Evaluation of a novel Asymmetric Genetic Algorithm to optimize the structural design of 3D regular and irregular steel frames

Es-haghi, Mohammad Sadegh; Shishegaran, Aydin; Rabczuk, Timon

doi:10.1007/s11709-020-0643-2

Cited by 26 publications

(12 citation statements)

References 49 publications

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“…In this part, the details of the optimal designs and the seismic behavior of the 40-story framed tube are presented. For the optimization process, the algorithms of AGA, which are depicted in [2], are used. The AGA algorithm has some differences from the GA, the most important of which are in the constraints evaluation strategy.…”

Section: Resultsmentioning

confidence: 99%

“…As a result, evaluation of constraints is not carried out for whole members of the population in AGA. See [2] for more information. The hyperparameters of AGA were selected by trying different values of the number of generations, the population size, the crossover probability, and the mutation probability, and the amounts of them are 200, 50, 60, and 5, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…This makes the conventional design provided by engineers not necessarily economical. This highlights the significance of optimization tools in the design process of these structures to save on construction costs [1][2][3][4]. Many of the studies in the field of tubular structures deal with the modeling of a tall building as a huge cantilever box beam [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Strategy for Tall Building Optimization via the Combination of AGA and Machine Learning Methods

Es-haghi

Sarcheshmehpour

2021

The 1st International Electronic Conference on Algorithms

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The optimum design of tall buildings, which have a proportionately huge quantity of structural elements and a variety of design code constraints, is a very computationally expensive process. In this paper, a novel strategy, with a combination of evolutionary algorithms and machine learning methods, is developed for achieving the optimal design of tall buildings. The most time-consuming part is the analysis of tall buildings and the control of design code constraints requiring long and frequent analyses. The main idea is to use machine learning methods for this purpose. In this study, a practical methodology for obtaining the optimal design of tall building structures, regarding the constraints imposed by typical building codes, is introduced. The optimization process will be performed by a novel evolutionary algorithm, named asymmetric genetic algorithm (AGA), and in each iteration that requires checking the constraints for a large number of different structural states, machine learning methods, including MLP, GMDH and ANFIS-PSO are facilitators. More specifically, MLP (R2 = 0.988) has performed better than GMDH (R2 = 0.961) and ANFIS-PSO (R2 = 0.953). By coupling ETABS and MATLAB software, various combinations of sections for structural elements are assigned and analyzed automatically, thus creating a database for training neural networks. The applicability of the suggested procedure is described through the determination of the optimal seismic design for a 40-story framed tube building. Results designate that the present method not only supports the precision of the methodology but also remarkably diminishes the computational time and memory needed in comparison with the existing classical methods. More importantly, the optimization process time is also significantly decreased.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Strategy for Tall Building Optimization via the Combination of AGA and Machine Learning Methods

Es-haghi

Sarcheshmehpour

2021

The 1st International Electronic Conference on Algorithms

Self Cite

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“…Some of the intelligent algorithms are inspired from finding the food in nature of swarms such as particle swarm optimization (PSO) [19], genetic algorithm (GA) [20], differential evolution (DE) [21], Evolution Strategy (ES) [22], ant colony optimization (ANT) [23], artificial bee colony (ABC) [24], firefly algorithm (FA) [25], cat swarm optimization (CAT) [26], gravitational search algorithm (GSA) [27], Jaya optimization algorithm (JAYA) [28], Cuckoo search algorithm (CS) [29], etc. These intelligent algorithms are applied to many different fields including the field of structural analysis and civil engineering [30][31][32]. In SHM, the selection of a reliable algorithm plays a significant role.…”

Section: Introductionmentioning

confidence: 99%

A variable velocity strategy particle swarm optimization algorithm (VVS-PSO) for damage assessment in structures

Minh

Khatir

Rao

et al. 2021

Engineering with Computers

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In this paper, for the first time, a variable velocity strategy particle swarm optimization (VVS-PSO) is presented to solve the optimization problems ranging from numerical functions to real-world problems. VVS-PSO introduces a new term added in the velocity updating process at each iteration. This new term is controlled by a reduction linear function, which allows VVS-PSO to reach a faster convergence rate. At the same time, it also leads to enhance the accuracy level. In this way, the strategy of position updating in VVS-PSO is more flexible than that of the original PSO. This strategy will support VVS-PSO to improve the distance between the current step and the previous step and to expand the feasible search space around each particle. To illustrate the convergence rate and level of accuracy of VVS-PSO, the original PSO and 4 well-known optimization algorithms are employed to solve 23 classical benchmark functions. Then, an engineering design problem and experimental validation using a four-storey steel frame are also presented to examine the reliability of VVS-PSO for solving particular real applications. VVS-PSO finally is applied to a real 3D reinforced concrete structure for the purpose of damage assessment. First, the modal assurance criterion (MAC) method, which considers the differences between the mode shapes, is combined with the Root-Mean-Square-Error (RMSE) that registers the differences between frequencies at two states, e.g., damaged and undamaged structures, to determine the objective function. Then, VVS-PSO is used to minimize the objective function, which accounts for variables related to stiffness reduction in elements. The presented results illustrate that VVS-PSO can solve the optimization and structural damage assessment problems with very high accuracy and reliability.

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“…In order to deal with complex problems, which are usually difficult to attack with a purely model-based approach, machine learning (ML) is currently experiencing a burst of popularity in applications, and also in the civil engineering field, see examples [23][24][25][26][27][28][29][30][31][32]. Regarding the foundation engineering field, neural networks have been employed to estimate the settlement and the load carrying capacity of pile foundations [33][34][35], and of isolated shallow footings [36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Prediction of the Seismic Bearing Capacity of a Shallow Strip Footing over a Void in Heterogeneous Soils

et al. 2021

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The seismic bearing capacity of a shallow strip footing above a void displays a complex dependence on several characteristics, linked to geometric problems and to the soil properties. Hence, setting analytical models to estimate such bearing capacity is extremely challenging. In this work, machine learning (ML) techniques have been employed to predict the seismic bearing capacity of a shallow strip footing located over a single unsupported rectangular void in heterogeneous soil. A dataset consisting of 38,000 finite element limit analysis simulations has been created, and the mean value between the upper and lower bounds of the bearing capacity has been computed at the varying undrained shear strength and internal friction angle of the soil, horizontal earthquake accelerations, and position, shape, and size of the void. Three machine learning techniques have been adopted to learn the link between the aforementioned parameters and the bearing capacity: multilayer perceptron neural networks; a group method of data handling; and a combined adaptive-network-based fuzzy inference system and particle swarm optimization. The performances of these ML techniques have been compared with each other, in terms of the following statistical performance indices: coefficient of determination (); root mean square error (); mean absolute percentage error; scatter index; and standard bias. Results have shown that all the ML techniques perform well, though the multilayer perceptron has a slightly superior accuracy featuring noteworthy results (0.9955 and 0.0158).

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Evaluation of a novel Asymmetric Genetic Algorithm to optimize the structural design of 3D regular and irregular steel frames

Cited by 26 publications

References 49 publications

A Novel Strategy for Tall Building Optimization via the Combination of AGA and Machine Learning Methods

A Novel Strategy for Tall Building Optimization via the Combination of AGA and Machine Learning Methods

A variable velocity strategy particle swarm optimization algorithm (VVS-PSO) for damage assessment in structures

Machine Learning-Based Prediction of the Seismic Bearing Capacity of a Shallow Strip Footing over a Void in Heterogeneous Soils

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