Author
Listed:
- Aman Kumar
(AcSIR—Academy of Scientific and Innovative Research, Ghaziabad 201002, India
Structural Engineering Department, CSIR—Central Building Research Institute, Roorkee 247667, India)
- Harish Chandra Arora
(AcSIR—Academy of Scientific and Innovative Research, Ghaziabad 201002, India
Structural Engineering Department, CSIR—Central Building Research Institute, Roorkee 247667, India)
- Nishant Raj Kapoor
(AcSIR—Academy of Scientific and Innovative Research, Ghaziabad 201002, India
Architecture and Planning Department, CSIR—Central Building Research Institute, Roorkee 247667, 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)
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.
Suggested Citation
Aman Kumar & Harish Chandra Arora & Nishant Raj Kapoor & Mazin Abed Mohammed & Krishna Kumar & Arnab Majumdar & Orawit Thinnukool, 2022.
"Compressive Strength Prediction of Lightweight Concrete: Machine Learning Models,"
Sustainability, MDPI, vol. 14(4), pages 1-22, February.
Handle:
RePEc:gam:jsusta:v:14:y:2022:i:4:p:2404-:d:753589
Download full text from publisher
Citations
Citations are extracted by the
CitEc Project, subscribe to its
RSS feed for this item.
Cited by:
- Hamed Safayenikoo & Fatemeh Nejati & Moncef L. Nehdi, 2022.
"Indirect Analysis of Concrete Slump Using Different Metaheuristic-Empowered Neural Processors,"
Sustainability, MDPI, vol. 14(16), pages 1-16, August.
- Ehsan Mansouri & Maeve Manfredi & Jong-Wan Hu, 2022.
"Environmentally Friendly Concrete Compressive Strength Prediction Using Hybrid Machine Learning,"
Sustainability, MDPI, vol. 14(20), pages 1-17, October.
- Nishant Raj Kapoor & Ashok Kumar & Anuj Kumar & Dilovan Asaad Zebari & Krishna Kumar & Mazin Abed Mohammed & Alaa S. Al-Waisy & Marwan Ali Albahar, 2022.
"Event-Specific Transmission Forecasting of SARS-CoV-2 in a Mixed-Mode Ventilated Office Room Using an ANN,"
IJERPH, MDPI, vol. 19(24), pages 1-27, December.
- Ahmed M. Ebid & Ahmed Farouk Deifalla & Hisham A. Mahdi, 2022.
"Evaluating Shear Strength of Light-Weight and Normal-Weight Concretes through Artificial Intelligence,"
Sustainability, MDPI, vol. 14(21), pages 1-49, October.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2404-:d:753589. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.