Compressive Strength Prediction of Lightweight Concrete: Machine Learning Models

Aman Kumar, Harish Chandra Arora, Nishant Raj Kapoor, Mazin Abed Mohammed, Krishna Kumar, Arnab Majumdar and Orawit Thinnukool
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Aman Kumar: AcSIR—Academy of Scientific and Innovative Research, Ghaziabad 201002, India
Harish Chandra Arora: AcSIR—Academy of Scientific and Innovative Research, Ghaziabad 201002, India
Nishant Raj Kapoor: AcSIR—Academy of Scientific and Innovative Research, Ghaziabad 201002, India
Mazin Abed Mohammed: College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq
Krishna Kumar: Department of Hydro and Renewable Energy, Indian Institute of Technology, Roorkee 247667, India
Arnab Majumdar: Department of Civil Engineering, Imperial College London, London SW7 2AZ, UK
Orawit Thinnukool: College of Arts, Media, and Technology, Chiang Mai University, Chiang Mai 50200, Thailand

Sustainability, 2022, vol. 14, issue 4, 1-22

Abstract: Concrete is the most commonly used construction material. The physical properties of concrete vary with the type of concrete, such as high and ultra-high-strength concrete, fibre-reinforced concrete, polymer-modified concrete, and lightweight concrete. The precise prediction of the properties of concrete is a problem due to the design code, which typically requires specific characteristics. The emergence of a new category of technology has motivated researchers to develop mechanical strength prediction models using Artificial Intelligence (AI). Empirical and statistical models have been extensively used. These models require a huge amount of laboratory data and still provide inaccurate results. Sometimes, these models cannot predict the properties of concrete due to complexity in the concrete mix design and curing conditions. To conquer such issues, AI models have been introduced as another approach for predicting the compressive strength and other properties of concrete. This article discusses machine learning algorithms, such as Gaussian Progress Regression (GPR), Support Vector Machine Regression (SVMR), Ensemble Learning (EL), and optimized GPR, SVMR, and EL, to predict the compressive strength of Lightweight Concrete (LWC). The simulation approaches of these trained models indicate that AI can provide accurate prediction models without undertaking extensive laboratory trials. Each model’s applicability and performance were rigorously reviewed and assessed. The findings revealed that the optimized GPR model (R = 0.9803) used in this study had the greatest accuracy. In addition, the optimized SVMR and GPR model showed good performance, with R-values 0.9777 and 0.9740, respectively. The proposed model is economic and efficient, and can be adopted by researchers and engineers to predict the compressive strength of LWC.

Keywords: lightweight concrete; lightweight aggregate; GPR; SVMR; Ensemble Learning; compressive strength; machine leaning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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