An evaluation of effective design parameters on earthquake performance of RC buildings using neural networks

Arslan, Musa Hakan

doi:10.1016/j.engstruct.2010.03.010

Cited by 99 publications

(48 citation statements)

References 30 publications

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“…The first one employs standard numerical optimization techniques (BF, CGB, CGF, CGP, LM, OSS, and SCG). The second one is metaheuristic techniques including variable back-propagation learning rate (GDA, GDM, and GDX) and flexible back-propagation (RP), derived from the performance analysis of a standard steepest descent algorithm [36]. Therefore, the number of input nodes of the NN is 8 with two hidden layers and hidden nodes which provide a compatibility between accurate predictions and computationally efficient calculations.…”

Section: Nn Predictions Schemementioning

confidence: 99%

Seismic Failure Probability of a Curved Bridge Based on Analytical and Neural Network Approaches

Karimi-Moridani

Zarfam

Ghafory‐Ashtiany

2017

Shock and Vibration

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This study focuses on seismic fragility assessment of horizontal curved bridge, which has been derived by neural network prediction. The objective is the optimization of structural responses of metaheuristic solutions. A regression model for the responses of the horizontal curved bridge with variable coefficients is built in the neural networks simulation environment based on the existing NTHA data. In order to achieve accurate results in a neural network, 1677 seismic analysis was performed in OpenSees. To achieve better performance of neural network and reduce the dimensionality of input data, dimensionality reduction techniques such as factor analysis approach were applied. Different types of neural network training algorithm were used and the best algorithm was adopted. The developed ANN approach is then used to verify the fragility curves of NTHA. The obtained results indicated that neural network approach could be used for predicting the seismic behavior of bridge elements and fragility, with enough feature extraction of ground motion records and response of structure according to the statistical works. Fragility curves extracted from the two approaches generally show proper compliance.

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Section: Nn Predictions Schemementioning

confidence: 99%

Seismic Failure Probability of a Curved Bridge Based on Analytical and Neural Network Approaches

Karimi-Moridani

Zarfam

Ghafory‐Ashtiany

2017

Shock and Vibration

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“…This choice was based on the fact that this type of ANNs was successfully used in many published investigations which are related to the subject of the present investigation (see e.g. [10,11,16,17,18]). …”

Section: Selection Of the Type Of Annsmentioning

confidence: 99%

Approach to Prediction of R/C Buildings’ Seismic Damage as Pattern Recognition Problem Using Artificial Neural Networks

Morfidis¹,

Kostinakis²

2017

Proceedings of the 6th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering (COM

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“…ANN model was trained using self simulation to learn the cyclic behavior of the beam-column connection. Arslan [50] evaluated the effective design parameters and earthquake performance of the RC buildings using neural networks. The earthquake performance estimation percentages of the selects ANN algorithms vary between 91.68% and 98.47% depending on the type of the algorithm and other parameters of the ANN model.…”

Section: Earthquake Engineering Applicationsmentioning

confidence: 99%

Applications of Soft Computing in Civil Engineering: A Review

Chandwani¹,

Agrawal²,

Nagar³

2013

IJCA

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The field of engineering is a creative one. The problems encountered in this field are generally unstructured and imprecise influenced by intuitions and past experiences of a designer. The conventional methods of computing relying on analytical or empirical relations become time consuming and labor intensive when posed with real life problems. To study, model and analyze such problems, approximate computer based Soft Computing techniques inspired by the reasoning, intuition, consciousness and wisdom possessed by a human beings are employed. In contrast to conventional computing techniques which rely on exact solutions, soft computing aims at exploiting given tolerance of imprecision, the trivial and uncertain nature of the problem to yield an approximate solution to a problem in quick time. Soft Computing being a multi-disciplinary field uses a variety of statistical, probabilistic and optimization tools which complement each other to produce its three main branches viz., Neural Networks, Genetic Algorithms and Fuzzy Logic. The review paper presents the applications of two major Soft Computing techniques viz., Artificial Neural Networks and Genetic Algorithms in the field of Civil Engineering, which to some extent has replaced the time consuming conventional techniques of computing with intelligent and time saving computing tools.

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An evaluation of effective design parameters on earthquake performance of RC buildings using neural networks

Cited by 99 publications

References 30 publications

Seismic Failure Probability of a Curved Bridge Based on Analytical and Neural Network Approaches

Seismic Failure Probability of a Curved Bridge Based on Analytical and Neural Network Approaches

Approach to Prediction of R/C Buildings’ Seismic Damage as Pattern Recognition Problem Using Artificial Neural Networks

Applications of Soft Computing in Civil Engineering: A Review

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