Compressive Strength Prediction of Lightweight Concrete : Machine Learning Models
Kumar, Aman ; Arora, Harish Chandra ; Kapoor, Nishant Raj ; Mohammed, Mazin Abed ; Kumar, Krishna LU
; Majumdar, Arnab
and Thinnukool, Orawit
(2022)
In Sustainability (Switzerland)
14(4).
- 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... (More)
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.
(Less)
- author
- Kumar, Aman
; Arora, Harish Chandra
; Kapoor, Nishant Raj
; Mohammed, Mazin Abed
; Kumar, Krishna
LU
; Majumdar, Arnab
and Thinnukool, Orawit
- publishing date
- 2022-02-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Compres-sive strength, Ensemble Learning, GPR, Lightweight aggregate, Lightweight concrete, Machine leaning, SVMR
- in
- Sustainability (Switzerland)
- volume
- 14
- issue
- 4
- article number
- 2404
- publisher
- MDPI AG
- external identifiers
-
- scopus:85125069523
- ISSN
- 2071-1050
- DOI
- 10.3390/su14042404
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
- id
- 32e9f879-4407-4e47-89e2-fc5eaa2008c7
- date added to LUP
- 2024-04-15 12:34:33
- date last changed
- 2024-05-22 09:04:17
@article{32e9f879-4407-4e47-89e2-fc5eaa2008c7,
abstract = {{<p>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.</p>}},
author = {{Kumar, Aman and Arora, Harish Chandra and Kapoor, Nishant Raj and Mohammed, Mazin Abed and Kumar, Krishna and Majumdar, Arnab and Thinnukool, Orawit}},
issn = {{2071-1050}},
keywords = {{Compres-sive strength; Ensemble Learning; GPR; Lightweight aggregate; Lightweight concrete; Machine leaning; SVMR}},
language = {{eng}},
month = {{02}},
number = {{4}},
publisher = {{MDPI AG}},
series = {{Sustainability (Switzerland)}},
title = {{Compressive Strength Prediction of Lightweight Concrete : Machine Learning Models}},
url = {{http://dx.doi.org/10.3390/su14042404}},
doi = {{10.3390/su14042404}},
volume = {{14}},
year = {{2022}},
}