Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete-Reference-Cited by-同舟云学术

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

Published:2018 Issue: Volume:2018 Page:1-9
ISSN:1687-8086
Container-title:Advances in Civil Engineering
language:en
Short-container-title:Advances in Civil Engineering

Author:

Chopra Palika¹^ORCID,Sharma Rajendra Kumar¹,Kumar Maneek²^ORCID,Chopra Tanuj²^ORCID

Affiliation:

1. Department of Computer Science and Engineering, Thapar University, Patiala, India

2. Department of Civil Engineering, Thapar University, Patiala, India

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.

Publisher

Hindawi Limited

Subject

Civil and Structural Engineering

Link

http://downloads.hindawi.com/journals/ace/2018/5481705.pdf

Reference37 articles.

1. Prediction of compressive strength of concrete by neural networks

2. Prediction of cement strength using soft computing techniques

3. Fuzzy logic model for the prediction of cement compressive strength

4. Predicting the compressive strength and slump of high strength concrete using neural network

5. Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks

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