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Investigation on Tensile and Flexural Behaviour of Fibre Reinforced Concrete Using Artificial Neural Network

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A.Dhanalakshmi et al. View all

Issue 1

Solid Waste Management

The purpose of this study is to investigate the impact that using marble sludge powder as a partial replacement for cement in concrete can have. Experiments were conducted to investigate a variety of characteristics of fiber-reinforced concrete using both fresh concrete and concrete that had been...

Fibre Reinforced Concrete Polypropylene 3s +1 more
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Artificial Neural Network and Weighted Arithmetic Indexing Approach for Surface Water Quality Assessment of the Brahmani River

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Madhusmita Ghadai et al. View all

Issue 4

Water Quality

An approach was used in this study to relate the predicted and calculated water quality index (WQI) of the Brahmani River. The WQI was predicted using an artificial neural network (ANN) tool, and the weighted Arithmetic Index technique was used to calculate the WQI (WAI). The WQI is calculated using...

ANN Brahmani River +3 more
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Estimation of Waste Mobile Phones in the Philippines using Neural Networks

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Mark Gino K. Galang et al. View all

Issue 4

CEST2017 - Electric and electronic waste

Waste mobile phone is one of the subgroups of e-waste which is defined as discarded electronic products in the Philippine context. This study estimated current and projected quantities of waste mobile phones in the country using feed forward neural network. The neural network architecture used had...

Feed-forward neural network eWaste +1 more
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Measurement and prediction of karstic spring flow rates

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K.L. Katsifarakis et al. View all

Issue 2

Water Resources Management

This paper deals with prediction of the response of karstic springs by means of artificial neural networks (ANNs). A feed-forward back propagation ANN with three layers has been developed, to predict flow rates of two karstic springs, located at Rouvas area, Crete, Greece, using rainfall data as...

karstic aquifer karstic spring +2 more
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Water level prediction by artificial neural network in a flashy transboundary river of Bangladesh

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ROBIN K. BISWAS et al. View all

Issue 2

Water Resources Management

This paper presents the sensitivity analysis results of feed forward multilayer perceptron based Artificial Neural Network model for water level prediction in a data constraint transnational Surma River of Bangladesh. Catchment characteristics, hydro-geomorphological, meteorological and headwater...

Flood Forecasting Stochastic Hydrological Modelling +4 more
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Artificial neural networks predictive models. A case study: carbon and bromine concentrations prediction based on chlorination time

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Farmaki E.G., Samios S.A., Thomaidis N.S., Golfinopoulos S., Efstathiou C.E. and Lekkas T.D.

Issue 1

General

Artificial neural networks (ANNs) are being used increasingly to predict water variables. This study offers an alternative approach to quantify the relationship between time of chlorination in potable water (due to convectional treatment procedure) and chlorination by-products concentration...

ANNs CBPs +3 more
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Neural networks approached for modelling river suspended sediment concentration due to tropical storms

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Wang Y-M., Kerh T. and Traore S.

Issue 4

General

Artificial neural networks are one of the advanced technologies employed in hydrology modelling. This paper investigates the potential of two algorithm networks, the feed forward backpropagation (BP) and generalized regression neural network (GRNN) in comparison with the classical regression for...

event-based sediment turbidity +4 more
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