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Amelioration of sandwich panels by replacing polyurethane foam with coconut husk and study on computational prediction using ANN and LR

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Abstract

In recent times, agricultural byproducts are being used as an economical and lightweight alternative for traditional building materials to provide solution for sustainable construction. The aim of this study is to develop a sandwich panel of 30 × 30 cm with core thickness of 2 and 3 cm, and to predict their mechanical properties with Linear Regression (LR) and an Artificial Neural Network (ANN) model. Sandwich panel was prepared with Calcium Silicate Board as face sheet material with Waste Coconut Husk (CH) and Polyurethane Foam (PU) as a binding material. PU is composed of isocyanate (A) and polyol (B) and was used in a ratio of 1:1.2. CH is obtained as a byproduct from the production of coconut shells aggregate. Compressive, flexural, water absorption test and SEM analysis for the sandwich panel with PU and PU replaced with CH at 10, 20, 30, 40 and 50% were carried out. From the test results, the sandwich panel with 20% CH yields higher compression and flexural strengths, which proves to be the optimum level of CH. To predict mechanical properties over the defined range of conditions, LR and ANN model was built. The analysis shows the error with less than 4%. Comparing with experimental and predicted values, the R2 > 0.90 in ANN and R2 < 0.90 in LR model, the ANN model outperforms the LR with higher accuracy.

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  1. Department of Civil Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India

    Prerna Sharma & V. R. Prasath Kumar

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Sharma, P., Kumar, V.R.P. Amelioration of sandwich panels by replacing polyurethane foam with coconut husk and study on computational prediction using ANN and LR. Innov. Infrastruct. Solut. 8, 331 (2023). https://doi.org/10.1007/s41062-023-01284-6

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