Artificial neural network evaluation of cement-bonded particle board produced from red ironwood (Lophira alata) sawdust and palm kernel shell residues

Atoyebi, O. D. and Awolusi, T. F. and Davies, I. E. E. (2018) Artificial neural network evaluation of cement-bonded particle board produced from red ironwood (Lophira alata) sawdust and palm kernel shell residues. Case Studies in Construction Materials, 9. ISSN 2214-5095

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Abstract

As a way of promoting environmental sustainability, it becomes paramount to salvage the quantity of agricultural wastesbeingdestroyedordisposedintotheenvironment.Anovelstrategytoreducethesewastesisbyreusingthem.Inthepresentstudy,thephysicalandmechanicalpropertiesofparticleboardsproducedfromredironwood(Lophiraalata)sawdustandpalmkernelshell(PKS)wasevaluatedbyartificialneuralnetwork(ANN).Theproductionofthisparticleboardsinvolvedthesynergisticcombinationofeffectiveparameterssuchaspercentagecompositionofcement,sawdustandpalmkernelshellvariedbetween25–40,20–50and20–50respectively.Theboardsweretestedforphysicalpropertiessuchaswaterabsorption(WA),thicknessswelling(TS),densityandmechanicalpropertiessuchasmodulusofrupture(MOR)andmodulusofelasticity(MOE).ThenetworkswastrainedandtestedbyMultilayerNormalFeedForwardPerceptron(MNFFP),withaquickpropagationlearningalgorithm.TheperformanceoftheANNnetworkshowsithasahighpotentialforpredictingthepropertiesofcementbondedparticleboard.©2018TheAuthors.PublishedbyElsevierLtd.ThisisanopenaccessarticleundertheCC

Item Type:	Article
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Depositing User:	Mr DIGITAL CONTENT CREATOR LMU
Date Deposited:	04 Oct 2019 13:11
Last Modified:	04 Oct 2019 13:11
URI:	https://eprints.lmu.edu.ng/id/eprint/2483

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