Applied Mechanics and Materials Vol. 929

Abstract: The expansion of mobile networks generates high energy demand, especially in remote areas. In Benin, the use of diesel generators in these areas leads to pollution and energy losses on site in the event of fuel shortages. To solve these problems, the integration of renewable energy sources such as solar in mobile base stations is a promising solution. The objective of this study is to optimize the integration of intermittent renewable sources, powering the base transceiver stations (BTS). This leads to the reduction of CO2 gaze emissions and the reliability of power supply to mobile networks in remote areas. The work is based on optimizing a PV/Diesel/battery hybrid system in terms of energy production to permanently power the telecommunication systems. To achieve this goal, the NSGA-II genetic algorithm is employed, taking into account a non-linear load profile that perfectly reflects the energy demand of a BTS site. The results show that the optimal power system for BTS in Benin is composed of a 12 kW diesel generator and 50 solar panels with a peak power of 540 Wc and 30 modular 48V/150Aah batteries. This system would emit 3571.3282 kg/year CO2 with a Lost Power System Probability (LPSP) equal to 4%. The average monthly consumption of the existing site, which is the subject of this study and operates on a diesel generator, is 1500 liters per month, or 18,000 liters per month. This diesel consumption results in annual pollution of 53460 tons of CO2. With the integration of solar energy in this system, the theoretical results show a consumption of 1202 liters of diesel for a production of greenhouse gases of 3571 tonnes of CO2. We note a significant reduction of about 93% on the consumption of fuel oil and on the production of greenhouse gases.

Abstract: This article presents the results of the suitability assessment and identification of favorable sites for photovoltaic solar power plants connected to the electricity grid in northern Benin. The integration of renewable energies into Benin's energy park constitutes a major challenge for which those in power are seeking an optimal solution. The north of the country has strong solar potential, we based our study on this region in order to evaluate the technical and economic feasibility of photovoltaic power plants. The method adopted consists first of analyzing the opportunity to choose potential sites through the combination of a geographic information system (GIS) and a hierarchical analytical process (AHP) approach. In this process, nine (9) factors were weighted, namely solar irradiation, grid connection infrastructure, topography, land cover and use, surface and soil characteristics, environmental risks, flooding, restricted areas and distances from the road and power grid to determine potential sites. Then thanks to the Technique of Order of Preference by Similarity with the Ideal Solution (TOPSIS), the distances were optimized for the identification of the most favorable sites. The results of the application of the selection criteria applied to the northern zone around the substations of the Beninese Energy Electrical located in Bembereke, Djougou, Kandi, Natitingou and Parakou made it possible to identify 25 potential sites then 15 favorable sites. This hybrid method has the advantage of determining both favorable sites and the capacities of the resulting power plants.

Abstract: With the increasing population and unavailability of plain grounds in some regions, the need for high-rise buildings is increasing day by day. Buildings constructed on sloping terrain are more prone to earthquakes because of their uneven floor plans and elevations. This study examines the seismic behavior of high-rise buildings on both flat and sloping terrains, with a focus on comparing regular and irregular structural configurations. In this study a total of 25 G+15 RCC Step-back buildings with regular and irregular configurations such as Square shape, C-shape, U-shape, L-shape and Cross shape each of which are resting on grounds with sloping angles of 0º, 10º, 20º, 30º and 45º have been made using ETABS software as per IS 1893 (Part 1): 2016. The study employs the Response Spectrum Method to evaluate Storey Displacements, Storey Drifts, Base shears, and Time periods. It is observed that the storey displacement and storey drift decrease as we increase in sloping angle mainly due to curtailment of the columns, and the regular configurations of structures have better seismic performance than irregular configurations.

Abstract: The sustainability and performance of highway infrastructure are vital issues in the current era of rapid urbanization and civilization. The stability of highway embankments needs spatial precaution to maintain good traffic flow and serviceability performance. The lateral sliding and crack formation on the pavement appear due to a lack of confinement against embankments. Conventional retaining structures like gravity walls and reinforced cement concrete retaining walls absorb plastic strains and dissipate in the form of cracks, leading to failure of the structure. Flexibility needs to be introduced in the structure to maintain its strength and shape during the serviceable period. Less explored geocell-retained structures are a good option to maintain flexibility and stress rearrangement in different layers along the structure's height. This research aims to evaluate the structural efficiency and sustainability of the geocell-retained highway embankments as an alternative approach to conventional rigid retaining walls. In the current study, numerical analysis was performed using the Abaqus 2017 version on the geocell retained wall by considering three different shapes (square, hexagonal, and honeycomb) of geocell fiber with aggregates as infill material. Highway cyclic loading (0.5 Hz) and seismic loading (7.5M Kobe earthquake-0.05 Hz) are analyzed respectively. The horizontal stress, displacement, and crest settlement are considered basic parameters to judge geocell efficiency over conventional retaining structures. Shapes like square geocells perform well compared to hexagonal and honeycomb shapes due to more contact area and uniform all-around lateral confinement against instability. This study found that square-shaped geocells provide superior confinement and stability, minimizing crest settlement (1.5 mm under static loading and 0.5 mm under seismic loading) and enabling efficient stress distribution. The findings suggest that geocell-reinforced systems, especially with square cells, offer a much more cost-effective and flexible solution for enhancing the durability and performance of the highway embankments.

Abstract: Maintaining safe pipeline conditions is crucial to ensure sustainable and reliable transportation for energy and water. Pipelines are generally laid underground due to larger transport capacity, rapid construction speed, space restriction, and safety precautions. Nevertheless, they are prone to failures due to mechanical problems, extreme operation, and aggressive surrounding environmental conditions. The usage of machine learning methods to predict buried pipeline failures has risen recently due to its effectiveness in addressing the aforementioned problems. This paper reviews making predictions on different buried pipeline failures by adopting machine learning approaches, particularly artificial neural networks (ANN) and hybrid methods. It highlights the detail of the machine learning algorithms as well as the parameters that were used in the predictive models with concise elaboration. Findings show that the ANN method gives accurate failure prediction, while the hybrid method enhances the prediction accuracy. Nevertheless, there is no single absolute algorithm that can work best to solve all pipeline failures. Finding the most suitable machine learning algorithm for a specific pipeline failure will be a challenge to overcome. This review is expected to give more comprehension to industry players related to machine learning methods as a potential tool to solve various buried pipeline problems. Further, this review may prompt other interested researchers to further discover machine learning potentials and ways to increase its effectiveness.

Abstract: Existing open-source robotic hand designs rarely achieve full human-like functionality, limiting their accessibility and utility. Ale-HAND-ra addresses this gap with a cost-effective, open-source humanoid robotic hand featuring 20 degrees of freedom, developed using filament- and resin-based 3D printing for affordable production (total cost: \$161.54). Grounded in biomechanical analysis, its kinematic model enables precise finger movements and versatile grasping, validated through simulations and tests against human benchmarks. Employing a modular design with servomotors and low-cost materials, Ale-HAND-ra balances functionality and simplicity, achieving a 350-gram grip capacity. Released on GitHub, this prototype fosters collaboration for educational, prosthetic, and robotic applications.

Abstract: Over the past decade, noninvasive brain-computer interfaces (BCIs) leveraging electroencephalography(EEG) to decode gait intention have matured from proof-of-concept studies to nearclinicalimplementations. We systematically reviewed 55 studies (January 2015-April 2024) usingPRISMA guidelines, focusing on neurophysiological markers (MRCPs, ERD/ERS, high-γ), signalprocessing pipelines (artifact suppression, time-frequency transforms), machine-learning classifiers(CSP-SVM, ERD SVM), and deep-learning frameworks (spatio-spectral CNNs, LSTM RNNs). Acrossstudies, median classification accuracy rose from 75% (2015-2018) to 87% (2021-2024), while detectionlatency fell below 200ms. Innovations include enhancing intention detection with emotionevokingmusic stimuli (up to early ERD and improved accuracy), decoding pediatric gait kinematicswith state-space models (r = 0.71 hip, 0.59 knee), and session-to-session transfer learning withoutrecalibration (≤4% performance drop). Challenges remain in artifact mitigation, small sample sizes,and limited multi-centre trials. We propose open, standardized datasets, transfer-learning pipelines,and larger clinical validations to accelerate translation.

Abstract: Sports biomechanics is a specialized discipline that seeks to optimize sports performance through the analysis of biomechanical parameters. However, research conducted in the context of sports in low-resource countries is limited, as specialized systems for these analyzes can be expensive. This study presents a quantitative and descriptive research on the kinematics of the quality of the soccer-pass, by proposing a low-cost system solution using IMU sensors and Kinovea software for the evaluation of the quality of the pass. Inertial sensors were used to collect data on linear acceleration, angular velocity, and orientation of the athlete during the execution of the pass. These sensors were strategically placed on the players' bodies to detect movement during the technical gesture. The results obtained from the combination of Kinovea software and inertial sensors allowed precise kinematic measurements of angles and distances between key anatomical points, providing valuable information on the quality and effectiveness of the movements performed by athletes. Joint angles and distances were evaluated, revealing different approaches between subjects. Significant correlations were found between the speed, acceleration, and flexion of the performing knee. The analysis of anthropometric variables also showed correlations between Body Mass Index and knee flexion in both types of support. In the end, a significant relationship was revealed between speed and flexion of the supporting knee during the technical gesture. The proposed system has the potential to be used as a low-cost solution for the analysis of biomechanical parameters in athletes of soccer.

Abstract: The work focuses on building a sophisticated legged-wheel model that will perform in different terrains for convenient locomotion. Although a variation of wheels is available for different vehicles, locomotion mechanisms, or robotics, there is still a lack of convenient transformable wheels for even and uneven environments. When focusing on wheelchairs, the main obstacle is seen in stairs. Hence, the focus of this work is to model and analyze a transformable wheel for a wheelchair. Several parameters will be examined to find the most optimal solutions. This work carried out 41 simulations to assess performance under various conditions, taking into account factors like leg length, number of legs, acting force, and rotor speed. It was found that, with the given environment, the following parameters were most optimal for the wheelchair to move on stairs and on even terrain. The wheels with a length of 19 cm showed the best results for climbing stairs. A further increase in length would introduce instability. The optimal rotation speed was found to be around 20-22 rpm. At higher speed, it led to excessive resistance and instability. It was also found that a person's weight acting on the wheelchair is significant, as lighter weight results in slipping and incapability of climbing stairs. In concluding the findings, it is obvious that each parameter plays a vital role in the overall performance of the wheelchair.