Computing with words for solving the fuzzy transportation problem

Pratihar, Jayanta; Dey, Arindam; Khan, Abhinandan; Banerjee, Pritha; Pal, Rajat Kumar

doi:10.1007/s00500-023-08958-4

Cited by 3 publications

(2 citation statements)

References 42 publications

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“…As indicated in above methodology section, the two models FFBP-ANN model and RBF-ANN model are used to predict pressure distribution along the height of the vertical stack by taking the discharging floor height and discharge flow rate as input parameters. The sample size of 88 satisfies the criteria mentioned by Pratihar 18 and has been used for both the models. The criteria relate the value of training scenarios(L) with network parameters (i.e., weights and bias).…”

Section: Resultsmentioning

confidence: 99%

“…Pratihar 18 mathematically defined error (Ek) as the representation of the difference between the target data and predicted data from the output layer aswhere TOk is the target data and OOk is the predicted data from the output layer. The steps followed through equations (1)–(6), outline the mathematical representation of the Feed Forward Artificial Neural Network (FFANN) algorithm with given weight vectors, bias and activation functions.…”

Section: Ann Modellingmentioning

confidence: 99%

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Artificial neural network modelling for predicting classical air pressure profile curves in building drainage systems

Mahapatra

Gormley

2023

Building Services Engineering Research and Technology

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Water trap seal loss can lead to the ingress of foul odours from the sewer network carrying harmful pathogens which can affect the health of building occupants. This loss is due to excessive negative or positive pressures within the system as a consequence of rapidly changing flow due to the unsteady nature of air and water flow discharges from appliances. This pressure regime within the system is often represented by a pressure profile along the height of the main vertical stack in the Building Drainage System (BDS). Experimental data from peer-reviewed literature and data from a unique 34-storey drainage test rig have been used as pressure profile data (Test data) for an Artificial Neural Network (ANN) model. Discharge input height (storey number) and discharge flow rate are considered to be the two independent input parameters and the pressure along the vertical stack is considered to be the output. In this work, both a Feed Forward and Back Propagation (FFBP) ANN model and a Radial Basis Function (RBF) ANN model have been used to train the algorithm. The work has confirmed the applicability of the FFBP-ANN model for steady two-phase fluid flow phenomena in BDS and allows for the prediction of pressures in a system for which no pressure data exists, by the prediction of modelled weights, based only on its physical and flow characteristics. Practical Application: Of great concern to designers of building drainage systems (BDS) is the control of pressure fluctuations within the system to prevent water trap seal loss. Prediction of a pressure profile based on the characteristics of the building – e.g. height, location of water discharge, discharge flow rate and ventilation type, would therefore increase confidence in designs, particularly in tall buildings. The work presented here addresses, for the first time, the applications of two ANN models for predicting the pressure profile in the BDS vertical stack of multi-storey buildings.

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Section: Resultsmentioning

confidence: 99%