Probabilistic Performance-Based Optimum Seismic Design of RC Structures Considering Soil–Structure Interaction Effects

Yazdani, Hessam; Khatibinia, Mohsen; Gharehbaghi, Sadjad; Hatami, Kianoosh

doi:10.1061/ajrua6.0000880

Cited by 30 publications

(15 citation statements)

References 59 publications

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“…The successful application of the particle swarm optimization (PSO) algorithm in the optimum design of RC structures has been reported in the literature (e.g., [26][27][28][29][30][31][32][33]). In this paper, the PSO algorithm was used to optimize RCRWs.…”

Section: Pso Algorithmmentioning

confidence: 99%

Cost-Based Optimum Design of Reinforced Concrete Retaining Walls Considering Different Methods of Bearing Capacity Computation

Moayyeri¹,

Gharehbaghi²,

Plevris

2019

Mathematics

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This paper investigates the effect of computing the bearing capacity through different methods on the optimum construction cost of reinforced concrete retaining walls (RCRWs). Three well-known methods of Meyerhof, Hansen, and Vesic are used for the computation of the bearing capacity. In order to model and design the RCRWs, a code is developed in MATLAB. To reach a design with minimum construction cost, the design procedure is structured in the framework of an optimization problem in which the initial construction cost of the RCRW is the objective function to be minimized. The design criteria (both geotechnical and structural limitations) are considered constraints of the optimization problem. The geometrical dimensions of the wall and the amount of steel reinforcement are used as the design variables. To find the optimum solution, the particle swarm optimization (PSO) algorithm is employed. Three numerical examples with different wall heights are used to capture the effect of using different methods of bearing capacity on the optimal construction cost of the RCRWs. The results demonstrate that, in most cases, the final design based on the Meyerhof method corresponds to a lower construction cost. The research findings also reveal that the difference among the optimum costs of the methods is decreased by increasing the wall height.

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Section: Pso Algorithmmentioning

confidence: 99%

Cost-Based Optimum Design of Reinforced Concrete Retaining Walls Considering Different Methods of Bearing Capacity Computation

Moayyeri¹,

Gharehbaghi²,

Plevris

2019

Mathematics

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“…In the recent decades, soft computing-based approach have been widely using to simplify the complex problems in a broad range of civil engineering problems (e.g. [5][6][7][8][9][10][11][12][13][14][15][16][17][18]). Some of the relevant studies to application of ANNs in structural engineering problems are described herein.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Performance Point of Semi-Rigid Steel Frames Using Artificial Neural Networks

Bahmani¹,

Ghasemi²,

Mousaviamjad³

et al. 2019

IJISA

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One of the main steps in the performance based seismic analysis and design of structures is determination of performance point where the nonlinear static analysis approach is used. The aim of this paper is to predict the performance point of semi-rigid steel frames using Artificial Neural Networks. As such, to generate data required for the prediction, several semirigid steel frames were modeled and their performance point was determined then. Ten input variables including number of bays, number of stories, bays width, moment of inertia of beams, cross sectional area of columns, cross sectional area of braces, rigidity degree of connections and soft story (existence or nonexistence) were considered in the prediction. In addition, the actual results were obtained at the presence of different earthquake intensity levels and soil types. Back Propagation with eleven different algorithms and Radial Basis Function Artificial Neural Networks were used in the prediction. The prediction process was carried out in two steps. In the first step, all samples were used for the prediction and the performance metrics were computed. In the second step, three of the best networks were selected, and the optimum number of samples was found considering a very slight reduction in the accuracy of the networks used. Finally, it was shown that, despite using rather limited number of samples, the generated Artificial Neural Networks accurately predict the performance point of semi-rigid steel frames.

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“…Although both ANNs and SVMs have received significant attention (e.g. Salajegheh and Heidari 2005, Gholizadeh and Salajegheh 2009, Papadopoulos et al 2012, Yazdani et al 2016, they require a pre-defined and initial structure for the equation and network architecture to be determined by the user. Genetic programming (GP), a learning algorithm originated from genetic algorithms, is another well-known and successful technique for developing nonlinear mathematical models for the complex problems.…”

Section: Introductionmentioning

confidence: 99%

A hybrid computational approach for seismic energy demand prediction

Gharehbaghi¹,

Gandomi

Achakpour

et al. 2018

Expert Systems with Applications

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In this paper, a hybrid genetic programming (GP) with multiple genes is implemented for developing prediction models of spectral energy demands. A multi-objective strategy is used for maximizing the accuracy and minimizing the complexity of the models. Both structural properties and earthquake characteristics are considered in prediction models of four demand parameters. Here, the earthquake records are classified based on soil type assuming that different soil classes have linear relationships in terms of GP genes. Therefore, linear regression analysis is used to connect genes for different soil types, which results in a total of sixteen prediction models. The accuracy and effectiveness of these models were assessed using different performance metrics and their performance was compared with several other models. The results indicate that not only the proposed models are simple, but also they outperform other spectral energy demand models proposed in the literature.

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Probabilistic Performance-Based Optimum Seismic Design of RC Structures Considering Soil–Structure Interaction Effects

Cited by 30 publications

References 59 publications

Cost-Based Optimum Design of Reinforced Concrete Retaining Walls Considering Different Methods of Bearing Capacity Computation

Cost-Based Optimum Design of Reinforced Concrete Retaining Walls Considering Different Methods of Bearing Capacity Computation

Prediction of Performance Point of Semi-Rigid Steel Frames Using Artificial Neural Networks

A hybrid computational approach for seismic energy demand prediction

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