0
  • DE
  • EN
  • FR
  • International Database and Gallery of Structures

Advertisement

Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete

Author(s):



Medium: journal article
Language(s): English
Published in: Advances in Civil Engineering, , v. 2018
Page(s): 1-9
DOI: 10.1155/2018/5481705
Abstract:

A comparative analysis for the prediction of compressive strength of concrete at the ages of 28, 56, and 91 days has been carried out using machine learning techniques via “R” software environment. R is digging out a strong foothold in the statistical realm and is becoming an indispensable tool for researchers. The dataset has been generated under controlled laboratory conditions. Using R miner, the most widely used data mining techniques decision tree (DT) model, random forest (RF) model, and neural network (NN) model have been used and compared with the help of coefficient of determination (R²) and root-mean-square error (RMSE), and it is inferred that the NN model predicts with high accuracy for compressive strength of concrete.

Copyright: © 2018 Palika Chopra et al.
License:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10176758
  • Published on:
    30/11/2018
  • Last updated on:
    02/06/2021
 
Structurae cooperates with
International Association for Bridge and Structural Engineering (IABSE)
e-mosty Magazine
e-BrIM Magazine