Soft computing techniques in probabilistic seismic analysis of structures (original) (raw)
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Advances in Engineering Software, 2008
The objective of this paper is to investigate the efficiency of soft computing methods, in particular methodologies based on neural networks, when incorporated into the solution of computationally intensive engineering problems. Two types of applications have been considered, namely parameter (flaw) identification and probabilistic seismic analysis of structures. Artificial neural networks (ANNs) based metamodels are used in order to replace the time-consuming repeated structural analyses. The back-propagation algorithm is employed for training the ANN, using data derived from selected analyses. The trained ANN is then used to predict the values of the necessary data. The numerical tests demonstrate the computational advantages of the proposed methodologies.
Soft Computing in Earthquake engineering: A short overview
Soft Computing refers to the name for solving the hardest problems with which human are confronted today that tolerates the imprecision, uncertainty, partial truth, and approximation of the solutions. Nature inspired algorithms, like evolutionary algorithms, swarm intelligence, and neural networks become one of the leading methods for solving these problems. The soft computing methods have also been applied for solving the earthquake engineering problems. In this paper, a short review of these methods is presented. In line with this, the problems solved by soft computing algorithms are identified, then, the characteristics of these algorithms are exposed and finally, the applications of the soft computing algorithms are identified. The paper concludes with an overview of the possible directions for further development.
Neural Networks: Some Successful Applications in Computational Mechanics
This article presents recent applications of neural computations in the field of stochastic finite element analysis of structures and earthquake engineering. The incorporation of Neural Networks (NN) in this type of problems is crucial since it leads to substantial reduction of the excessive computational cost. Earthquake- resistant design of structures using Probabilistic Safety Analysis (PSA) is an emerging field in structural engineering. The efficiency of soft computing methodologies is investigated when incorporated into the solution of computationally intensive earthquake engineering problems considering uncertainties.
Probabilistic Design of Earthquake‐Resistant Structures
Journal of Structural Engineering, 1987
This is the first of a two-part paper on the development and preliminary testing of a methodology for the optimal probabilistic limit states design of seismic-resistant steel frames. A mechanism for allowing a designer to include the effects of uncertainties and multiple design objectives in the design process is described. The concept of [GOOD, BAD] and [HIGH, LOW] preference pairs is explained. Scaling procedures for combining the aforementioned effects and the statistics of frame response in a single design entity called designer dissatisfaction are developed. The features of the proposed design method are demonstrated via the design of a simple one-story shear building.
7th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, Crete - Greece, 24/06/2019-26/06/2019, 2019
The implementation of methods which belong to the field of Artificial Intelligence such as the Artificial Neural Networks (ANN) based methods is continuously increased in many scientific and technological applications. As regards the civil engineering applications, the investigation for the utilization of these methods has led to very promising results. More specifically, the experimental application of ANN-based methods for the seismic vulnerability assessment of structures has proved that they can be utilized as alternative methods in parallel of the well-documented existing methods. However, despite their promising results there is no wide acceptance of ANNs as computational tools for the prediction of the seismic damage level of structures. This can possibly be attributed mainly to the fact that the vast majority of civil engineers who investigate methods for structures' seismic vulnerability assessment has no the minimum required background about the abilities and the utilization of the ANN-based methods. The current paper attempts to present a different point of view of the ANN-based methods and to prove that the research for their further implementation can be approachable by civil engineers provided that the corresponding formulation is defined in certain stages. In the framework of the current paper it is also proved that by the utilization of ANNs the definition of relatively simple equations for the preliminary estimation of the seismic damage level of R/C buildings in near-real time is possible.
2016
One of the efficient methods in seismic resistant design of medium height buildings is applying seismic isolation systems at the base of the structures to mitigate the response of structure. In this study an effective numerical reliability-based optimization technique is presented for the optimum design of isolation system under random time history earthquake loading. Friction Pendulum System (FPS) as one of the popular types of seismic isolation devices is considered to protect delicate equipment installed on the floor of a specific concrete building. So the object is to minimize the probability of failure of the base-isolated building subjected to design performance criteria in terms of the story acceleration of the superstructure. Due to stochastic nature of variables such as input ground motion; a novel method is proposed to predict the reliability of the supposed structure using artificial neural networks (ANN). The reliability of the system in the format of probability of fail...
Computational Methods in Earthquake Engineering
2011
The book provides an insight on advanced methods and concepts for design and analysis of structures against earthquake loading. It consists of 25 chapters covering a wide range of timely issues in Earthquake Engineering. The goal of this Volume is to establish a common ground of understanding between the communities of Earth Sciences and Computational Mechanics towards mitigating future seismic losses. Due to the great social and economic consequences of earthquakes, the topic is of great scientific interest and is expected to be of valuable help to the large number of scientists and practicing engineers currently working in the field. The chapters of this Volume are extended versions of selected papers presented at the COMPDYN 2009 conference, held in the island of Rhodes, Greece, under the auspices of the European Community on Computational Methods in Applied Sciences (ECOMASS).
Recent advances in reliability-based structural optimization under earthquake loading
2005
In this paper a robust and efficient methodology is presented for treating large-scale reliability-based, structural optimization problems under seismic loads using the pushover analysis method. The optimization problem is solved with the Evolution Strategies method, while the reliability analysis is carried out with the Monte Carlo simulation method. In order to reduce the excessive computational cost of the reliability-based optimization process the repeated structural analyses that are required during the Monte Carlo simulations are replaced by an efficient artificial neural network approximation scheme. 2. Keywords: structural optimization, reliability analysis, Monte Carlo, evolutionary computation, artificial neural networks.