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

Abstract

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.

Prediction of the Compressive Strength of Waste-Based Concretes Using Artificial Neural Network

“…It has been demonstrated that ANNs can predict the compressive and tensile strength of concretes containing construction and agricultural wastes [ 32 , 48 ], blast furnace slag [ 35 ], and alkali-activated mortars [ 34 , 37 ]. Some authors combined an ANN with other techniques, such as a genetic algorithm (GA) [ 35 ], statistics and holistic models [ 44 ], the cuckoo search method [ 49 , 50 ], ANFIS models [ 24 , 51 ], fuzzy logic models [ 52 , 53 ], and the Monte Carlo approach [ 54 ], to optimize the prediction results. Jiang et al [ 53 ] and Farooq et al [ 55 ] studied the prediction of mechanical properties of self-compacting concretes and high-performance concretes using an ANN on over 1030 datasets.…”

Effects of Sulfate and Sulfuric Acid on Efficiency of Geopolymers as Concrete Repair Materials

“…Geopolymer mortars (GPMs) as cement-free materials are useful for their high strength, excellent performance, and environmental friendliness. Most of the starting source materials abundant in calcium (CaO), aluminum (Al 2 O 3 ), and silicon (SiO 2 ) with alkali activation [ 1 , 2 , 3 , 4 ] are generally used to make these pastes, mortars, and concretes. Initial materials include palm oil fuel ash (POFA), fly ash (FA), granulated blast furnace slag (GBFS), etc.…”

Experimental and Informational Modeling Study of Sustainable Self-Compacting Geopolymer Concrete

“…The use of Portland cement (PC) as the primary binder in SCC remains a major concern because of the elevated levels of carbon dioxide (CO 2 ) emissions and embodied energy (EE) associated with its production. Cement production constitutes approximately 7% of the global CO 2 emissions [5][6][7]. If the cement industries were considered as a country, it would indeed rank third after China and the USA in terms of total CO 2 emissions.…”