Journal of Materials and Engineering Structures
Volume 6, Numéro 4, Pages 583-892
2019-12-30
Estimation Of Compressive Strength Of High-strength Concrete By Random Forest And M5p Model Tree Approaches
Authors : Singh Balraj . Sihag Parveen . Tomar Anjul . Sehgad Ankush .
Abstract
High strength concrete (HSC) define as the concrete that meets a unique mixture of performance uniformity requirements that cannot be reached routinely using conventional constituents and regular mixing, placing, and curing events. The modeling of such type of concrete is very difficult. In this investigation, the performance of the random forest regression and M5P model tree were compared to estimate the 28th day compressive strength of the HSC. Total data set consists of 83 data out of which 70 % of the total dataset used to train the model and residual 30 % used to test the models. The accuracy of the models was depending upon the three performance evaluation parameters which are correlation coefficient (R), root mean square error (RMSE) and maximum absolute error (MAE). The results recommend that random forest regression is more accurate to predict the compressive strength as compare to M5P model tree. Sensitivity analysis indicates that water (W) and Silica fumes (SF) are the most valuable constituents of the HSC and compressive strength mainly depends on these constituents.
Keywords
High strength concretes; M5P model tree; Random forest; Silica fume
Les articles similaires
Sihag Parveen
.
Kumar Munish
.
Singh Varun
.
pages 93-103.
Muktadir Munshi Galib
.
Alam M I Fahim
.
Rahman Asifur
.
Haque Mohammad Robiul
.
pages 73-82.
Saadi Imene
.
Benmarce Abdelaziz
.
pages 106-116.
Folagbade Samuel Olufemi
.
Osadola Opeyemi Ayodeji
.
pages 455-463.
Boakye Daniel M.
.
Uzoegbo Herbert C.
.
Mojagotlhe Nonhlanhla
.
Malemona Moeti
.
pages 11-21.
