Gene Expression Programming (GEP) Modelling of Sustainable Building Materials including Mineral Admixtures for Novel Solutions

• Published in: Mining (Basel) Vol. 2; no. 4; pp. 629 - 653
• Main Authors: Kontoni, Denise-Penelope N., Onyelowe, Kennedy C., Ebid, Ahmed M., Jahangir, Hashem, Rezazadeh Eidgahee, Danial, Soleymani, Atefeh, Ikpa, Chidozie
• Format: Journal Article
• Language: English
• Published: Rouyn-Noranda MDPI AG 01.12.2022
• Subjects: Chromosomes; Civil engineering; concrete; construction materials; Drilling & boring machinery; Fuzzy logic; Gene expression; gene expression programming (GEP); Genetic algorithms; Genetic engineering; Genotype & phenotype; green admixtures; Green buildings; mineral admixtures; Neural networks; soil; Sustainable materials
• ISSN: 2673-6489; 2673-6489
• DOI: 10.3390/mining2040034

In this study, the employment of the gene expression programming (GEP) technique in forecasting models on sustainable construction materials including mineral admixtures and civil engineering quantities (e.g., compressive strength), was investigated. Compared to the artificial neural networks (ANN) based formulations, which are often too complicated to be used, GEP-based derived models provide estimation equations that are reasonably simple and may be used for practical design purposes and even for hand calculations. Many popular models, such as best-fitted curves based on regression analyses, multi-linear regression (MLR), multinomial logistic regression (MNLR), and multinomial variate regression (MNVR), can also be used for construction materials properties modeling. However, due to the nonlinearity and complexity of the target properties, the models established using linear regression analyses may not reveal the precise behavior. Additionally, regression models lack generality, and this comes from the fact that some functions are defined for regression in classical regression techniques; while in the GEP approach, there is no predefined function to be considered, and it reproduces or omits various combinations of parameters to provide the formulation that fits the experimental outcomes. If the input parameters can be evaluated through simple laboratory or rapid measurements, and also a comprehensive experimental database is made available, the models can be constructed with optimal flexibility. Flexibility in choosing the complexity and fitness functions, such as RMSE, MAE, and MSE, might lead to better performance of the approach and well-capturing the governing pattern behind the material’s characteristics. There may be minor inaccuracies with this technique; however, the explicit mathematical expressions, which can be easily implemented in the design and analysis process, may cover the minor inaccuracies compared to ANN, support vector machine (SVM), and other intelligent approaches. Based on the presented study, sometimes it would be better to provide more than one GEP model and consider different combinations of input contributing variables to afford the possible initial feed for a more settled and comprehensive model. Mostly, GEP’s strengths as a superior machine learning technique in modeling the behavior of construction materials including mineral admixtures, leading to innovative solutions in civil engineering, have been presented.