A neural network approach for approximate force response analyses of a bridge population

Date

2013-03-01

Author

Hasançebi, Oğuzhan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

188
views

0
downloads

In this paper, artificial neural networks (ANNs) are used to develop an efficient method for rapid and approximate force response analyses of a bridge population. The single-span reinforced concrete T-beam bridge population in Pennsylvania State is taken as a particular case study. First, a statistical analysis is conducted to examine implicit and explicit dependencies between various geometrical and structural parameters of the bridges, and the governing bridge parameters are identified along with their ranges of variation within the population. Then, a set of sample bridges are randomly generated using different combinations of the governing parameters within their predefined ranges of variation. An exact finite element analysis is implemented for each sample bridge, and the maximum moment and shear responses in beams are obtained at critical locations under various combinations of standard truck loads. An ANN is implemented to learn the relationship between the bridge parameters (inputs) and responses (outputs) based on the sample set and to make predictions for other bridges that are not present in the set. The performances of a variety of different ANN architectures are tested, and their prediction capabilities are measured and compared.

Subject Keywords

Neural networks, Approximate structural analysis, T-beam bridge population, FE modeling, Condition assessment

URI

https://hdl.handle.net/11511/39828

Journal

NEURAL COMPUTING & APPLICATIONS

DOI

https://doi.org/10.1007/s00521-011-0767-3

Collections

Department of Civil Engineering, Article

Suggestions

OpenMETU
Core

A unifying grid approach for solving potential flows applicable to structured and unstructured grid configurations Cete, A. Ruhsen; Yuekselen, M. Adil; Kaynak, Uenver (Elsevier BV, 2008-01-01) In this study, an efficient numerical method is proposed for unifying the structured and unstructured grid approaches for solving the potential flows. The new method, named as the "alternating cell directions implicit - ACDI", solves for the structured and unstructured grid configurations equally well. The new method in effect applies a line implicit method similar to the Line Gauss Seidel scheme for complex unstructured grids including mixed type quadrilateral and triangle cells. To this end, designated al...
A linear approximation for training Recurrent Random Neural Networks Halıcı, Uğur (1998-01-01) In this paper, a linear approximation for Gelenbe's Learning Algorithm developed for training Recurrent Random Neural Networks (RRNN) is proposed. Gelenbe's learning algorithm uses gradient descent of a quadratic error function in which the main computational effort is for obtaining the inverse of an n-by-n matrix. In this paper, the inverse of this matrix is approximated with a linear term and the efficiency of the approximated algorithm is examined when RRNN is trained as autoassociative memory.
An experimental comparison of symbolic and neural learning algorithms Baykal, Nazife (1998-04-23) In this paper comparative strengths and weaknesses of symbolic and neural learning algorithms are analysed. Experiments comparing the new generation symbolic algorithms and neural network algorithms have been performed using twelve large, real-world data sets.
A temporal neural network model for constructing connectionist expert system knowledge bases Alpaslan, Ferda Nur (Elsevier BV, 1996-04-01) This paper introduces a temporal feedforward neural network model that can be applied to a number of neural network application areas, including connectionist expert systems. The neural network model has a multi-layer structure, i.e. the number of layers is not limited. Also, the model has the flexibility of defining output nodes in any layer. This is especially important for connectionist expert system applications.
A systematic study of probabilistic aggregation strategies in swarm robotic systems Soysal, Onur; Şahin, Erol; Department of Computer Engineering (2005) In this study, a systematic analysis of probabilistic aggregation strategies in swarm robotic systems is presented. A generic aggregation behavior is proposed as a combination of four basic behaviors: obstacle avoidance, approach, repel, and wait. The latter three basic behaviors are combined using a three-state finite state machine with two probabilistic transitions among them. Two different metrics were used to compare performance of strategies. Through systematic experiments, how the aggregation performa...

Citation Formats

O. Hasançebi, “A neural network approach for approximate force response analyses of a bridge population,” NEURAL COMPUTING & APPLICATIONS, pp. 755–769, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39828.