Green optimization of vapour-cured geopolymer mortars: Predicting cost, mechanical properties, and environmental impact with artificial neural networks

In this study, prediction and optimization of Na concentration, Ms modulus (total ratio of SiO₂/Na₂O in alkali activators), fly ash (FA) and granulated blast furnace slag (GGBFS) contents of geopolymer mortars produced by vapour curing in a prefabricated element production plant were carried out focusing on compressive strength, cost and environmental impacts. After the compressive strengths of geopolymer mortars made with comprehensive mixture designs were obtained experimentally, an artificial neural network (ANN) model was developed to estimate the compressive strength from Na, Ms, FA and GGBFS input variables. Moreover, primary energy (PE), global warming potential (GWP, equivalent-CO₂ emission) and cost were optimized separately in different compressive strength ranges with ANN predictions. The efficiency optimizations were also performed to evaluate the cost and environmental impacts per unit compressive strength. As a result of the optimizations, the maximum compressive strength of geopolymer mortar coded with 0.41 FA/0.59 GGBFS (Na = 6.75, Ms = 0.83) was determined as 54.42 MPa. The most efficient geopolymer mortar mixtures within the optimization ranges were 0.29 FA/0.71 GGBFS (Na = 3, Ms = 0.25, 27.79 MPa) regarding GWP with 1.82 (kgCO₂/m³)/MPa, 0.34 FA/0.66 GGBFS (Na = 3, Ms = 0.55, 31.49 MPa) regarding PE with 27 (MJ/m³)/MPa, and 0.38 FA/0.62 GGBFS (Na = 6.56, Ms = 0.66, 52.50 MPa) considering cost efficiency with 2.29 (€/m³)/MPa. FA + GGBFS based geopolymer mortars stand out as the most sustainable options in terms of energy efficiency and carbon footprint with lower PE/MPa and GWP/MPa values compared to 100 %FA and 100 %GGBFS based geopolymer mortars. Optimized geopolymer mortars are 78 %, 35 %, and 42 % more efficient than traditional OPC based mortars in terms of CO₂ emissions, energy consumption, and cost, respectively. • Strength, cost, energy consumption, and carbon emissions (GWP) were optimized. • Cost, energy, and environmental impacts per unit strength were evaluated. • GGBFS + FA mortars showed superior performance in energy efficiency and carbon footprint. • Sustainable geopolymer mortar designs were developed for prefabricated production. • Compressive strength of geopolymer mortars was accurately predicted using an ANN model.

Authors

Institutions

Publication Details

Journal
Cleaner Waste Systems
Published
2025-03-25
DOI
https://doi.org/10.1016/j.clwas.2025.100270
Citations
2
Primary Topic
Concrete and Cement Materials Research
Type
article
Field-Weighted Citation Impact
1.91
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Green optimization of vapour-cured geopolymer mortars: Predicting cost, mechanical properties, and environmental impact with artificial neural networks

Bilal Baran, Serhat Çeli̇kten, Mustafa Sarıdemir
2 citations
Cleaner Waste Systems
Concrete and Cement Materials Research
1.91
article

Green optimization of vapour-cured geopolymer mortars: Predicting cost, mechanical properties, and environmental impact with artificial neural networks

Bilal Baran, Serhat Çeli̇kten, Mustafa Sarıdemir
article en
2 citations

Abstract

In this study, prediction and optimization of Na concentration, Ms modulus (total ratio of SiO₂/Na₂O in alkali activators), fly ash (FA) and granulated blast furnace slag (GGBFS) contents of geopolymer mortars produced by vapour curing in a prefabricated element production plant were carried out focusing on compressive strength, cost and environmental impacts. After the compressive strengths of geopolymer mortars made with comprehensive mixture designs were obtained experimentally, an artificial neural network (ANN) model was developed to estimate the compressive strength from Na, Ms, FA and GGBFS input variables. Moreover, primary energy (PE), global warming potential (GWP, equivalent-CO₂ emission) and cost were optimized separately in different compressive strength ranges with ANN predictions. The efficiency optimizations were also performed to evaluate the cost and environmental impacts per unit compressive strength. As a result of the optimizations, the maximum compressive strength of geopolymer mortar coded with 0.41 FA/0.59 GGBFS (Na = 6.75, Ms = 0.83) was determined as 54.42 MPa. The most efficient geopolymer mortar mixtures within the optimization ranges were 0.29 FA/0.71 GGBFS (Na = 3, Ms = 0.25, 27.79 MPa) regarding GWP with 1.82 (kgCO₂/m³)/MPa, 0.34 FA/0.66 GGBFS (Na = 3, Ms = 0.55, 31.49 MPa) regarding PE with 27 (MJ/m³)/MPa, and 0.38 FA/0.62 GGBFS (Na = 6.56, Ms = 0.66, 52.50 MPa) considering cost efficiency with 2.29 (€/m³)/MPa. FA + GGBFS based geopolymer mortars stand out as the most sustainable options in terms of energy efficiency and carbon footprint with lower PE/MPa and GWP/MPa values compared to 100 %FA and 100 %GGBFS based geopolymer mortars. Optimized geopolymer mortars are 78 %, 35 %, and 42 % more efficient than traditional OPC based mortars in terms of CO₂ emissions, energy consumption, and cost, respectively. • Strength, cost, energy consumption, and carbon emissions (GWP) were optimized. • Cost, energy, and environmental impacts per unit strength were evaluated. • GGBFS + FA mortars showed superior performance in energy efficiency and carbon footprint. • Sustainable geopolymer mortar designs were developed for prefabricated production. • Compressive strength of geopolymer mortars was accurately predicted using an ANN model.

Cleaner Waste SystemsVol. 11
Nevşehir Hacı Bektaş Veli University (TR), Niğde Ömer Halisdemir Üniversitesi (TR)
Life in Land
Openalex Percentile: Top 17%
Concrete and Cement Materials Research
1.91
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.