Eco-friendly concrete incorporating palm oil fuel ash: Fresh and mechanical properties with machine learning prediction, and sustainability assessment

Noor Md Sadiqul Hasan; Md Habibur Rahman Sobuz; Nur Mohammad Nazmus Shaurdho; Md Montaseer Meraz; Shuvo Dip Datta; Fahim Shahriyar Aditto; Md Kawsarul Islam Kabbo; Md Jihad Miah

doi:10.1016/j.heliyon.2023.e22296

Eco-friendly concrete incorporating palm oil fuel ash: Fresh and mechanical properties with machine learning prediction, and sustainability assessment

Heliyon. 2023 Nov 15;9(11):e22296. doi: 10.1016/j.heliyon.2023.e22296. eCollection 2023 Nov.

Authors

Noor Md Sadiqul Hasan¹, Md Habibur Rahman Sobuz², Nur Mohammad Nazmus Shaurdho¹, Md Montaseer Meraz², Shuvo Dip Datta², Fahim Shahriyar Aditto², Md Kawsarul Islam Kabbo², Md Jihad Miah³

Affiliations

¹ Department of Civil Engineering, College of Engineering and Technology, International University of Business Agriculture and Technology, Dhaka 1230, Bangladesh.
² Department of Building Engineering and Construction Management, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh.
³ Department of Civil Engineering, University of Asia Pacific, Dhaka 1205, Bangladesh.

Abstract

Rising natural resource consumption leads to increased hazardous gas emissions, necessitating the concrete industry's focus on sustainable alternatives like palm oil fuel ash (POFA) to replace cement. Also, advanced machine learning (ML) techniques can uncover previously unreported insights about the effects of POFA that may be missing from the literature. Hence, this study investigates the influence of varying concentrations of POFA on fresh and mechanical characteristics with quantifying ML approaches and microstructural performance, as well as the environmental impact of structural concrete. For this, cement substitutions of 5 %, 15 %, 25 %, 35 %, and 45 % (by weight of cement) were utilized. POFA enhanced the overall concrete workability, with slump increments ranging from approximately 9 %-55 % and compacting factor increments of 4 %-12 %. Mechanical performance of POFA concrete improved up to 25 % replacement levels, with the highest enhancements observed in compressive (4.5 %), splitting tensile (36 %), and flexural (31 %) strength, for the mix containing 15 % POFA. The finer particle size of POFA improved microstructural performance by reducing porosity, aligning with the enhanced mechanical strength. The environmental impact of POFA was assessed by measuring eCO₂ emissions, revealing a potential reduction of up to 44 %. Incorporating 5 %-15 % POFA yielded ideal mechanical performance results, significantly enhancing sustainability and cost-effectiveness. Regarding the ML approach, it can be observed that a low regression coefficient (R²) contrasts sharply with the higher R² values for the random forest (RF) and the ensemble model, indicating satisfactory precision prediction with experimental results.

Keywords: Concrete; Cost and eCO2 emissions; Eco-friendly; Fresh characteristics; Machine learning techniques; Mechanical performance; Palm oil fuel ash; Sustainability.