The self-adaptive population Rao algorithms (SAP-Rao) are employed in this study to produce the optimal designs for steel grillage structures. The size variables in the optimization problem consist of the cross-sectional area of the discrete W-shapes of these beams. The LRFD-AISC design code was used to optimize the constrained size of this kind of structure. The solved problem’s primary goal is to determine the grillage structure’s minimum weight. As constraints, it is decided to use the maximum stress ratio and the maximum displacement at the inner point of the steel grillage structure. The finite element method (FEM) was employed to compute the moment and shear force of each member, as well as the joint displacement. A computer program for the study and design of grillage structures, as well as the optimization technique for SAP-Rao, was created in MATLAB. The outcomes of this study are compared to earlier efforts on grillage structures. The findings demonstrate that the optimal design of grillage structures can be successfully accomplished using the SAP-Rao method described in this paper.
This paper investigates the reduction of bending moment in critical members by adding some extra members in the optimum location. Instead of enlarging the size of members to resist the moment, eight additional members are added in the optimum location to reduce the bending moment in the critical members. The total weight of the proposed structure with extra members is less than that of the original structure that resists the induced bending moment. Moreover, the location of the additional bars significantly reduces the nodal displacements. This paper investigates the effect of placing extra members on vertically and/or horizontally loaded egg-shaped single-layer frames. An egg-shaped structure is designed based on the maximum induced moment; in such frames, the bending moment is the dominant internal force. Then some extra members are suggested to be added to the structure to reduce the maximum bending moment to the lowest possible value; thus, the designed cross-sectional area is minimized. Furthermore, the optimized structure's total weight and shape deformation is less than the ordinary structure's. The study results show that the extra bars' location depends on the loadings' direction. Moreover, the weight of the horizontally loaded egg-shaped structure can be optimized by up to 28%. The results were verified by MATLAB and SAP2000 software.
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The metaheuristic algorithm is proposed to solve the weight minimization problem of trussstructures, considering the shape and sizing design variables. Design variables are discreteand/or continuous. The design of truss structures is optimized by an efficient optimiza-tion algorithm called Jaya. The main feature of Jaya is that it does not require settingalgorithm-specific parameters. The algorithm has a very simple formulation in which thebasic idea is to approach the best solution and escape from the worst solution [6]. Analysesof structures are performed by a finite element code in MATLAB. The effectiveness of theJaya algorithm is demonstrated using two benchmark examples: planar truss 18-bar andspatial truss 39-bar, and compared with results in references.
A genetic algorithm is proposed to solve the weight minimization problem of spatial truss structures considering size and shape design variables. A very recently developed metaheuristic method called JAYA algorithm (JA) is implemented in this study for optimization of truss structures. The main feature of JA is that it does not require setting algorithm specific parameters. The algorithm has a very simple formulation where the basic idea is to approach the best solution and escape from the worst solution. Analyses of structures are performed by a finite element code in MATLAB. The effectiveness of JA algorithm is demonstrated through benchmark spatial truss 39-bar, and compare with results in references.
W artykule zaproponowano algorytm genetyczny do rozwiązania problemu minimalizacji masy płaskiej kratownicy, biorąc pod uwagę zmienność pola przekroju. Minimalna masa konstrukcji stalowej to też niska emisja CO2. Konstrukcja jest zoptymalizowana za pomocą wydajnego algorytmu zwanego Teaching Learning Based Optimization. Proces TLBO jest podzielony na dwie części: pierwsza składa się z "fazy nauczyciela", a druga składa się z "fazy ucznia". Obliczenia wykonywane są z pomocą programu metody elementów skończonych zakodowanym w MATLAB-ie.
EN
The article proposes a genetic algorithm for solving the problem of minimizing the mass of a plane truss, taking into account the variability of the cross-sectional area. The minimum mass of the steel structure is also low CO2 emission. The design is optimized using an efficient algorithm called Teaching Learning Based Optimization. The TLBO process is divided into two parts: the first consists of the "teacher phase" and the second consists of the "student phase". The calculations are performed with the help of the finite element method program coded in MATLAB.
Although in the scientific literature there are studies regarding optimization of structural members subject to static loads or even cyclic in-phase loads, the optimization of structures subject to cyclic, out-of-phase multiaxial loads is still an unexplored issue. In this paper, we present an approach to the problem of size optimization of rectangular cross-section members subject to multiaxial in-phase and out-of-phase cyclic loads. The objective of the optimization is to minimize the cross sectional area of such elements while retaining their fatigue endurance. Under the proposed methodology, optimum values of the area are achieved for six loading cases and for three values of the height to width ratio of the cross section, and these values are reported. The novelty of the approach lies in the inclusion of two multiaxial high cycle fatigue criteria, i.e., Dang Van and Vu-Halm-Nadot ones, as constraints for size optimization problems, fully integrated within an in-house developed tool, capable of handling non-proportional stresses. A plot of the feasible solution space for this optimization problem is also obtained.
Bridge crane is one of the most widely used cranes in our country, which is indispensable equipment for material conveying in the modern production. The security of bridge crane is always focused on when being used. The important indicators of crane performances include strength, stiffness, and crane weight, which mainly depend on the structure design of the bridge crane. So it is of importance to research on energy-saving optimization design by means of finite element analysis, ADMAS and Matlab. In this paper, the framework of energy-saving optimization is proposed. Secondly, taking 50 t – 31.5 m bridge crane as research object, its structure is described and the FE model of the bridge cranes is developed for the finite element analysis. Thirdly, shape optimal mathematical model of the crane is proposed for shape optimization as well as size optimal mathematical model for size optimization and topology optimal mathematical model for topology optimization. Besides, further comprehensive energy-saving optimizations are carried out as well as cross-section optimization. Finally, system-level energy-saving optimization design of bridge crane is further carried out with energy-saving transmission design results feedback to energy-saving optimization design of metal structure. The optimization results show that structural optimization design can reduce total mass of crane greatly by using the finite element analysis and optimization technology premised on the design requirements of cranes such as stiffness, strength and so on, thus energy-saving design can be achieved.
PL
Suwnica pomostowa jest jednym z najczęściej używanych typów suwnic w Chinach i stanowi niezbędne wyposażenie do transportu materiałów w nowoczesnej produkcji. Kluczową kwestią dotyczącą obsługi suwnicy pomostowej jest zawsze bezpieczeństwo. Ważnymi wskaźnikami wydajności suwnicy są m.in. wytrzymałość, sztywność oraz ciężar suwnicy, które zależą głównie od konstrukcji suwnicy. Konieczne są zatem badania nad optymalizacją energooszczędności konstrukcji za pomocą analizy elementów skończonych, ADMAS oraz Matlab. W niniejszej pracy zaproponowano koncepcję optymalizacji energooszczędności. Po drugie, opisano budowę suwnicy pomostowej (50 t – 31.5 m) oraz opracowano model MES suwnicy do analizy metodą elementów skończonych. Po trzecie, przyjmując minimalną pojemność jako funkcję celu, wysokość i szerokość suwnicy jako zmienne projektowe, a naprężenie, energię odkształcenia, modalnych jako ograniczenia, ustalono optymalny model matematyczny kształtu żurawia dla celów optymalizacyjnego projektowania kształtu. Po czwarte, przyjmując minimalny udział objętościowy jako funkcję celu, a grubości płyt jako zmienne projektowe, ustalono optymalny model matematyczny rozmiarów do celów optymalizacyjnego projektowania rozmiarów. Po piąte, przyjmując minimalny udział objętościowy jako funkcję celu, a gęstości materiału każdego z elementów jako zmienne projektowe, ustalono optymalny model matematyczny topologii do celów optymalizacyjnego projektowania topologii. Wreszcie, wykonano multidyscyplinarny energooszczędny projekt optymalizacyjny systemu suwnicy pomostowej, a wyniki energooszczędnego projektowania układu napędu zostały wykorzystane jako informacja zwrotna przy energooszczędnym projektowaniu optymalizacyjnym konstrukcji metalowej. Wyniki optymalizacji pokazują, że optymalizacyjne projektowanie konstrukcji z wykorzystaniem analizy MES oraz technologii optymalizacji opartej na wymogach projektowych dla suwnic, takich jak sztywność, wytrzymałość itd., może znacznie obniżyć całkowitą masę dźwigu, a co za tym idzie zwiększyć jego energooszczędność.
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