TY  - JOUR
SN  - 20904479
PB  - Ain Shams University
Y1  - 2022///
VL  - 13
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118766459&doi=10.1016%2fj.asej.2021.09.020&partnerID=40&md5=87657372a1959d8b9e651dc0d9b883e5
A1  - Aslam, F.
A1  - Elkotb, M.A.
A1  - Iqtidar, A.
A1  - Khan, M.A.
A1  - Javed, M.F.
A1  - Usanova, K.I.
A1  - Khan, M.I.
A1  - Alamri, S.
A1  - Musarat, M.A.
JF  - Ain Shams Engineering Journal
AV  - none
KW  - Compressive strength; Forecasting; Genetic algorithms; Genetic programming; Machine learning
KW  -  Genetic Expression Programming; Machine-learning; Multi-physics; Multiphysics model; Programming models; Programming technique; Regression modelling; Rice husk; Rice-husk ash; Strength prediction
KW  -  Regression analysis
TI  - Compressive strength prediction of rice husk ash using multiphysics genetic expression programming
ID  - scholars16812
IS  - 3
N2  - Rice husk ash (RHA) is obtained by burning rice husks. An advanced programming technique known as genetic expression programming (GEP) is used in this research for developing an empirical multiphysics model for predicting the compressive strength of RHA incorporated concrete. A vast database comprising of 250 data points is obtained from the extensive and consistent literature review. Different parameters such as age, RHA content, cement content, water content, amount of superplasticizer and aggregate content are used as inputs. A closed-form equation solution was obtained to predict the compressive strength of RHA based on input parameters. The performance of GEP is evaluated by comparing it with regression models. Statistical parameter R2 is used to assess the results predicted by GEP and regression models. Statistical and parametric analysis is also carried out to determine the influence of inputs on the outcome. The GEP model performed better in all terms as compared to other models. © 2021 THE AUTHORS
N1  - cited By 31
ER  -