جستجوی مقالات مرتبط با کلیدواژه "الگوریتم جستجوی هارمونی" در نشریات گروه "عمران"

Due to the difficulties in assessing the deformation of jointed aggregates at the laboratory scale, various in situ testing methods such as plate loading test and dilatometry can be used to consider the effect of scale and joints. Although these methods are currently the best, they are expensive, time consuming, and have operational difficulties during implementation. Therefore, in this paper, to overcome these problems, new harmony search algorithms (HS) and teaching-learning optimization algorithm (TLBO) are used to indirectly estimate the modulus of rock mass deformation. In these models, the rock mass classification score (RMR), uniaxial compressive strength of virgin rock (UCS), depth (D) and the modulus of elasticity of intact rock (Ei) as input parameters and the modulus of rock mass deformability (Em) as output parameter Used. In this paper, Using different statistical indicators, the model created by the algorithms is evaluated and validated. The evaluation results showed that the relationship accuracy for the harmonic search algorithm using R2 and VAF methods is about 0.91-0.93 and using the RMSE and MSE methods is between 0.000017-0.0042. Also, the relationship accuracy for the optimization algorithm Based on teaching and learning using R2 and VAF methods, about 0.92-0.95 and using RMSE and MSE methods were between 0.00001- 0.0032.

In recent decades, different control strategies (i.e., passive, active, semi-active, or hybrid) have been extensively employed to suppress the response of offshore structures subjected to dynamic lateral loads. Jacket platforms are vital structures for oil-rich countries that are frequently subjected to dynamic harsh sea waves. Therefore, the vibration control of these structures is substantial to increase productivity, safety, and serviceability and prevent premature possible fatigue failure. In this study, the behavior of the Ressalat platform located in the Persian Gulf is evaluated under wave loads with two different return periods of 50 and 100 years. A simplified equivalent seven-degree-of-freedom lumped mass linear model is adopted for the Ressalat platform. In the Persian Gulf, wave loads are the dominant load in the designing procedure. Due to the stochastic nature of wave loads, random wave and constrained new-wave theories are utilized in the generation of the wave records. Then, Passive Tuned Mass Damper (PTMD) and Active Tuned Mass Damper (ATMD) are employed to suppress the platform vibration and subsequently, their control performances are compared. Various calculated normalized performance criteria (responses of controlled to uncontrolled structures) for passive and active controlled platforms including normalized maximum and Root Mean Square (RMS) of displacement, velocity, and acceleration of the deck level are calculated and compared. The uncontrolled and controlled platforms are modeled and analyzed using MATLAB and SIMULINK software. Moreover, the fuzzy intelligent algorithm with a triangular membership function is implemented to calculate the control force and the Harmony Search Algorithm (HAS) is examined to optimize the actuator power. Also, the Fluid-Structure Interaction (FSI), the effect of added mass due to the accelerated motion of the body in the fluid, and the saturation of the actuator are considered. The results show the effectiveness of the proposed control system by reducing 20% and 50% of the maximum and RMS of the platform deck acceleration, respectively, over time.

Large-scale spatial skeletal structures belong to a special kind of 3D structures widely used in exhibition centers, supermarkets, sport stadiums, airports, etc., to cover large surfaces without intermediate columns. Space structures are often categorized as grids, domes and barrel vaults. Double layer grid structures are classical instances of prefabricated space structures and also the most popular forms which are frequently used nowadays.Topology optimization of large-scale skeletal structures has been recognized as one of the most challenging tasks in structural design. In topology optimization of these structures with discrete cross-sectional areas, the performance of meta-heuristic optimization algorithms can be increased if they are combined with continuous-based topology optimization methods. In this article, a hybrid methodology combining evolutionary structural optimization (ESO) and harmony search algorithm (HSA) methods is proposed for topologyoptimization of double layer grid structures subject to vertical load. In the present methodology, which is called ESO-HSA method, the size optimization of double layer grid structures is first performed by the ESO. Then, the outcomes of the ESO are used to improve the HSA. In fact, a sensitivity analysis is carried out using an optimization method (ESO) to determine more important members based on the cross-sectional areas of members. Then, the obtained optimum cross-sectional areas of members are used to enhance the HSA through two modifications. Structural weight is minimized against constraints on the displacements of nodes, internal stresses and element slenderness ratio. In topology optimization of double layer grid structures, the geometry of the structure, support locations and coordinates of nodes are fixed and this structure is assumed as a ground structure. Presence/absence of bottom nodes, and element cross-sectional areas are selected as design variables. In topology optimization of the ground structure, tabulating of nodes is carried out based on structural symmetry: this leads to reduce complexity of design space and nodes are removed in groups of 8, 4 or 1. The presence or absence of each node group is determined by a variable (topology variable) which takes the value of 1 and 0 for the two cases, respectively. The ground structure is assumed to be supported at the perimeter nodes of the bottom grid. Therefore, these supported nodes will not be removed from the ground structure. In order to achieve a practical structure, the existence of nodes in the top grid will not be considered as a variable. This causes the load bearing areas of top layer nodes to remain constant. Also, discrete variables are used to optimize the cross-sectional area of structural members. These variables are selected from pipe sections with specified thickness and outer diameter. Therefore, in topology optimization problem, the number of design variables is the summation of the number of compressive and tensile element types and the number of topology variables. The proposed approach is successfully tested in topology optimization problem of double layer grid structure. In particular, ESO-HSA is very competitive with other metaheuristic methods recently published in literature and can always find the best design overall. Also, it is determined that HSA method can find better answer in the topology optimization of large-scale skeletal structures, in comparison to optimum structures attained by the GSA and ICA.

The optimal routing for transferring the wounded and relief assistance is a major problem in the event of a crisis. At this time the importance of two factors, namely time and money, in order to help and rescue the injured is doubled. This paper aims to find the most optimal route from a rescue center to the crisis center. The presented mathematical model aims to minimize the time and cost of accessing a crisis center. We also consider some assumptions, such as multiple storages, multiple paths, multiple scenarios, split delivery, multiple products, heterogeneity of the vehicles and time. Given that the values of some parameters, such as demand and time of the travel are uncertain, in which we state them in respect to the former mentioned assumptions. Considering these parameters uncertain makes them closer to the real problem. Most of the issues raised in this field have not considered all assumptions at the same time and they have considered the mentioned parameters (time and demand) as definitive. Finally, in order to find accurate answers regarding to multiple objectives of the model and different phases of parameters (i.e., time and demand), we use ε-constraint method in small-scale problems. Then because this problem is NP-hard, two meta-heuristic algorithms, namely MOHS and NSGA-II, are used to solve 15 issues large-scale problems. The results numerically show that both algorithms have high potential in producing good solutions at the right time and they are used to solve the largest and most complex issue in less than 480 seconds. This model is very suitable and uncertain with multiple objectives.

High fidelity models are becoming increasingly common in engineering optimization. The computation burden is often caused by expensive analysis and simulation processes in order to reach a comparable level of accuracy as physical testing data. The metamodels are initially developed as surrogates of the expensive simulation process in order to improve the overall computation efficiency. This work presents a new multilevel optimization approach for multidisciplinary structural design optimization based on multi fidelity modeling to decrease computational effort. Such method is a composition of a statistical estimating method and a metaheuristic algorithm. A low fidelity analysis response determines if the high fidelity analysis should be done or not. As a result, most of unnecessary high fidelity calculation will be omitted. The empirical results show the new algorithm causes a significant decrease in computational load as well as increase in convergence rate. Keywords: Multi level optimization; Metamodeling; Harmony search algorithm; Inverse distance weighting model.