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Abstract: In this paper, a comparative analysis of the effects of groundwater, seepage and hydraulic heave on the optimal design of embedded retaining walls is carried out. The optimization model for an optimal retaining wall (ORW) minimizes the total length of the retaining wall considering design constraints. The model is extended to include the probability of failure as an additional constraint. This overcomes the limitations of the partial safety factor approach, which does not fully account for uncertainties in the soil. In contrast, the reliability-based design (RBD) approach integrates these uncertainties and enables an assessment of the impact of seepage and hydraulic heave on the reliability of the structure. A real-coded genetic algorithm was used to determine optimal designs for both optimization methods. The results of the case study show that the addition of seepage (groundwater flow) to the hydrostatic conditions has a modest effect on the embedment depth. The design based on partial safety factors, which takes seepage into account, leads to a slight increase in the embedment depth of 0.94% compared to a retaining wall design that only takes the hydrostatic conditions of the groundwater into account. When designing on the basis of probability failure, the percentage increase in embedment depth due to seepage is between 2.19% and 6.41%, depending on the target probability of failure. Furthermore, the hydraulic heave failure mechanism did not increase the required embedment depth of the retaining wall, which means that the failure mechanism of rotation near the base was decisive for the design.
Keywords: embedded retaining wall, reliability-based design, partial safety factor design, optimization, genetic algorithm
Published in DKUM: 10.12.2024; Views: 0; Downloads: 4
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Abstract: Circular planar spiral coils are the most important parts of wireless power transfer systems. This paper presents the optimization of wireless power transfer coils used for wireless power transfer, which is a problem when designing wireless power transfer systems. A single transmitter coil transfers power to a single receiving side. The performance of the wireless power transfer system depends greatly on the size and shape of the wireless power transfer system. Therefore, the optimization of the coils is of the utmost importance. The main optimization parameter was the coupling coefficient between the transmitter and the receiver coil in the horizontally aligned and misaligned position. A genetic evolutionary algorithm was used to optimize the coil, according to the developed cost function. The algorithm was implemented using the MATLAB programming language. The constraints regarding the design of the coils are also presented for the problem to be analyzed correctly. The results obtained using the genetic algorithm were first verified using FEM simulations. The optimized coils were later fabricated and measured to confirm the theory.
Keywords: wireless power transfer, coil optimization, genetic algorithm, coupling coefficient measurement
Published in DKUM: 14.08.2024; Views: 77; Downloads: 9
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Abstract: This paper proposes a new string transformation technique called Move with Interleaving (MwI). Four possible ways of rearranging 2D raster images into 1D sequences of values are applied, including scan-line, left-right, strip-based, and Hilbert arrangements. Experiments on 32 benchmark greyscale raster images of various resolutions demonstrated that the proposed transformation reduces information entropy to a similar extent as the combination of the Burrows–Wheeler transform followed by the Move-To-Front or the Inversion Frequencies. The proposed transformation MwI yields the best result among all the considered transformations when the Hilbert arrangement is applied.
Keywords: computer science, algorithm, string transformation, information entropy, Hilbert space filling curve
Published in DKUM: 22.05.2024; Views: 160; Downloads: 9
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Abstract: An optimization model for reinforced concrete circular columns based on the Eurocodes is presented. With the developed optimization model, which takes into account the exact distribution of the steel reinforcement, which is not the case when designing with conventional column design charts, an optimal design for the reinforced concrete cross section is determined. The optimization model uses discrete variables, which makes the results more suitable for actual construction practice and fully exploits the structural capacity of the structure. A parametric study of the applied axial load and bending moment was performed for material cost and CO2 emissions. The results based on a single objective function show that the optimal design of the reinforced concrete column cross section obtained for the material cost objective function contains a larger cross-sectional area of concrete and a smaller area of steel compared with the optimization results when CO2 emissions are determined as the objective function. However, the optimal solution in the case where the material cost was assigned as the objective function has much more reserve in axial load capacity than in the optimal design where CO2 was chosen as the objective function. In addition, the multi-objective optimization was performed to find a set of solutions that provide the best trade-offs between the material cost and CO2 emission objectives.
Keywords: reinforced concrete columns, circular cross section, costs, CO2 emissions, multi-objective optimization, genetic algorithm
Published in DKUM: 15.04.2024; Views: 338; Downloads: 208
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Abstract: In this thesis, we compare and evaluate different SLAM solutions for a low-cost mobile robot. We present a simulator for the robot and use it to gather simulated data. Using this data, we then optimize the SLAM algorithms using an evolutionary algorithm. The optimized solutions are then validated and compared to default SLAM configurations. Up to 83 % reduction of error is achieved on validation data with multiple SLAM algorithms with improvements also visible on the real-world data.
Keywords: SLAM, optimization, mobile robot, evolutionary algorithm, simulation.
Published in DKUM: 05.10.2023; Views: 376; Downloads: 31
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