Mapping the strength of agro-ecological lightweight concrete containing oil palm by-product using artificial intelligence techniques

Abstract

The critical challenge for the cement production industry is the high emission of greenhouse gases. For the sustainability in a cycling economy context civil and environmental engineers should reconsider future generations and should redesign and manufacturing in a safe and eco-friendly alternative. Previous experimental studies have shown, that using oil palm by-product in the production of sustainable lightweight structural concrete, (SLSC) presents good life cycle, land-use efficiency and economic benefits. In this work a formula-based model is proposed to evaluate the strength of SLSC, knowledge-aided modeling based on machine learning methods (e. g., for further development, Multivariate Adaptive Regression Splines (MARS), Gene Expression Programming (GEP), M5P Model Tree (M5P) and Extreme Learning Machine (ELM)) were selected. A comprehensive and reliable experimental record comprising of 449 data of SLSC containing oil palm by-product was utilized to formulate strength predictive models. The proposed models were investigated, in terms of assessment criteria, error measurements, and performance evaluation. The efficiency of the MARS model was shown by comparing its performance with GEP, M5P and ELM. As well as Monte-Carlo simulation approach was conducted to verify the proposed models. Evaluation of the relative importance of effective variables presented that the gravel (11.84%), oil palm waste aggregate (11.38%) and water to binder ratio (11.15%) were the most influential features in the strength prediction of the SLSC. Also, a parametric study is developed to provide robustness of the proposed predictive model. Results of the modeling provide a new understanding of the provision of an auto-estimated strength evaluation of SLSC incorporating oil palm by-product with superiority promotion of knowledge-aided modeling mechanism.