Journal ArticleDOI
Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings
Sankhadeep Chatterjee,Sarbartha Sarkar,Sirshendu Hore,Nilanjan Dey,Amira S. Ashour,Valentina Emilia Balas +5 more
Reads0
Chats0
TLDR
A particle swarm optimization-based approach to train the NN (NN-PSO), capable to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistory reinforced concrete building structure in the future.Abstract:
Faulty structural design may cause multistory reinforced concrete (RC) buildings to collapse suddenly. All attempts are directed to avoid structural failure as it leads to human life danger as well as wasting time and property. Using traditional methods for predicting structural failure of the RC buildings will be time-consuming and complex. Recent research proved the artificial neural network (ANN) potentiality in solving various real-life problems. The traditional learning algorithms suffer from being trapped into local optima with a premature convergence. Thus, it is a challenging task to achieve expected accuracy while using traditional learning algorithms to train ANN. To solve this problem, the present work proposed a particle swarm optimization-based approach to train the NN (NN-PSO). The PSO is employed to find a weight vector with minimum root-mean-square error (RMSE) for the NN. The proposed (NN-PSO) classifier is capable to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. A database of 150 multistoried buildings’ RC structures was employed in the experimental results. The PSO algorithm was involved to select the optimal weights for the NN classifier. Fifteen features have been extracted from the structural design, while nine features have been opted to perform the classification process. Moreover, the NN-PSO model was compared with NN and MLP-FFN (multilayer perceptron feed-forward network) classifier to find its ingenuity. The experimental results established the superiority of the proposed NN-PSO compared to the NN and MLP-FFN classifiers. The NN-PSO achieved 90 % accuracy with 90 % precision, 94.74 % recall and 92.31 % F-Measure.read more
Citations
More filters
Journal ArticleDOI
Optimizing connection weights in neural networks using the whale optimization algorithm
TL;DR: The qualitative and quantitative results prove that the proposed WOA-based trainer is able to outperform the current algorithms on the majority of datasets in terms of both local optima avoidance and convergence speed.
Journal ArticleDOI
An enhanced productivity prediction model of active solar still using artificial neural network and Harris Hawks optimizer
TL;DR: A new productivity prediction model of active solar still was developed depending on improving the performance of the traditional artificial neural networks using Harris Hawks Optimizer, which had the best accuracy in predicting the solar still yield compared with the real experimental results.
Journal ArticleDOI
Structural damage detection using finite element model updating with evolutionary algorithms: a survey
TL;DR: This study aims to present a review of critical aspects of structural damage identification using evolutionary algorithm-based finite element model updating for structural damage detection and possible research directions for utilizing evolutionary algorithms to solve damage detection problems.
Journal ArticleDOI
Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm
TL;DR: A novel model for accurately forecasting long-term monthly gold price fluctuations using a recent meta-heuristic method called whale optimization algorithm (WOA) as a trainer to learn the multilayer perceptron neural network (NN), which demonstrates the superiority of the hybrid WOA–NN model over other models.
Journal ArticleDOI
Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on Particle Swarm Optimization and XGBoost
Xiliang Zhang,Hoang Nguyen,Xuan-Nam Bui,Quang-Hieu Tran,Dinh-An Nguyen,Dieu Tien Bui,Hossein Moayedi +6 more
TL;DR: A novel intelligent approach for predicting blast-induced PPV was developed and the proposed PSO-XGBoost emerged as the most reliable model, in contrast, the empirical models yielded worst performances.
References
More filters
Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Journal ArticleDOI
An introduction to variable and feature selection
Isabelle Guyon,André Elisseeff +1 more
TL;DR: The contributions of this special issue cover a wide range of aspects of variable selection: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.
Journal Article
Data Mining Concepts and Techniques
TL;DR: Data mining is the search for new, valuable, and nontrivial information in large volumes of data, a cooperative effort of humans and computers that is possible to put data-mining activities into one of two categories: Predictive data mining, which produces the model of the system described by the given data set, or Descriptive data mining which produces new, nontrivials information based on the available data set.
Book
Neural Networks: A Systematic Introduction
TL;DR: The authors may not be able to make you love reading, but neural networks a systematic introduction will lead you to love reading starting from now.
Book
Handbook of Neural Computing Applications
TL;DR: Part 1 Introduction - structures, dynamics, and learning, A.J.Maren et al hardware implementations, S.R.Morgan and C.T.Harston structures; part 2 Implementing neural networks: system design, D.Jones and S.Franklin configuring and optimizing feedforward networks, and the future of neurocomputing in the year 2000 and beyond.
Related Papers (5)
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more