Estimation of strengths of hybrid FR‐SCC by using deep‐learning and support vector regression models

Abstract

In this work, to estimate the compressive, splitting tensile, and flexural strength of self‐compacting concrete (SCC) having single fiber and binary, ternary, and quaternary fiber hybridization, the deep‐learning (DL) and support vector regression (SVR) models were devised. The fiber content and coarse aggregate/total aggregate ratio (CA/TA) were the variables for 24 designed mixtures. Four different fibers, which were a macro steel fiber, two types of micro steel fibers with different aspect ratio, and polyvinyl alcohol (PVA) fiber, were used in SCC mixtures. The specimens of each mixture were tested to measure the engineering properties for 7, 28, and 90 days. The amount of cement, fly ash, fine aggregate, CA, high‐range water‐reducing admixture, water, macro steel fiber, PVA fiber, two types of micro steel fibers, and curing time were selected as input layers while the output layers were strength results. The experimental results were compared with the estimation results. The engineering properties were estimated using the SVR model with 95.25%, 87.81%, and 93.89% accuracy, respectively. Furthermore, the DL model estimated compressive strength, tensile strength, and flexural strength with 99.27%, 98.59%, and 99.15% accuracy, respectively. It was found that the DL estimated the engineering properties of hybrid fiber–reinforced SCC with higher accuracy than SVR.

Forecasting residual mechanical properties of hybrid fibre-reinforced self-compacting concrete (HFR-SCC) exposed to elevated temperatures

“…The study demonstrated that out of all the boosting algorithms employed, extreme gradient boosting algorithm proved to be the most accurate depicting the least absolute error of 1.43 MPa between real and predicted values. Similarly, Kina et al [ 56 ] utilized deep learning and SVM to estimate cs, fs, and split-tensile strength of SCC containing a mix of macro steel and micro synthetic fibres. The authors prepared 24 laboratory samples and measured their strength at 7, 28 and 90 days, then utilized that data to build ML models and concluded that deep learning predicted the SCC strength reinforced with different fibres more accurately than SVM.…”

Comparative analysis of various machine learning algorithms to predict 28-day compressive strength of Self-compacting concrete

“… SVM, ANN, GEP 300 C–S 2021 Fly ash [ 37 ] 16. SVR, Deep Learning (DL) 24 C–S, Splitting tensile strength 2021 Fly ash, Silica fume [ 52 ] …”

Effect of Fiber Type, Shape and Volume Fraction on Mechanical and Flexural Properties of Concrete

“…In order to improve the engineering properties of concrete in terms of brittle-ness, post-cracking capability and burst failure, short and randomly distributed fibers can be gradually added into concrete [2,3]. Adding fibers such as basalt, polypropylene (PP), glass, and steel fibers into cement-based composites is so common to upgrade the tensile performance and mechanical properties [4][5][6][7][8][9][10]. These advantageous properties of fibers cause an increase in the application of fiber-reinforced concrete throughout the world.…”