Forecast flexural strength of pervious concrete by SVR. (2021)
- Record Type:
- Journal Article
- Title:
- Forecast flexural strength of pervious concrete by SVR. (2021)
- Main Title:
- Forecast flexural strength of pervious concrete by SVR
- Authors:
- Bin Ahmed, Farabi
Mitu, Sadia Mannan
Biswas, Rajib Kumar
Abid Ahsan, Khan
Mim, Sabrina Mostarin
Ahmed, Saif - Abstract:
- Abstract: For the development of construction technology, pervious concrete is vastly using in road infrastructure. Pervious concrete has a strong drainage ability that can recharge groundwater, made by the coarse aggregate, water, and cement with little fine aggregate or without fine aggregate. The compressive and flexural strengths of pervious concrete are vital parameters to identify the concrete quality and also necessary in the process of analysis and design of a concrete infrastructure. This research represents a system for forecasting the flexural strength obtained from the compressive strength (0 up to 30 MPa) of concrete cube and cylinder types specimens by support vector regression (SVR) for both 7 and 28 days. Experimental data from various literature have been collected to formulate the model. It appears that the relationship between the compressive strength and flexural strength of pervious concrete is non-linear. The linear models developed with limited lab test found ineffective when they are subjected to wide range of data set for flexural strength prediction. In this study, all compared models and developing model's effectiveness are examined based on R 2, RMSE, MAPE, MSE, and IAE parameters for both 7- and 28-days specimens.
- Is Part Of:
- Materials today. Volume 45:Part 6(2021)
- Journal:
- Materials today
- Issue:
- Volume 45:Part 6(2021)
- Issue Display:
- Volume 45, Issue 6, Part 6 (2021)
- Year:
- 2021
- Volume:
- 45
- Issue:
- 6
- Part:
- 6
- Issue Sort Value:
- 2021-0045-0006-0006
- Page Start:
- 5277
- Page End:
- 5284
- Publication Date:
- 2021
- Subjects:
- Compressive strength -- Flexural strength -- Kernel radial basis function regression analysis -- Power regression analysis -- Support vector machine
Materials science -- Congresses -- Periodicals
620.1 - Journal URLs:
- http://www.sciencedirect.com/science/journal/22147853 ↗
http://www.sciencedirect.com/ ↗ - DOI:
- 10.1016/j.matpr.2021.01.832 ↗
- Languages:
- English
- ISSNs:
- 2214-7853
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 17169.xml