Prediction of concrete compressive strength using non-destructive test results


Erdal H., ERDAL M., ŞİMŞEK O., Erdal H. I.

COMPUTERS AND CONCRETE, vol.21, no.4, pp.407-417, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 21 Issue: 4
  • Publication Date: 2018
  • Doi Number: 10.12989/cac.2018.21.4.407
  • Journal Name: COMPUTERS AND CONCRETE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.407-417
  • Keywords: concrete, compressive strength, machine learning regression, non-destructive testing, ARTIFICIAL NEURAL-NETWORKS, ULTRASONIC PULSE VELOCITY, COMPUTATIONAL INTELLIGENCE TECHNIQUES, BAGGING ENSEMBLE MODELS, FUZZY-LOGIC, DESIGN PARAMETERS, SLUMP FLOW, FLY-ASH, REGRESSION, ALGORITHM
  • Gazi University Affiliated: Yes

Abstract

Concrete which is a composite material is one of the most important construction materials. Compressive strength is a commonly used parameter for the assessment of concrete quality. Accurate prediction of concrete compressive strength is an important issue. In this study, we utilized an experimental procedure for the assessment of concrete quality. Firstly, the concrete mix was prepared according to C 20 type concrete, and slump of fresh concrete was about 20 cm. After the placement of fresh concrete to formworks, compaction was achieved using a vibrating screed. After 28 day period, a total of 100 core samples having 75 mm diameter were extracted. On the core samples pulse velocity determination tests and compressive strength tests were performed. Besides, Windsor probe penetration tests and Schmidt hammer tests were also performed. After setting up the data set, twelve artificial intelligence (Al) models compared for predicting the concrete compressive strength. These models can be divided into three categories (i) Functions (i.e., Linear Regression, Simple Linear Regression, Multilayer Perceptron, Support Vector Regression), (ii) Lazy-Learning Algorithms (i.e., IBk Linear NN Search, KStar, Locally Weighted Learning) (iii) TreeBased Learning Algorithms (i.e., Decision Stump, Model Trees Regression, Random Forest, Random Tree, Reduced Error Pruning Tree). Four evaluation processes, four validation implements (i.e., 10-fold cross validation, 5-fold cross validation, 10% split sample validation & 20% split sample validation) are used to examine the performance of predictive models. This study shows that machine learning regression techniques are promising tools for predicting compressive strength of concrete.