Assessment of pile drivability using random forest regression and multivariate adaptive regression splines

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Wengang Zhang ◽  
Chongzhi Wu ◽  
Yongqin Li ◽  
Lin Wang ◽  
P. Samui
Keyword(s):  
Random Forest ◽  
Multivariate Adaptive Regression Splines ◽  
Regression Splines ◽  
Random Forest Regression ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

scholarly journals Novel Approaches to Modelling Flammability Characteristics of Polymethyl Methacrylate (PMMA) via Multivariate Adaptive Regression Splines and Random Forest Methods

2020 ◽  
pp. 1-14
Author(s):  
Mohammed Okoe Alhassan ◽  
Michael Boakye Osei
Keyword(s):  
Random Forest ◽  
Polymethyl Methacrylate ◽  
Polymeric Materials ◽  
Multivariate Adaptive Regression Splines ◽  
Coefficient Of Determination ◽  
Regression Splines ◽  
Soft Computing Techniques ◽  
Flammability Characteristics ◽  
Adaptive Regression ◽  
Adaptive Regression Splines

Soft-computing techniques for fire safety parameter predictions in flammability studies are essential for describing a material fire behaviour. This study proposed, two novel Artificial Intelligence developed models, Multivariate Adaptive Regression Splines (MARS) and Random Forest (RF) methods, to model and predict peak heat release rate (pHRR) of Polymethyl methacrylate (PMMA) from Microscale Combustion Calorimetry (MCC) experiment. From the statistical analysis, MARS presented the highest coefficient of determination (R2) values of (0.9998) and (0.9996) for training and testing respectively, with low MAD, MAPE and RMSE values. Comparatively, MARS outperformed RF in the predictions of pHRR, through its model algorithms that generated optimized equations for pHRR predictions, covering all non-linearity points of the experimental data. Amongst the input variables (sample mass, THR, HRC, pTemp and pTime), heating rate (β), highly influenced pHRR outcome predictions from MARS and RF models. However, to validate the performance and applicability of the proposed models. Results of MARS and RF were benchmarked with that from Artificial Neural Network (ANN) methods. The MARS and RF models observed the least error deviation when compared with pHRR results for PMMA from the ANN models. This study therefore, recommends the adoption of MARS and RF in the predictions of flammability characteristics of polymeric materials.

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