@article{scholars19231,
            note = {cited By 13},
            year = {2023},
             doi = {10.1007/s12008-023-01386-6},
         journal = {International Journal on Interactive Design and Manufacturing},
       publisher = {Springer-Verlag Italia s.r.l.},
           title = {Development of performance-based models for green concrete using multiple linear regression and artificial neural network},
        abstract = {The impact of process inputs and critical performance parameters on product quality is an important aspect of production and this is also true for concrete. There has been an increasing emphasis on the use of machine learning algorithms for modelling in order to improve production quality and processes. Multiple linear regression and artificial neural network are used as predictive models in this study to generalise the relationship between seven process variables and three performance parameters in green concrete production. Models were developed by using 103 experimental datasets obtained from the production of green concrete. Indices such as p value, residual predicted plots, R-squared and mean squared error were used to evaluate the models. Due to the masking effect and non-linear nature of the rheologic properties, multiple linear regression was ineffective at predicting the rheologic behaviour of green concrete, as evidenced by low R2 values of 0.323 and 0.506 obtained for slump and flow properties, respectively. However, the model was significant at predicting the compressive strength with an R2 value of 0.898. Conversely, artificial neural network models with varying amount of hidden layer neurons generalized the relationship between the process variables and performance parameters much better. Optimal network architecture of 7-4-1, 7-2-1 and 7-3-1 with corresponding R2 values of 0.918, 0.826 and 0.945 were obtained for slump, flow and compressive strength, respectively. Therefore, in developing performance-based models to produce green concrete the use of ANN is considered a better alternative particularly when there are limited number of process inputs. {\^A}{\copyright} 2023, The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature.},
        keywords = {Compressive strength; Concretes; Learning algorithms; Machine learning; Mean square error; Multilayer neural networks; Network architecture, Critical performance parameters; Green concrete; Machine learning algorithms; Multiple linear regressions; Performance parameters; Performance-based models; Process inputs; Process Variables; Products quality; Rheological property, Multiple linear regression},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85161959532&doi=10.1007\%2fs12008-023-01386-6&partnerID=40&md5=f8f1a6e3eb189fd6bdc1786dcaa56262},
            issn = {19552513},
          author = {Singh, P. and Adebanjo, A. and Shafiq, N. and Razak, S. N. A. and Kumar, V. and Farhan, S. A. and Adebanjo, I. and Singh, A. and Dixit, S. and Singh, S. and Sergeevna, M. T.}
}