HPC Strength Prediction Using Artificial Neural Network

Kasperkiewicz, Janusz; Racz, Janusz; Dubrawski, Artur

doi:10.1061/(asce)0887-3801(1995)9:4(279)

Cited by 182 publications

(71 citation statements)

References 13 publications

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“…Theoretical concepts in neural networks can be seen in many books, such as Kosko (1992) and Wu (1994). Applications of the network to prediction problems in civil engineering include French et al (1992), Yeh et al (1993), Kasperkiewiecz et al (1995), Grubert (1995), Deo (1998, 2000) and Deo and Kiran Kumar (1999), and these are related to prediction of rainfall, runoff, concrete strength, estuarine instabilities, river stage and waves, respectively.…”

Section: Introductionmentioning

confidence: 99%

Neural networks for wave forecasting

et al. 2001

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The physical process of generation of waves by wind is extremely complex, uncertain and not yet fully understood. Despite a variety of deterministic models presented to predict the heights and periods of waves from the characteristics of the generating wind, a large scope still exists to improve on the existing models or to provide alternatives to them. This paper explores the possibility of employing the relatively recent technique of neural networks for this purpose. A simple 3-layered feed forward type of network is developed to obtain the output of significant wave heights and average wave periods from the input of generating wind speeds. The network is trained with different algorithms and using three sets of data. The results show that an appropriately trained network could provide satisfactory results in open wider areas, in deep water and also when the sampling and prediction interval is large, such as a week. A proper choice of training patterns is found to be crucial in achieving adequate training.

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Section: Introductionmentioning

confidence: 99%

Neural networks for wave forecasting

et al. 2001

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“…Traditionally, the learning process is used to determine proper interconnection weights, and the network is trained to make proper associations between the inputs and their corresponding outputs (Yeh et al 1993;Oztas et al 2006Oztas et al , 1993Kasperkiewicz et al 1995). Errors that arise during the learning process can be expressed in terms of mean square error (MSE) and are calculated using Eq.…”

Section: Model Descriptionmentioning

confidence: 99%

An Artificial Neural Networks Model for Predicting Permeability Properties of Nano Silica–Rice Husk Ash Ternary Blended Concrete

Najigivi

Khaloo

zad

et al. 2013

Int J Concr Struct Mater

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In this study, a two-layer feed-forward neural network was constructed and applied to determine a mapping associating mix design and testing factors of cement-nano silica (NS)-rice husk ash ternary blended concrete samples with their performance in conductance to the water absorption properties. To generate data for the neural network model (NNM), a total of 174 field cores from 58 different mixes at three ages were tested in the laboratory for each of percentage, velocity and coefficient of water absorption and mix volumetric properties. The significant factors (six items) that affect the permeability properties of ternary blended concrete were identified by experimental studies which were: (1) percentage of cement; (2) content of rice husk ash; (3) percentage of 15 nm of SiO 2 particles; (4) content of NS particles with average size of 80 nm; (5) effect of curing medium and (6) curing time. The mentioned significant factors were then used to define the domain of a neural network which was trained based on the Levenberg-Marquardt back propagation algorithm using Matlab software. Excellent agreement was observed between simulation and laboratory data. It is believed that the novel developed NNM with three outputs will be a useful tool in the study of the permeability properties of ternary blended concrete and its maintenance.

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“…The model proposed herein predicts the compressive strength of HPC using an experimental database originally collected by Yeh [13] and furnished fro m various university research labs, which was posted to the University of Califo rnia, Irvine machine learn ing repository website. The database includes a total of 1030 concrete samples and covers 9 attributes, 8 of which are quantitative input variables and 1 of which is an output variable.…”

Section: Databasementioning

confidence: 99%

“…However, as pointed out previously, the result depends on ingredient combinations and proportions, mixing techniques and other factors that must be controlled during manufacturing. Kasperkicz et al [13] stated that the introduction of new ingredients and technologies implies that the number of parameters for HPC mix design may extend to 10-, 20-or even higher dimensional decision space numbers. Waiting 28 days to get 28-day compression strength is time consuming and not a common practice in the construction industry.…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have propos ed artificial neural networks (ANNs) and ANN variations to map non-linear relationships among factors of influence on 28-day HPC co mpressive strength. Kasperkicz et al [13] proposed an artificial neural network of the fuzzy-ATMAP to predict HPC strength properties. It was found that concrete property prediction could be effectively modeled using a neural system without being affected by data complexity, inco mpleteness, or incoherence.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

High-Performance Concrete Compressive Strength Prediction Based Weighted Support Vector Machines

Rguig¹,

Mohamed²

2017

IJERA

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Concrete is the safest and sustainable construction material wh ich is most widely used in the world as it provides superior fire resistance, gains strength over time and gives an extremely long service life. Unfortunately high performance concrete is undoubtedly one of the most innovativ e materials in construction. Its Designing involves the process of selecting suitable ingredients of concrete (water, cement, fine and aggregates and a number of additives like mineral and chemical ad mixture) and determining their relat ive amounts with the objective of producing a high performance concrete of the required, strength, durability, and workab ility as economically as possible. Their proportions have a high influence on the final strength of the product. These relations do not seem to follow a mathematical formu la and yet their knowledge is crucial to optimize the quantities of raw materials used in the manufacture of high performance concrete. Therefore, it would be important to have a tool to numerically model such relationships, even before pro cessing. In this aspect the main purpose of this paper is to predict the compressive strength of the high performance concrete by using classification algorith ms. For building these models, training and testing using the available experimental results for 1030 specimens produced with 8 d ifferent mixture proportions are used. The result fro m this study suggests that weighted Support Vector Machines (wSVM) based models perform remarkably well in predict ing the compressive strength of the concrete mix.

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HPC Strength Prediction Using Artificial Neural Network

Cited by 182 publications

References 13 publications

Neural networks for wave forecasting

Neural networks for wave forecasting

An Artificial Neural Networks Model for Predicting Permeability Properties of Nano Silica–Rice Husk Ash Ternary Blended Concrete

High-Performance Concrete Compressive Strength Prediction Based Weighted Support Vector Machines

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