Application of the Adaptive Neuro-Fuzzy  Inference System for Optimal Design of Reinforced Concrete Beams

Yeh, Jiin-Po; Yang, Ren-Pei

doi:10.4236/jilsa.2014.64013

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…Take civil engineering as an example. Many publications have been seen, such as multiobjective optimization of trusses [3], reliability analysis of structures [4], global optimization of grillages [5], global optimization of trusses with a modified genetic algorithm [6], optimization of pile groups using hybrid genetic algorithms [7], locating the critical slip surface in slope stability analyses [8][9][10], optimal design of reinforced concrete beams [11], etc.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Continuous Reinforced Concrete Beams using Neural Networks

Yeh

Yang

2015

TMLAI

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This paper aims to build a neural network model to optimally design two-span continuous reinforced concrete beams. The training and checking data of the neural network are obtained by genetic algorithms, whose constraints are built according to the ACI Building Code and objective function is to find the minimum cost of longitudinal reinforcement, stirrups and concrete. The neural network adopted in this paper is the feed forward back propagation network, whose input vector consists of the span, width and effective depth of the beam, dead load, compressive strength of concrete as well as yield strength of steel and the output vector the positive and negative steel ratios and minimum total cost. The correlation coefficients between the target and network output of the testing data can reach as high as 0.9992, 0.9980 and 0.9999, respectively for the positive and negative steel ratios and minimum cost. Compared with the adaptive neuro-fuzzy inference system, the neural network shows almost the same accuracy but is much easier implemented.

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Section: Introductionmentioning

confidence: 99%

Optimal Design of Continuous Reinforced Concrete Beams using Neural Networks

Yeh

Yang

2015

TMLAI

Self Cite

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A survey of time series forecasting from stochastic method to soft computing

Hendikawati

Subanar

Abdurakhman

et al. 2020

J. Phys.: Conf. Ser.

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Forecasting is a part of statistical modelling that is widely used in various fields because of its benefits in decision-making. The purpose of forecasting is to predict the future values of certain variables that vary with time using its previous values. Forecasting is related to the formation of models and methods that can be used to produce a good forecast. This research is a survey paper research that used a systematic mapping study and systematic literature review. Generally, time series research uses linear time series models, specifically the autoregressive integrated moving average model that has long been used because it has good forecasting accuracy. The successfulness of the Box–Jenkins methodology is based on the reality that various models can imitate the behaviour of various types of series, usually without requiring many parameters to be estimated in the final choice of the model. However, the assumption of stationarity that must be met makes this method inflexible to use. With the advances in computers, forecasting methods from stochastic models to soft computing continue to develop and extend. Soft computing for forecasting can provide more accurate results than traditional methods. Moreover, soft computing has many advantages in terms of the amount of data that can be analysed and the time- and cost-effectiveness of the process.

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