Soft computing techniques for the prediction of concrete compressive strength using Non-Destructive tests. (11th October 2021)

• Record Type: Journal Article
• Authors: Asteris, Panagiotis G.; Skentou, Athanasia D.; Bardhan, Abidhan; Samui, Pijush; Lourenço, Paulo B.
• Is Part Of: Construction & building materials. Volume 303(2021)
• Publication Date: 2021-10-11
• Subjects: Artificial neural networks -- Compressive strength of Concrete -- Non-destructive testing methods -- Soft computing -- Artificial Intelligence; Building materials -- Periodicals; 624.18
• DOI: 10.1016/j.conbuildmat.2021.124450
• Languages: English
• ISSNs: 0950-0618

Abstract

Graphical abstract: Highlights: Prediction of concrete compressive strength using six widely used soft computing models. The generated GP expressions can be utilized to estimate concrete compressive strength directly. Determination of best performing model using a20-index. Comparative assessment of results based on earlier studies. Abstract: This study presents a comparative assessment of conventional soft computing techniques in estimating the compressive strength (CS) of concrete utilizing two non-destructive tests, namely ultrasonic pulse velocity and rebound hammer test. In specific, six conventional soft computing models namely back-propagation neural network (BPNN), relevance vector machine, minimax probability machine regression, genetic programming, Gaussian process regression, and multivariate adaptive regression spline, were used. To construct and validate these models, a total of 629 datasets were collected from the literature. Experimental results show that the BPNN attained the most accurate prediction of concrete CS based on both ultrasonic pulse velocity and rebound number values. The results of the employed MARS and BPNN models are significantly better than those obtained in earlier studies. Thus, these two models are very potential to assist engineers in the design phase of civil engineering projects to estimate the concrete CS with a greater accuracy level.