Ahmadi M, Ebadi-Jamkhaneh M, Dalvand A et al. (2024) Hybrid bioinspired
metaheuristic approach for design compressive strength of
high-strength concrete-filled high-strength steel tube columns.
Neural Computing and Applications 36(14): 7953–7969, 10.1007/
s00521-024-09494-4.
Ahmed HU, Abdalla AA, Mohammed AS et al. (2022) Statistical methods
for modeling the compressive strength of geopolymer mortar.
Materials 15(5): 1868, 10.3390/ma15051868.
Akiba T, Sano S, Yanase T, Ohta T and Koyama M (2019). Optuna: a
next-generation hyperparameter optimization framework. In
Proceedings of the 25th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining.
Aladsani MA et al. (2022) Explainable machine learning model for
predicting drift capacity of reinforced concrete walls. ACI Structural
Journal 119(3): 191–204.
Amer Salih M, Farzadnia N, Demirboga R et al. (2022) Effect of elevated
temperatures on mechanical and microstructural properties of alkaliactivated
mortar made up of POFA and GGBS. Construction and
Building Materials 328: 127041, 10.1016/j.conbuildmat.2022.
127041.
Ameri F, Zareei SA and Behforouz B (2020) Zero-cement vs.
cementitious mortars: an experimental comparative study on
engineering and environmental properties. Journal of Building
Engineering 32: 101620, 10.1016/j.jobe.2020.101620.
Arbi K, Nedeljkovic´ M, Zuo Y et al. (2016) A review on the durability of
alkali-activated fly ash/slag systems: advances, issues, and
perspectives. Industrial & Engineering Chemistry Research 55(19):
5439–5453.
Assi L, Carter K, Deaver E et al. (2018) Sustainable concrete: building a
greener future. Journal of Cleaner Production 198: 1641–1651, 10.1016/
j.jclepro.2018.07.123.
ASTM (2002) ASTM C348: Standard test method for flexural strength of
hydraulic-cement mortars. ASTM International, West
Conshohocken, PA, USA.
ASTM (2013) ASTM C1437: Standard test method for flow of hydraulic
cement mortar. ASTM International, West Conshohocken, PA, USA.
ASTM (2020) ASTM C109: Standard test method for compressive
strength of hydraulic cement mortars (using 2-in. or [50 mm] cube
specimens). ASTM International, West Conshohocken, PA, USA.
ASTM (2021) ASTM C778: Standard specification for standard sand.
ASTM International, West Conshohocken, PA, USA.
ASTM (2023) ASTM C618: Standard specification for coal fly ash and
raw or calcined natural pozzolan for use in concrete. ASTM
International, West Conshohocken, PA, USA.
Aydın S and Baradan B (2014) Effect of activator type and content on
properties of alkali-activated slag mortars. Composites Part B:
Engineering 57: 166–172, 10.1016/j.compositesb.2013.10.001.
Barati AA, Zhoolideh M, Azadi H et al. (2023) Interactions of land-use
cover and climate change at global level: how to mitigate the
environmental risks and warming effects. Ecological Indicators 146:
109829.
Blank J and Deb K (2020) Pymoo: multi-objective optimization in
python. IEEE Access 8: 89497–89509.
Breiman L (2001) Random forests. Machine Learning 45(1): 5–32.
Breiman L, Friedman J, Olshen R and Stone C (1984) Classification and
Regression Trees. CRC Press, Boca Raton, FL, USA.
Burges CJC (1998) A tutorial on support vector machines for pattern
recognition. Data Mining and Knowledge Discovery 2(2): 121–167,
10.1023/A:1009715923555.
Cakiroglu C, Shahjalal M, Islam K et al. (2023) Explainable ensemble
learning data-driven modeling of mechanical properties of fiberreinforced
rubberized recycled aggregate concrete. Journal of
Building Engineering 76: 107279, 10.1016/J.JOBE.2023.107279.
C¸ elikten S, Bayer O¨ ztu¨ rk Z and Atabey I˙I˙ (2024) High-temperature
resistance of ceramic sanitaryware waste and fly ash-based
geopolymer and hybrid geopolymer mortars produced at ambient
curing conditions. Construction and Building Materials 446:
137990, 10.1016/j.conbuildmat.2024.137990.
Chang CC and Lin CJ (2011) LIBSVM: a library for support vector
machines. ACM Transactions on Intelligent Systems and Technology
2(3): 1–27, 10.1145/1961189.1961199.
Chi M and Huang R (2013) Binding mechanism and properties of alkaliactivated
fly ash/slag mortars. Construction and Building Materials
40: 291–298, 10.1016/j.conbuildmat.2012.11.003.
Chung SS and Poon CS (1994) Hong Kong citizens’ attitude towards
waste recycling and waste minimization measures. Resources,
Conservation and Recycling 10(4): 377–400, 10.1016/0921-3449(94)
90024-8.
Cortes C and Vapnik V (1995) Support-vector networks. Machine
Learning 20(3): 273–297, 10.1109/64.163674.
Deb PS, Nath P and Sarker PK (2014) The effects of ground granulated
blast-furnace slag blending with fly ash and activator content on the
workability and strength properties of geopolymer concrete cured at
ambient temperature. Materials & Design 62: 32–39, 10.1016/j.
matdes.2014.05.001.
Dehghani A, Aslani F and Ghaebi Panah N (2021) Effects of initial SiO2/
Al2O3 molar ratio and slag on fly ash-based ambient cured
geopolymer properties. Construction and Building Materials 293:
123527, 10.1016/j.conbuildmat.2021.123527.
Dimas D, Giannopoulou I and Panias D (2009) Polymerization in sodium
silicate solutions: a fundamental process in geopolymerization
technology. Journal of Materials Science 44(14): 3719–3730, 10.1007/
s10853-009-3497-5.
Ding Y, Wei W, Wang J et al. (2023) Prediction of compressive strength
and feature importance analysis of solid waste alkali-activated
cementitious materials based on machine learning. Construction and
Building Materials 407: 133545, 10.1016/j.conbuildmat.2023.
133545.
Drucker H, Burges CJ, Kaufman L, Smola A and Vapnik V (1996).
Support vector regression machines. In Advances in Neural
Information Processing Systems 9 (NIPS 1996), pp. 155–161.
Ellis K, Silvestrini R, Varela B et al. (2016) Modeling setting time and
compressive strength in sodium carbonate activated blast furnace
slag mortars using statistical mixture design. Cement & Concrete
Composites 74: 1–6, 10.1016/j.cemconcomp.2016.08.008.
Fang S, Lam ESS, Li B et al. (2020) Effect of alkali contents, moduli and
curing time on engineering properties of alkali activated slag.
Construction and Building Materials 249: 118799, 10.1016/j.
conbuildmat.2020.118799.
Fawagreh K, Gaber MM and Elyan E (2014) Random forests: from early
developments to recent advancements. Systems Science & Control
Engineering 2(1): 602–609.
Freund Y and Mason L (1999) The alternating decision tree learning
algorithm. In ICML ‘99: Proceedings of the 16th International
Conference on Machine Learning, pp. 124–133.
Friedman JH (2002) Stochastic gradient boosting. Computational
Statistics & Data Analysis 38(4): 367–378, 10.1016/S0167-9473(01)
00065-2.
Ge X, Liu Y, Mao Y et al. (2023) Characteristics of fly ash-based
geopolymer concrete in the field for 4 years. Construction and
Building Materials 382: 131222, 10.1016/j.conbuildmat.2023.
131222.
Geurts P, Ernst D and Wehenkel L (2006) Extremely randomized trees.
Machine Learning 63(1): 3–42.
Ghanbari S, Shahmansouri AA, Akbarzadeh Bengar H et al. (2023)
Compressive strength prediction of high-strength oil palm shell
lightweight aggregate concrete using machine learning methods.
Environmental Science and Pollution Research International 30(1):
1096–1115, 10.1007/s11356-022-21987-0.
Gnanadurai LT, Renganathan NT and Selvaraj C (2021) Synthesis and
characterization of synthetic sand by geopolymerization of industrial
wastes (fly ash and GGBS) replacing the natural river sand.
Environmental Science and Pollution Research International 28(40):
56294–56304, 10.1007/s11356-021-14223-8.
Grimm NB, Chapin FS III, Bierwagen B et al. (2013) The impacts of
climate change on ecosystem structure and function. Frontiers in
Ecology and the Environment 11(9): 474–482, 10.1890/120282.
Hammad N, Elnemr A and Shaaban IG (2023) State-of-the-art report: the
self-healing capability of alkali-activated slag (AAS) concrete.
Materials 16(12): 4394, 10.3390/ma16124394.
Hammad N, El-Nemr A and Shaaban I (2024a) Enhancing durability in
bacteria-based AAS composites at varied alkali environments.
Progress in Engineering Science 2(1): 100047, 10.1016/j.pes.2024.
100047.
Hammad N, El-Nemr A and Shaaban IG (2024b) The efficiency of
calcium oxide on microbial self-healing activity in alkali-activated
slag (AAS). Applied Sciences 14(12): 5299, 10.3390/app14125299.
Hassan MA, Khalil A, Kaseb S et al. (2017) Exploring the potential of
tree-based ensemble methods in solar radiation modeling. Applied
Energy 203: 897–916, 10.1016/j.apenergy.2017.06.104.
HF (Hugging Face) (2025) See https://huggingface.co/spaces/
MohamedRabie26/Alkali-Activated_Mortar/tree/main (accessed
14/07/2025).
Iqbal HW, Hamcumpai K, Nuaklong P et al. (2023) Effect of graphene
nanoplatelets on engineering properties of fly ash-based geopolymer
concrete containing crumb rubber and its optimization using
response surface methodology. Journal of Building Engineering 75:
107024, 10.1016/j.jobe.2023.107024.
Irshidat MR, Al-Nuaimi N and Rabie M (2021a) Potential utilization of
municipal solid waste incineration ashes as sand replacement for
developing sustainable cementitious binder. Construction and
Building Materials 312: 125488, 10.1016/j.conbuildmat.2021.
125488.
Irshidat MR, Al-Nuaimi N and Rabie M (2021b) Sustainable utilization of
waste carbon black in alkali-activated mortar production. Case
Studies in Construction Materials 15: e00743, 10.1016/j.cscm.2021.
e00743.
Irshidat MR, Al-Nuaimi N and Rabie M (2022a) Sustainable alkaliactivated
binders with municipal solid waste incineration ashes as
sand or fly ash replacement. Journal of Material Cycles and Waste
Management 24(3): 992–1008, 10.1007/s10163-022-01374-0.
Irshidat MR, Al-Nuaimi N and Rabie M (2022b) Thermal behavior and
post-heating fracture characteristics of polypropylene microfiberreinforced
geopolymer binders. Construction and Building Materials
332: 127310, 10.1016/j.conbuildmat.2022.127310.
Jafari A, Ma L, Shahmansouri AA et al. (2023) Quantitative fractography
for brittle fracture via multilayer perceptron neural network.
Engineering Fracture Mechanics 291: 109545, 10.1016/j.
engfracmech.2023.109545.
JaiSai T (2022) Compressive strength optimization of geopolymer mortar
made from alkaline liquid comprising acidic water. Materials Today:
Proceedings 59: 179–187, 10.1016/j.matpr.2021.10.395.
Jalal M and Jalal H (2020) Behavior assessment, regression analysis and
support vector machine (SVM) modeling of waste tire rubberized
concrete. Journal of Cleaner Production 273: 122960, 10.1016/j.
jclepro.2020.122960.
Kizhakkum Paramban R and Varatharajapuram Govindarajulu K (2024)
Characteristic study of geopolymer fly ash fine aggregate and its
influence on partial replacement of M-sand in the strength properties
of mortar. Structures 68: 107141, 10.1016/j.istruc.2024.107141.
Kohani Khoshkbijari R, Farahmandfar A, Zaati Zehni N et al. (2024)
Properties of ground granulated blast-furnace slag-based geopolymer
mortars containing glass powder, feldspar, and metakaolin under
different curing conditions. Construction and Building Materials
435: 136753, 10.1016/j.conbuildmat.2024.136753.
Kontoni D-PN and Ahmadi M (2024) Practical prediction of ultimate
axial strain and peak axial stress of FRP-confined concrete using
hybrid ANFIS-PSO models. In Artificial Intelligence Applications
for Sustainable Construction (Nehdi ML, Kumar K, Kumar A,
Arora HC and Damasˇevicˇius R (eds)). Woodhead Publishing,
Cambridge, UK, pp. 225–255, 10.1016/B978-0-443-13191-2.
00015-8.
Kurt Z, Yilmaz Y, Cakmak T et al. (2023) A novel framework for
strength prediction of geopolymer mortar: renovative precursor
effect. Journal of Building Engineering 76: 107041, 10.1016/j.jobe.
2023.107041.
Marathe S and Rodrigues AP (2024) Intelligent models for prediction of
compressive strength of geopolymer pervious concrete hybridized
with agro-industrial and construction-demolition wastes. Studia
Geotechnica et Mechanica 46(s1): 349–376, 10.2478/sgem-2024-
0020.
Matsimbe J, Dinka M, Olukanni D et al. (2024) Fundamental machine
learning algorithms and statistical models applied in strength
prediction of geopolymers: a systematic review. Discover Applied
Sciences 6(10): 538, 10.1007/s42452-024-06244-y.
Nadarajah A, Mohd Nasir NA, Abu Bakar N et al. (2024) Fly ash-GGBS
blended geopolymer mortar for early engineering characteristic at
ambient temperature. Ain Shams Engineering Journal 15(7):
102821, 10.1016/j.asej.2024.102821.
Narimani Zamanabadi S, Zareei SA, Shoaei P et al. (2019) Ambient-cured
alkali-activated slag paste incorporating micro-silica as repair
material: effects of alkali activator solution on physical and
mechanical properties. Construction and Building Materials 229:
116911, 10.1016/j.conbuildmat.2019.116911.
Nasr D, Pakshir AH and Ghayour H (2018) The influence of curing
conditions and alkaline activator concentration on elevated
temperature behavior of alkali activated slag (AAS) mortars.
Construction and Building Materials 190: 108–119, 10.1016/j.
conbuildmat.2018.09.099.
Nath P and Sarker PK (2014) Effect of GGBFS on setting, workability
and early strength properties of fly ash geopolymer concrete cured
in ambient condition. Construction and Building Materials 66:
163–171, 10.1016/j.conbuildmat.2014.05.080.
Nguyen KT, Nguyen QD, Le TA et al. (2020) Analyzing the compressive
strength of green fly ash based geopolymer concrete using
experiment and machine learning approaches. Construction and
Building Materials 247: 118581, 10.1016/j.conbuildmat.2020.
118581.
Nguyen MH, Mai H-VT, Trinh SH et al. (2023) A comparative assessment
of tree-based predictive models to estimate geopolymer concrete
compressive strength. Neural Computing and Applications 35(9):
6569–6588, 10.1007/s00521-022-08042-2.
Nofalah M-H, Ghadir P, Hasanzadehshooiili H et al. (2023) Effects of
binder proportion and curing condition on the mechanical
characteristics of volcanic ash- and slag-based geopolymer mortars;
machine learning integrated experimental study. Construction and
Building Materials 395: 132330, 10.1016/j.conbuildmat.2023.
132330.
Nurruddin MF, Sani H, Mohammed BS and Shaaban I (2018) Methods of
curing geopolymer concrete: a review. International Journal of
Advanced and Applied Sciences 5(1): 31–36, 10.21833/ijaas.2018.
01.005.
Osman A and Irshidat MR (2023) Design of sustainable ambient-cured
geopolymer composites for repair application of Rc structures. See
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4490477
(accessed 14/07/2025).
Pacheco-Torgal F, Moura D, Ding Y et al. (2011) Composition, strength
and workability of alkali-activated metakaolin based mortars.
Construction and Building Materials 25(9): 3732–3745, 10.1016/j.
conbuildmat.2011.04.017.
Pal M and Deswal S (2011) Support vector regression based shear
strength modelling of deep beams. Computers & Structures
89(13–14): 1430–1439, 10.1016/j.compstruc.2011.03.005.
Prakasam G, Murthy AR and Saffiq Reheman M (2020) Mechanical,
durability and fracture properties of nano-modified FA/GGBS
geopolymer mortar. Magazine of Concrete Research 72(4): 207–216,
10.1680/jmacr.18.00059.
Prem PR, Thirumalaiselvi A and Verma M (2019) Applied linear and
nonlinear statistical models for evaluating strength of geopolymer
concrete. Computers and Concrete 24(1): 7–17.
Rabie M and Shaaban IG (2025) Glass fibre concrete: experimental
investigation and predictive modeling using advanced machine
learning with an interactive online interface. Construction and
Building Materials 472: 140951, 10.1016/j.conbuildmat.2025.
140951.
Rabie M, Irshidat MR and Al Nuaimi N (2020) Effect of alkaline
activators on the mechanical properties of geopolymer mortar. In
Proceedings of the International Conference on Civil
Infrastructure and Construction (CIC), pp. 377–381, 10.29117/cic.
2020.0048.
Rabie M, Irshidat MR and Al-Nuaimi N (2022) Ambient and heatcured
geopolymer composites: mix design optimization and life
cycle assessment. Sustainability 14(9): 4942, 10.3390/
su14094942.
Salas DA, Ramirez AD, Ulloa N et al. (2018) Life cycle assessment of
geopolymer concrete. Construction and Building Materials 190:
170–177, 10.1016/j.conbuildmat.2018.09.123.
Shahmansouri AA, Bengar HA and Ghanbari S (2020a) Experimental
investigation and predictive modeling of compressive strength of
pozzolanic geopolymer concrete using gene expression
programming. See 10.31224/osf.io/gx6fw 10.31224/osf.io/gx6fw
(accessed 14/07/2025).
Shahmansouri AA, Akbarzadeh Bengar H and Ghanbari S (2020b)
Compressive strength prediction of eco-efficient GGBS-based
geopolymer concrete using GEP method. Journal of Building
Engineering 31: 101326, 10.1016/j.jobe.2020.101326.
Shahmansouri AA, Nematzadeh M and Behnood A (2021a) Mechanical
properties of GGBFS-based geopolymer concrete incorporating
natural zeolite and silica fume with an optimum design using
response surface method. Journal of Building Engineering 36:
102138, 10.1016/j.jobe.2020.102138.
Shahmansouri AA, Yazdani M, Ghanbari S et al. (2021b) Artificial neural
network model to predict the compressive strength of eco-friendly
geopolymer concrete incorporating silica fume and natural zeolite.
Journal of Cleaner Production 279: 123697, 10.1016/j.jclepro.2020.
123697.
Shang J, Dai J-G, Zhao T-J et al. (2018) Alternation of traditional cement
mortars using fly ash-based geopolymer mortars modified by slag.
Journal of Cleaner Production 203: 746–756, 10.1016/j.jclepro.
2018.08.255.
Sharma U, Gupta N and Verma M (2023) Prediction of compressive
strength of GGBFS and flyash-based geopolymer composite by
linear regression, lasso regression, and ridge regression. Asian
Journal of Civil Engineering 24(8): 3399–3411, 10.1007/s42107-
023-00721-2.
Soutsos M, Boyle AP, Vinai R et al. (2016) Factors influencing the
compressive strength of fly ash based geopolymers. Construction
and Building Materials 110: 355–368, 10.1016/j.conbuildmat.2015.
11.045.
Sun Y, Cheng H, Zhang S et al. (2023) Prediction & optimization of
alkali-activated concrete based on the random forest machine
learning algorithm. Construction and Building Materials 385:
131519, 10.1016/j.conbuildmat.2023.131519.
Torres BM, Vo¨ lker C and Firdous R (2023) Concreting a sustainable
future: a dataset of alkali-activated concrete and its properties. Data
in Brief 50: 109525, 10.1016/j.dib.2023.109525.
Tran V, Ahmed M and Gohery S (2023) Prediction of the ultimate axial
load of circular concrete-filled stainless steel tubular columns using
machine learning approaches. Structural Concrete 24(3):
3908–3932, 10.1002/suco.202200877.
Tsai CJ (2024) Investigate the mechanical behavior of alkali-activated
slag mortar using sustainable waste sodium silicate-bonded sand as
an alkali activator and carbon free aggregate. Construction and
Building Materials 429: 136448, 10.1016/j.conbuildmat.2024.
136448.
Verma M (2023) Prediction of compressive strength of geopolymer
concrete using random forest machine and deep learning. Asian
Journal of Civil Engineering 24(7): 2659–2668, 10.1007/s42107-
023-00670-w.
Wakjira TG, Kutty AA and Alam MS (2024) A novel framework for
developing environmentally sustainable and cost-effective ultrahigh-
performance concrete (UHPC) using advanced machine
learning and multi-objective optimization techniques. Construction
and Building Materials 416: 135114, 10.1016/j.conbuildmat.2024.
135114.
Wang J, Xu L, Li M et al. (2023) Investigations on factors influencing
physical properties of recycled cement and the related carbon
emissions and energy consumptions. Journal of Cleaner Production
414: 137715, 10.1016/j.jclepro.2023.137715.
Wang S, Chen K, Liu J et al. (2024a) Multi-performance optimization of
low-carbon geopolymer considering mechanical, cost, and CO2
emission based on experiment and interpretable learning.
Construction and Building Materials 425: 136013, 10.1016/j.
conbuildmat.2024.136013.
Wang T, Fan X and Gao C (2024b) Development of high-strength
geopolymer mortar based on fly ash-slag: Correlational analysis of
microstructural and mechanical properties and environmental
assessment. Construction and Building Materials 441: 137515, 10.1016/
j.conbuildmat.2024.137515.
Wang T, Fan X and Gao C (2024c) Strength, pore characteristics, and
characterization of fly ash-slag-based geopolymer mortar modified
with silica fume. Structures 69: 107525, 10.1016/j.istruc.2024.
107525.
Wardhono A, Gunasekara C, Law DW et al. (2017) Comparison of long
term performance between alkali activated slag and fly ash
geopolymer concretes. Construction and Building Materials 143:
272–279, 10.1016/j.conbuildmat.2017.03.153.
Waskom M (2021) Seaborn: statistical data visualization. Journal of
Open Source Software 6(60): 3021, 10.21105/JOSS.03021.
Way DA, Cook A and Rogers A (2021) The effects of rising CO2
concentrations on terrestrial systems: scaling it up. The New
Phytologist 229(5): 2383–2385, 10.1111/nph.17096.
Wu H, He M, Wu S et al. (2024) Effects of binder component and curing
regime on compressive strength, capillary water absorption,
shrinkage and pore structure of geopolymer mortars. Construction
and Building Materials 442: 137707, 10.1016/j.conbuildmat.2024.
137707.
Xiang J, He Y, Liu L et al. (2020) Exothermic behavior and drying
shrinkage of alkali-activated slag concrete by low temperaturepreparation
method. Construction and Building Materials 262:
120056, 10.1016/j.conbuildmat.2020.120056.
Xie J, Wang J, Rao R et al. (2019) Effects of combined usage of GGBS
and fly ash on workability and mechanical properties of alkali
activated geopolymer concrete with recycled aggregate. Composites
Part B: Engineering 164: 179–190, 10.1016/j.compositesb.2018.11.
067.
Xie J, Li J, Zhang B et al. (2024) Effects of pretreated recycled fine
aggregates on the mechanical properties and microstructure of
alkali-activated mortar. Case Studies in Construction Materials 20:
e02819, 10.1016/j.cscm.2023.e02819.
Yilmazoglu A, Yildirim ST, Behc¸et O¨ F et al. (2022) Performance
evaluation of fly ash and ground granulated blast furnace slag-based
geopolymer concrete: a comparative study. Structural Concrete
23(6): 3898–3915, 10.1002/suco.202100835.
Yuan X-H, Chen W, Lu Z-A et al. (2014) Shrinkage compensation of
alkali-activated slag concrete and microstructural analysis.
Construction and Building Materials 66: 422–428, 10.1016/j.
conbuildmat.2014.05.085.
Yusuf MO, Megat Johari MA, Ahmad ZA et al. (2014) Evolution of
alkaline activated ground blast furnace slag–ultrafine palm oil fuel
ash based concrete. Materials & Design 55: 387–393, 10.1016/j.
matdes.2013.09.047.
Zhao J, Tong L, Li B et al. (2021) Eco-friendly geopolymer materials: a
review of performance improvement, potential application and
sustainability assessment. Journal of Cleaner Production 307:
127085, 10.1016/j.jclepro.2021.127085.