Life-Cycle Assessment of Alkali-Activated Materials Incorporating Industrial Byproducts

• Published in: Materials Vol. 14; no. 9; p. 2401
• Main Authors: Faridmehr, Iman, Nehdi, Moncef L., Nikoo, Mehdi, Huseien, Ghasan Fahim, Ozbakkaloglu, Togay
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 05-05-2021; MDPI
• Subjects: Algorithms; Artificial neural networks; By products; Byproducts; Carbon dioxide; Caustic soda; Cement; Coal-fired power plants; Construction industry; Energy consumption; Environmental impact; Industrial plant emissions; Industrial wastes; Life cycle analysis; Life cycle assessment; Manufacturing; Mechanical properties; Mortars (material); Optimization; Particle size; Portland cements; Raw materials; Recycling; Sodium; Vegetable oils; Los Angeles California; Malaysia; Singapore
• ISSN: 1996-1944; 1996-1944
• DOI: 10.3390/ma14092401

Eco-friendly and sustainable materials that are cost-effective, while having a reduced carbon footprint and energy consumption, are in great demand by the construction industry worldwide. Accordingly, alkali-activated materials (AAM) composed primarily of industrial byproducts have emerged as more desirable alternatives to ordinary Portland cement (OPC)-based concrete. Hence, this study investigates the cradle-to-gate life-cycle assessment (LCA) of ternary blended alkali-activated mortars made with industrial byproducts. Moreover, the embodied energy (EE), which represents an important parameter in cradle-to-gate life-cycle analysis, was investigated for 42 AAM mixtures. The boundary of the cradle-to-gate system was extended to include the mechanical and durability properties of AAMs on the basis of performance criteria. Using the experimental test database thus developed, an optimized artificial neural network (ANN) combined with the cuckoo optimization algorithm (COA) was developed to estimate the CO2 emissions and EE of AAMs. Considering the lack of systematic research on the cradle-to-gate LCA of AAMs in the literature, the results of this research provide new insights into the assessment of the environmental impact of AAM made with industrial byproducts. The final weight and bias values of the AAN model can be used to design AAM mixtures with targeted mechanical properties and CO2 emission considering desired amounts of industrial byproduct utilization in the mixture.