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

Atoyebi, O. D.; Awolusi, T.F.; Davies, Iyinoluwa E.E.

doi:10.1016/j.cscm.2018.e00185

Cited by 34 publications

(18 citation statements)

References 20 publications

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“…The value of R 2 and AFV obtained signifies a close relationship between the experimental values and the predicted values. Atoyebi et al [32] has identified that the closer this value is to one (1) the better is the predicting ability of the obtained model [32]. Figure 6 to 8 presents the the main effect plot for the three process variables.…”

Section: Derived Model Using Ann-gamentioning

confidence: 92%

<strong></strong>Comparison of Response Surface Methodology and Hybrid-Training Approach of Artificial Neural Network in Modeling the Properties of Concrete Containing Steel Fiber Extracted from Waste Tires

Awolusi¹,

Oke²,

Akinkurolere³

et al. 2019

Preprint

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The study presents a comparative approach between response surface methodology (RSM) and hybridized, genetic algorithm artificial neural network (GA-ANN) in predicting the water absorption, compressive strength, flexural strength split tensile strength and slump for steel fiber reinforced concrete. The effect of process variables such as aspect ratio, water cement ratio and cement content were investigated using the central composite design of response surface methodology.  This same experimental design was used in training the hybrid-training approach of artificial neural network. The predicting ability of both methodologies were compared using the  root mean sqaured error (RMSE), mean absolute error (MAE), model predictive error (MPE) and absolute average deviation (AAD). The RSM model was found more accurate in prediction compared to hybrid GA-ANN.

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Section: Derived Model Using Ann-gamentioning

confidence: 92%

<strong></strong>Comparison of Response Surface Methodology and Hybrid-Training Approach of Artificial Neural Network in Modeling the Properties of Concrete Containing Steel Fiber Extracted from Waste Tires

Awolusi¹,

Oke²,

Akinkurolere³

et al. 2019

Preprint

Self Cite

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“…Wood-cement composites measuring 12 mm × 500 mm × 500 mm with target density of 1300 kg/m 3 were prepared in the laboratory conditions. The amount of sunflower stalk particles added to wood particles was 0, 25, 50, 75 and 100 % based on the wood particle weight.…”

Section: Materijali I Metodementioning

confidence: 99%

Sunflower (Helianthus Annuus) Stalks as Alternative Raw Material for Cement Bonded Particleboard

Taş

Kul

2020

Drv. ind. (Online)

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Sunflower stalks (Helianthus annuus) were studied as an alternative raw material for cement bonded particleboard. Experimental cement bonded particleboards measuring 500 mm × 500 mm × 12 mm with nominal density of 1300 kg/m3 were produced using different ratios of sunflower stalk particles with wood. Properties of the cement bonded particleboards evaluated include water absorption, thickness swelling, screw withdrawal strength and bending properties. Results of the study showed that the addition of small amount of sunflower stalks in the production of cement bonded particleboard does not significantly influence the properties tested. Inclusion of more stalks in the mixture significantly decreases mechanical properties and raises thickness swelling and water absorption values of the cement bonded particleboard. Results indicate that boards which include a small amount of sunflower stalks provide properties required by the standards for general purpose-use cement bonded particleboards.

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“…Neural networks are also used as part of urban planning and for the analysis of aerial photographs in the interests of urban economy and construction [71][72], in waste management systems [73][74].…”

Section: Smart Citymentioning

confidence: 99%

Applying Artificial Neural Networks In Construction

Doroshenko

2020

E3S Web Conf.

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Currently, artificial neural networks (ANN) are used to solve the following complex problems: pattern recognition, speech recognition, complex forecasts and others. The main applications of ANN are decision making, pattern recognition, optimization, forecasting, data analysis. This paper presents an overview of applications of ANN in construction industry, including energy efficiency and energy consumption, structural analysis, construction materials, smart city and BIM technologies, structural design and optimization, application forecasting, construction engineering and soil mechanics.

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

Cited by 34 publications

References 20 publications

<strong></strong>Comparison of Response Surface Methodology and Hybrid-Training Approach of Artificial Neural Network in Modeling the Properties of Concrete Containing Steel Fiber Extracted from Waste Tires

<strong></strong>Comparison of Response Surface Methodology and Hybrid-Training Approach of Artificial Neural Network in Modeling the Properties of Concrete Containing Steel Fiber Extracted from Waste Tires

Sunflower (Helianthus Annuus) Stalks as Alternative Raw Material for Cement Bonded Particleboard

Applying Artificial Neural Networks In Construction

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