Assessment of soft computing techniques for the prediction of compressive strength of bacterial concrete
Authors:
- Fadi Almohammed,
- Parveen Sihag,
- Saad Sh. Sammen,
- Krzysztof Adam Ostrowski,
- Karan Singh,
- C. Venkata Siva Rama Prasad,
- Paulina Zajdel
Abstract
In this investigation, the potential of M5P, Random Tree (RT), Reduced Error Pruning Tree (REP Tree), Random Forest (RF), and Support Vector Regression (SVR) techniques have been evaluated and compared with the multiple linear regression-based model (MLR) to be used for prediction of the compressive strength of bacterial concrete. For this purpose, 128 experimental observations have been collected. The total data set has been divided into two segments such as training (87 observations) and testing (41 observations). The process of data set separation was arbitrary. Cement, Aggregate, Sand, Water to Cement Ratio, Curing time, Percentage of Bacteria, and type of sand were the input variables, whereas the compressive strength of bacterial concrete has been considered as the final target. Seven performance evaluation indices such as Correlation Coefficient (CC), Coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Bias, Nash-Sutcliffe Efficiency (NSE), and Scatter Index (SI) have been used to evaluate the performance of the developed models. Outcomes of performance evaluation indices recommend that the Polynomial kernel function based SVR model works better than other developed models with CC values as 0.9919, 0.9901, R2 values as 0.9839, 0.9803, NSE values as 0.9832, 0.9800, and lower values of RMSE are 1.5680, 1.9384, MAE is 0.7854, 1.5155, Bias are 0.2353, 0.1350 and SI are 0.0347, 0.0414 for training and testing stages, respectively. The sensitivity investigation shows that the curing time (T) is the vital input variable affecting the prediction of the compressive strength of bacterial concrete, using this data set.
- Record ID
- CUT38a15f9cbe064f45a86f963f690d49d3
- Publication categories
- ;
- Author
- Journal series
- Materials, ISSN , e-ISSN 1996-1944, Biweekly
- Issue year
- 2022
- Vol
- 15
- No
- 2
- Pages
- [1-17]
- Article number
- 489
- Other elements of collation
- schem.; tab.; wykr.; Bibliografia (na s.) - 15-17; Bibliografia (liczba pozycji) - 48; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 15, Iss. 2, Spec. Iss.
- Substantive notes
- Special Issue: Artificial Intelligence for Cementitious Materials
- Keywords in English
- bacterial concrete, compressive strength, soft computing techniques, support vector regression, M5P, random forest, Random Tree, artificial intelligence
- ASJC Classification
- DOI
- DOI:10.3390/ma15020489 Opening in a new tab
- URL
- https://www.mdpi.com/1996-1944/15/2/489 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 140
- Score source
- journalList
- Score
- Publication indicators
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT38a15f9cbe064f45a86f963f690d49d3/
- URN
urn:pkr-prod:CUT38a15f9cbe064f45a86f963f690d49d3
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.