beams and columns subjected to either concentrated or uniformly distributed loads, accounting for axial deformation. A mathematical model is derived, consisting of three coupled differential equations that govern the position of the member in its deformed configuration. The nonlinear boundary value problem is solved using an accurate numerical procedure based on the Dormand-Prince Runge-Kutta method, combined with the shooting method. Highly accurate tabulated results are presented for various taper ratios (ranging from 0.2 to 1.0) of a rectangular cross-section with linearly varying height, including free-end positions, buckling loads, and post-buckling behavior. Differences in the responses are observed when axial deformation is neglected, particularly in members with predominantly axial loading, varying according to the taper ratio. When axial deformation is neglected, the results show good agreement with available analytical and numerical solutions, demonstrating the accuracy of the proposed approach.

Resumo em Inglês:

Abstract In recent years, military conflicts between major phosphorus suppliers have disrupted the global fertilizer distribution chain. This scenario highlights the critical need for precise density measurements in phosphate mining to improve reserve estimation and reconciliation. In Brazil, formation density is traditionally estimated from drill cores, using hydrostatic balance methods, which can be time-consuming and less reliable for poorly consolidated rocks. Gamma-gamma geophysical logging emerges as a more effective alternative, offering high-resolution density data with rapid acquisition. The use of these nuclear tools requires some special care, such as data acquisition from the inside of boreholes with steel casing, essential for operational safety. However, this procedure introduces measurement changes in the density log readings. This study aimed to comprehend the main factors involved in these gamma-gamma readings through direct comparisons between logging records made in holes with and without casing. In this way, it was possible to suggest a workflow to correct density readings when influenced by the presence of steel casing. Data from four boreholes drilled in the Tapira Mining Complex, Minas Gerais (Brazil) were used. The correction creates synthetic density records that present good correspondence with measurements from uncased boreholes while being supported by borehole diameter control from caliper records. Finally, the results show that gamma-gamma density logging, applied to cased holes, can be a reliable and efficient method for density measurement in phosphate deposits, especially in friable ore zones.

Resumo em Inglês:

Abstract The pumping of iron ore slurry through long-distance pipelines requires reliable prediction of energy losses to ensure operational efficiency and maintenance planning. This study develops a predictive model for energy loss by integrating real operational data with statistical validation. Operational data, including slurry properties and flow parameters, were collected over seven months and used to develop a linear regression model, which identified flow rate as the most critical variable, with a Pearson correlation coefficient of 0.735. The model was validated using 12 months of field data from 2022, confirming its ability to detect trends of increasing pressure drop and to anticipate pig-cleaning operations. The application of this monitoring method led to a statistically significant increase in the average flow rate of 11.98 m3/h after pigging campaigns. This operational improvement resulted in an estimated production gain of 13,299 dry metric tons per month. Although the resulting equation is specific to Samarco’s Pipeline 2, the methodology offers a practical and replicable framework for transforming historical data into a proactive tool for maintenance and operational optimization in other slurry pipelines, enhancing efficiency and supporting predictive decision-making.

Resumo em Inglês:

Abstract Depending on the fuel and process conditions, nitrogen oxides (NOx) are important air pollutants generated by combustion processes when using ambient air (which contains N2) or nitrogen-containing fuels. The iron and steel industry, particularly iron ore pelletization, is a significant NOx emitter. Although NOx abatement has been widely applied in various industries, the straight grate pelletization process remains under-researched despite its global relevance. This study systematically reviews the technical publications on NOx reduction techniques applicable to iron ore pelletization, aiming to identify practical and scalable solutions for industrial implementation. Using 19 Boolean search queries across two major academic databases, we retrieved 626 articles, ultimately selecting 56 for in-depth analysis.The acquired data revealed that coal (39.29%) was the most used fuel, while natural gas (1.79%), a cleaner alternative, was rarely utilized. Selective Non-Catalytic Reduction (SNCR) emerged more often (21.43%) than Selective Catalytic Reduction (SCR), and ammonia was the predominant reducing agent (68.42%). Strategies, such as reducing peak combustion temperature (48.21%), also appeared regularly. Regionally, China (33.33%), the United Kingdom (16.67%), and South Korea (12.50%) led research output. These findings emphasize the significant gaps-especially the lack of focus on natural gas and the straight grate pelletization process-while highlighting to promising avenues for innovation. By consolidating current knowledge and practices, this review provides a foundation for future research and industrial strategies aimed at achieving cleaner, more sustainable iron ore pelletization technologies.

Resumo em Inglês:

Abstract AISI 52100 steel is an alloy steel with a high carbon and chromium content, widely used in industry to produce bearings. Through high energy ball milling, it is possible to obtain powders in submicrometer scale for later use in a new product using powder metallurgy. In this study, powders of AISI 52100 steel with a 3% alumina addition were obtained from scraps of this steel, through high energy ball milling. Parameters of speed (200, 300 and 400 rpm), ball to powder ratio (10:1, 15:1 and 20:1) and time (10, 20 and 30 hours) were varied to determine the best milling condition for the AISI 52100 steel with alumina. Particle size analysis through scanning electron microscopy was used to identify the ideal parameters of time, milling speed and ball to powder ratio. The results obtained indicated that the ball to powder ratio was the parameter that most affected the particle size.

Resumo em Inglês:

Abstract Geotechnical properties vary spatially owing to their mineralogical composition, stress history, and deposition processes, even within homogeneous soil layers. This high degree of variability imposes considerable limitations on the calculation and simulation of models based on deterministic parameters extracted from the field. Mine tailings exemplify this scenario, where the difficulty of sample extraction and laboratory characterization further complicates the challenges. Consequently, field investigation becomes a crucial factor in determining the behavior to be used in geomechanical models. To address this issue, this study focuses on quantifying statistical parameters, including the mean, standard deviation, probability density function, and fluctuation scale-scarce in the literature-derived from direct measurements of piezocone tests and related strength parameters. The results indicate that despite the high variability of the deposit, after a careful profile evaluation considering the characteristic behavior of coarse and fine materials, it was feasible to evaluate the adherence of both normal and lognormal distributions for strength parameters. The fluctuation scales also show high dispersion, ranging from 0.1 to 3.3 meters. This research contributes to a comprehensive understanding of the spatial variability in mine tailings and provides practical insights for future applications.

Resumo em Inglês:

Abstract This study investigated how technological advancement in the geotechnical field contributes to ensuring safety in idle mine pits. A qualitative research approach was used to analyze relevant literature on geotechnical safety in the continuous monitoring of idle mine pits. The study highlighted the importance of combining geotechnical principles-such as slope stability analysis and the behavior of soils and rocks-with advanced monitoring technologies, including inclinometers, piezometers, extensometers, water level indicators, topographic prisms, drones, and remote sensing. It also emphasized the implementation of risk management strategies and early warning systems to provide real-time information and allow for quick responses to signs of instability. The continuous training of professionals and effective communication between all stakeholders are crucial for the effectiveness of safety strategies. We concluded that the integrated application of these practices is essential to ensure the stability of mine pits, protect nearby communities, and mitigate geotechnical risks.