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    Comparison of deep LSTM and machine learning models for predicting compressive strength of fly ash/slag-based geopolymer concrete
    (Nature Portfolio, 2025) Kina, Ceren; Tanyildizi, Harun; Al Bakri Abdullah, Mohd Mustafa; Razak, Rafiza Abdul; Imjai, Thanongsak
    In the production of geopolymer concrete (GPC), using ground granulated blast furnace slag (GGBFS) and fly ash (FA) can reduce the carbon dioxide footprint and decrease the amount of waste materials released into the environment. Finding the compressive strength (fc) of GPC through experiments is time-consuming and costly; thus, applying artificial intelligence models can expedite this process. This study aims to compare the performance of deep Long Short-Term Memory (LSTM) and the machine learning (ML)-based algorithms in predicting the fc of FA/GGBFS-based GPC. Artificial neural networks (ANN), Bootstrap aggregating (Bagging), Least-Squares Boosting (LSBoost) and K-Nearest-Neighbours (kNN) were used for ML-based algorithms. For this goal, data were collected from the previous studies in the literature. The selected input characteristic variables included the chemical composition and quantities of FA and GGBFS, fine and coarse aggregates, sodium hydroxide molarity, alkaline activators, superplasticizer dosage, and curing temperature. Based on sensitivity analysis, the most influential parameter in the fc of FA/GGBFS-based GPC was the fine aggregate content. Performance metrics, error percentage distribution, and Taylor diagrams indicate that the highest accuracy was achieved by LSTM, which had an R-squared value of 0.98. This was followed by ANN, LSBoost, Bagging, and kNN. Notably, LSBoost and ANN also demonstrated strong performance, with R-squared values of 0.94 and 0.95, respectively. Also, Bagging showed acceptable ability for fc estimation of FA/GGBFS-based GPC due to having an R-squared value of 0.88, but kNN had very poor performance.
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    Extreme Learning Machine for Estimation of the Engineering Properties of Self-Compacting Mortar with High-Volume Mineral Admixtures
    (Springer Int Publ Ag, 2024) Turk, Kazim; Kina, Ceren; Tanyildizi, Harun
    The utilization of supplementary cementitious materials obtained from industrial by-products or wastes is one of the most effective ways to minimize the costs as well as environmental impact associated with cement production. This work investigated the effects of the replacement of Portland cement (PC) with (25, 30, 35 and 40%) fly ash (FA) and (5, 10, 15, and 20%) silica fume (SF) by weight as binary and ternary blends on the compressive strength (f(c)) and flexural strength (f(ft)) of self-compacting mortars (SCMs) at 28 and 91 curing days. Extreme learning machine (ELM), support vector regression (SVR), artificial neural network (ANN), and decision tree (DT) models were devised to predict these strengths of SCMs containing high-volume mineral admixture (HVMA). The selected input variables were the number of curing days, water-cementitious material (W/CM), PC, FA, SF, and sand contents, while the f(c) and f(ft) were the output variables. ANOVA results show that the curing time was the most effective parameter for determining both strengths. The results also indicated that ELM achieved superior performance for the prediction of f(c) and f(ft) of SCMs with HVMA compared to SVR, ANN, and DT due to having the highest coefficient of determination values of 0.9802 for both strengths.
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    Forecasting the compressive strength of GGBFS-based geopolymer concrete via ensemble predictive models
    (Elsevier Sci Ltd, 2023) Kina, Ceren; Tanyildizi, Harun; Turk, Kazim
    The compressive strength (fc) of the concrete is an important parameter in the structural design. However, the assessment of fc via an experimental program is time-consuming, costly, and needs a labor force. Therefore, the forecasting of fc through different algorithms can accelerate and facilitate this process and also provide guidance for scheduling the progress of the construction. While some studies have explored the use of models for the prediction of fc of concrete, the ensemble models that can predict the fc of GPC with industrial by-products is still lacking. Within this scope, decision tree (DT), Bootstrap aggregating (Bagging), and Least-squares boosting (LSBoost) models were devised to predict fc of ground granulated blast furnace slag (GGBFS)-based geopolymer concrete (GPC). The data points collected to devise a GEP model in the previous study were used and the prediction results of the GEP model were compared with the proposed ensemble models in the current study. The age of the specimen, NaOH solution concentration, natural zeolite (NZ) content, silica fume (SF) content, and GGBFS content were used as input parameters, and fc was used as output parameter. According to ANOVA analysis, the age of the specimen was found as the most influential parameter in the determination of the fc of GGBFS-based GPC. Also, Multiple linear regression equation was proposed to estimate the fc of GGBFS-based GPC with the accuracy of 93%. The most accurate model was introduced through performance metrics and the Taylor diagram. The results proved that the highest accuracy and stable predictions were achieved by the LSBoost model with R-squared value of 98.25% followed by GEP model developed in the previous study, DT and Bagging models. However, it is worth mentioning that due to having a high coefficient of correlation values (>%80), DT and Bagging models also have an acceptable ability for predicting fc of GGBS-based GPC.
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    Hybrid portland cement-slag-based geopolymer mortar: Strength, microstructural and environmental assessment
    (Elsevier, 2025) Kina, Ceren; Tanyildizi, Harun; Acik, Volkan
    The aim of the current work is to investigate the strength, microstructure, environmental and economic effects of hybrid ordinary portland cement (PC) and ground granulated blast-furnace slag (GGBS) based geopolymer mortar as an alternative to ordinary cement mortar. Eleven mixtures were prepared for this. In this regard, PC was blended with GGBS content of 0-90 wt% in these mixtures. The designed mortar samples were cured at ambient temperature (20 +/- 2 degrees C) to be more applicable in the construction industry, unlike most geopolymer productions and ordinary PC mortar samples were also produced to be comparable to the designed hybrid PC/ GGBS-based geopolymer mortars. The compressive strength (fc) development, ultrasonic pulse velocity (UPV), and dynamic modulus of elasticity (Edyn) values of these ten-hybrid PC/GGBS-based geopolymer mortars were compared with the designed ordinary PC mortar. The results indicated that the incorporation of 20 % PC with 80 % GGBS in the alkali-activated system had the best 28-day compressive strength value with 74.26 MPa, which was 91.07 % higher than that of the designed ordinary PC mortar. The techniques of scanning electron microscopy (SEM)-EDS, Fourier transform-infrared spectroscopy (FT-IR), and thermogravimetric analysis (TGA) were used to identify the microstructural changes caused by the use of ambient temperature cured hybrid 20 % cement-80 % GGBS based alkali-activated mortar. The relatively higher ratios of Ca/Al and Ca/Si compared to ordinary PC mortar proved the more excellent binding property of the C-A-S-H gel, and a denser microstructure was observed in the SEM results. The superior strength development of the hybrid 20 %cement-80 %GGBS alkaliactivated mortar was confirmed by the formation of highly cross-linked C-S-H and C-A-S-H gels due to the higher degree of polymerization and hydration. Additionally, the designed hybrid 20% cement-80 % GGBS geopolymer mortar presented significant environmental and economic benefits compared to those of ordinary PC mortar, with 32.6 % and 23.5 % lower CO2 emission and cost intensity values, respectively.
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    Machine Learning Prediction of Residual Mechanical Strength of Hybrid-Fiber-Reinforced Self-consolidating Concrete Exposed to Elevated Temperature
    (Springer, 2023) Turk, Kazim; Kina, Ceren; Tanyildizi, Harun; Balalan, Esma; Nehdi, Moncef L. L.
    Establishing the engineering properties of cement-based composites at elevated temperature requires costly, laborious, and time-consuming experimental work. Data-driven models can provide a robust and efficient alternative. In this study, extreme learning machine (ELM), support vector machine (SVM), artificial neural network (ANN), and decision tree (DT) models were trained to predict the residual compressive, splitting tensile, and flexural strengths of hybrid fiber-reinforced self-compacting concrete (HFR-SCC) exposed to high temperatures. Mixtures including macro and micro steel fibers, polyvinyl alcohol (PVA), and polypropylene (PP) were subjected to different temperature levels, leading to an experimental database of 360 specimens. Eleven input parameters including cement, fly ash, water, sand, gravel, fiber type, water reducer, and temperature were deployed. The residual mechanical strengths were targeted as output parameters. ANOVA was used to explore the influence of input parameters. Temperature was found to be the most influential parameter. Dataset consisting of 114 instances was retrieved from pertinent literature and used along with the authors' experimentally generated dataset for residual strength prediction. The experimental results were compared with predictions of ELM, SVM, ANN, and DT. ELM achieved superior performance and can offer a robust tool for predicting the residual mechanical strengths of HFR-SCC upon exposure to high temperature.
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    Macro-micro-nano and mechanical characteristics of cement clinker-gypsum-slag-based hybrid geopolymer mortars: A novel approach for reducing the cost and carbon footprint
    (Elsevier, 2025) Tanyildizi, Harun; Kina, Ceren; Acik, Volkan
    This study introduced a new binder system that includes gypsum, clinker, and blast furnace slag (BFS) within an alkali-activated system to reduce carbon dioxide (CO2) emissions in cement production. The key innovation lies in separately using clinker and gypsum, eliminating the grinding process and combining them with alkali-activated BFS at varying replacement ratios. In this context, ten ambient temperature-cured (20 +/- 2 degrees C) alkali-activated mortars, blended with clinker, gypsum, and slag, were designed, along with one water-cured control mortar containing only clinker and gypsum. Their flow diameters, as well as the initial and final setting times, were evaluated to assess their fresh properties. The optimal replacement ratio of clinker and gypsum with BFS was determined by assessing the 3-and 28-day compressive strengths, bulk density, and dynamic modulus of elasticity. The results showed that the alkali-activated mortars having 20 wt % clinker + gypsum combined with 80 wt% BFS exhibited the highest 28-day strength of 69.26 MPa. The microstructural characteristics of these samples were identified through scanning electron microscopy/energy dispersive X-ray (SEM/EDX), Fourier Transform Infrared (FT-IR), and Thermogravimetric (TG) analysis. The molar ratios of Ca/Si and Na/Al in alkali-activated BFS mortar blended with 20 wt% clinker + gypsum indicated the predominance of calcium aluminosilicate hydrate (C-A-S-H) and a denser microstructure with an 11 % pore fraction. Nano-indentation tests revealed that the calcium/sodium aluminosilicate hydrate ((C, N)-A-S-H) volume fraction was 35 %. In contrast, no phases related to geopolymerization were observed in the alkali-activated clinker + gypsum mortar, which showed noticeable deep cracks and a 15 % pore fraction. The high-density calcium silicate hydrate (C-S-H) volume was 45 % for pure clinker + gypsum-based mortar and 30 % for the alkali-activated version. Furthermore, replacing 20 wt% clinker + gypsum achieved a CO2 capture of 32.16 % and a cost saving of 20.0 %. Consequently, using clinker + gypsum-without grinding process-into alkali-activated BFS in suitable proportions offered a promising alternative for improving eco-efficiency and sustainability.
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    Novel hybrid deep-long short-term memory and machine learning algorithms for the crack-healing estimation of eco-friendly engineered cementitious composites
    (Elsevier, 2026) Kina, Ceren; Tanyildizi, Harun
    Self-healing concrete addresses cracks caused by environmental factors and tensile strain, helping to extend its lifespan. While Engineered Cementitious Composite (ECC) is particularly important for self-healing, developing a mix that includes supplementary materials instead of relying solely on cement can help reduce environmental impacts. Therefore, accurately predicting its self-healing capacity is crucial. The goal of this study is to develop two new hybrid models that combine Long Short-Term Memory (LSTM) with Gaussian Process Regression (GPR) and Least-Squares Boosting (LSBoost) models, respectively, as well as their individual algorithms to predict the crack-healing ability of eco-friendly ECC. The fly ash, silica fume, limestone powder dosages, and crack width before self-healing (CW-B) were used as input to predict the crack width of ECC after self-healing (CW-A). The ANOVA analysis revealed that CW-B had the most significant impact on CW-A, accounting for 91.84% of the variation. The individual models-LSTM, GPR, and LSBoost-estimated the CW-B with accuracies of 94.6%, 75.4%, and 64.4%, respectively. However, the performance of GPR and LSBoost improved with their hybrid usage alongside LSTM, achieving accuracies of 95.7% and 90.9%, respectively. The LSTM-GPR hybrid model outperformed all others, as evidenced by its narrow range of point-by-point residuals and Taylor plot. Additionally, the LSTM-LSBoost model, despite being slightly less accurate, still demonstrated acceptable predictive capability. These findings indicate that the novel hybrid LSTM-GPR model is the most effective for the self-healing ability prediction of eco-friendly ECC, achieving higher accuracy and lower error rates compared to actual outcomes.
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    Predicting sorptivity and freeze-thaw resistance of self-compacting mortar by using deep learning and k-nearest neighbor
    (Techno-Press, 2022) Turk, Kazi; Kina, Ceren; Tanyildizi, Harun
    In this study, deep learning and k-Nearest Neighbor (kNN) models were used to estimate the sorptivity and freeze -thaw resistance of self-compacting mortars (SCMs) having binary and ternary blends of mineral admixtures. Twenty-five environment-friendly SCMs were designed as binary and ternary blends of fly ash (FA) and silica fume (SF) except for control mixture with only Portland cement (PC). The capillary water absorption and freeze-thaw resistance tests were conducted for 91 days. It was found that the use of SF with FA as ternary blends reduced sorptivity coefficient values compared to the use of FA as binary blends while the presence of FA with SF improved freeze-thaw resistance of SCMs with ternary blends. The input variables used the models for the estimation of sorptivity were defined as PC content, SF content, FA content, sand content, HRWRA, water/cementitious materials (W/C) and freeze-thaw cycles. The input variables used the models for the estimation of sorptivity were selected as PC content, SF content, FA content, sand content, HRWRA, W/C and predefined intervals of the sample in water. The deep learning and k-NN models estimated the durability factor of SCM with 94.43% and 92.55% accuracy and the sorptivity of SCM was estimated with 97.87% and 86.14% accuracy, respectively. This study found that deep learning model estimated the sorptivity and durability factor of SCMs having binary and ternary blends of mineral admixtures higher accuracy than k-NN model.