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AI-driven sustainable design: Interpretable PSO-ML framework for modeling of splitting tensile strength in steel fiber-reinforced recycled aggregate concrete
(Ernst & Sohn, 2026) Utu, Rumeysa; Katlav, Metin; Turk, Kazim
The construction industry's intensive resource use and waste generation necessitate sustainable alternatives. Using recycled coarse aggregates in concrete offers a viable solution. Accurately predicting the splitting tensile strength (STS) of such concretes through data-driven methods reduces experimental needs, conserves materials, and enhances design efficiency and cost-effectiveness. With this aim, this research thoroughly investigates the feasibility of optimized machine learning (ML) algorithms through particle swarm optimization (PSO) for modeling the STS of eco-friendly steel fiber-reinforced recycled coarse aggregate concrete (SRCAC). Accordingly, based on experimental data in the literature, a comprehensive and reliable database consisting of 335 STS values with 11 input features was adopted. Additionally, SHapley Additive exPlanations (SHAP)-based feature importance and sensitivity analyses were implemented to make the black box behavior of PSO-ML models more transparent and to provide insight into the model's decision-making process. Given the results obtained, the PSO-ML models developed in this paper demonstrate high accuracy, stability, and generalizability in data-driven forecasting of STS of SRCAC, proving to be an effective tool for engineering applications. Specifically, the PSO-CatBoost and PSO-gradient boosting machine (GBM) models stand out with their high R2 values (0.931 and 0.894, respectively) and low root mean squared error (RMSE) values (0.58 and 0.73, respectively) on the testing set, demonstrating that these models can be used as reliable decision support systems, thanks to their prediction accuracy as well as their low variance and balanced distribution. In addition, based on SHAP-based explainability analyses, features such as the water/binder ratio (W/B), fiber factor (F), fine aggregate content (FA), and fiber length (l f) were determined as the most decisive parameters on STS, whereas the relative effect of variables like fiber volume fraction (V f) and fiber aspect ratio (l f /d f) remained limited. All in all, the PSO-ML framework introduced makes it possible to forecast the STS value of SRCAC with high accuracy without the need for experimental testing, providing an important digital tool for sustainable and economical concrete design. These models not only reduce the use of natural resources by determining the optimal design parameters for concrete mixes containing recycled materials but also support environmental sustainability by promoting the reuse of construction waste and reducing the carbon footprint. Thus, data-driven decision-making processes enable the development of more effective, transparent, and eco-friendly design strategies in civil engineering applications.
AI-guided design framework for bond behavior of steel-concrete in steel reinforced concrete composites: From dataset cleaning to feature engineering
(Elsevier, 2025) Katlav, Metin; Tabar, Mehmet Emin; Turk, Kazim
This research focuses on establishing an artificial intelligence (AI)-guided design approach for predicting bond behavior of profiled steel-concrete in steel-reinforced concrete (SRC) composites. For that, an extensive literature survey was undertaken, and datasets for three main characteristic bond stresses-bond stress at initial slip (zs), ultimate bond stress (zu), and residual bond stress (zr) -were gathered. In total, it was gathered data points 150 for zs, 251 for zu, and 215 for zr. In addition, the isolation forest algorithm was used to detect and clean the anomalous data in the dataset, resulting in exhaustive and trustworthy data for training the models. As AI models, four popular machine learning algorithms like RF, XGBoost, LightGBM, and CatBoost are adopted. To improve the prediction performance of the models, three cases are established by Shapley additive explanations (SHAP)-based feature engineering. Additionally, SHAP and feature importance analyses were used to examine the impact of each feature on the bond behavior in SRC composites to ensure the explainability of the model. Meanwhile, to enhance the applicability of the study in real-world applications, a graphical user interface (GUI) was designed. According to the results, the CatBoost model proved its superior predictive ability specifically for zs and zr output values; in the test phase, the RMSE values were 0.07 and 0.05, R2 values were 0.904 and 0.934, MAPE were 10.02% and 8.33%, and MAE values were 0.04 and 0.03, respectively. On the other hand, the XGBoost model had the best predictive efficiency in the test phase for the zu output value with RMSE = 0.06, R2 = 0.833, MAPE = 8.32 % and MAE = 0.04. Lastly, based on SHAP and feature importance assessments, the most impactful features on bond behavior were identified as follows: the ratio of side cover to steel section height (cv/ hs), the compressive strength of concrete (fcu), and the ratio of bonded length to steel section height (lb/hs), stirrup ratio (psv), and the yield strength of profiled steel (fy). This knowledge can guide engineers in paying focus to specific features in their design and evaluation processes, resulting in more reliable and optimized outcomes.
An intelligent framework for compressive strength prediction of eco-friendly SFR-RCAC: Base and stacked ensemble models combined with experimental verification
(Elsevier Sci Ltd, 2025) Katlav, Metin; Tabar, Mehmet Emin; Turk, Kazim
This paper adopts an approach based on base and stacked ensemble models to correctly model the compressive strength (CS), which is a key parameter to provide structural integrity and reliability of steel fiber-reinforced recycled coarse aggregate concrete (SFR-RCAC). To this end, a reliable framework is adopted that includes cleaned 440 instances with 11 input features. Additionally, the impact of the input features on the model is investigated in detail via SHapley additive explanation (SHAP) and partial dependence plots (PDPs) analyses. To facilitate practical implementation, a graphical user interface (GUI) is designed to make the estimation process user-friendly and its reliability is verified by additional experimental tests. Based on the results, all the developed models are capable of predicting the CS of SFR-RCAC with extraordinary accuracy and reliability: the 6 base ensemble models achieved an average R2 = 0.936 and RMSE = 3.55 during the testing phase, while the 35 stacked models recorded R2 = 0.942 and RMSE = 3.38, respectively. Notably, the stacked ensemble model (SM26) with the combination of Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Extra Trees Regressor (ETR) showed the best prediction performance in the test phase with the highest R2 (0.948) and the lowest RMSE (3.20) as well as the highest total score (287). Additionally, the error rate between the experimental values and the GUI predictions for the 10 designed mixes remains below +/- 8 %, verifying that the proposed GUI has high accuracy and robust generalization capability. Moreover, based on SHAP and PDP analyses, it is recommended for practical engineering applications to optimize the CS of SFR-RCAC by limiting the recycled coarse aggregate substitution ratio (Rr) to approximately 0.40, maintaining the steel fiber volume fraction (Vf) around 1.0 %, keeping the fiber factor (F) within the range of 0.6-0.8, and adjusting the water-tobinder (W/B) ratio between 0.30 and 0.40. To conclude, this research reveals the outstanding performance of the proposed models and GUI for predicting the CS value of SFR-RCAC and provides a significant contribution to the existing literature in this field. Thus, by promoting the efficient use of recycled coarse aggregates, it reduces the consumption of natural resources and allows the recycling of environmentally hazardous waste in the construction industry.
Bond strength of reinforcing bars in hybrid fiber-reinforced SCC with binary, ternary and quaternary blends of steel and PVA fibers
(Springer, 2021) Kina, Ceren; Turk, Kazim
In this study, the effect of inclusion of single fiber and binary, ternary and quaternary fiber hybridization on the bond performance of high strength self-compacting concrete (SCC) was investigated and 12 beam specimens having lap-spliced reinforcing bars in tension at the mid-span were designed. Four different fibers were used with different hybridizations. Fiber reinforced concrete beams demonstrated higher failure loads with a greater number of cracks. Especially the specimens with ternary fiber hybridization showed the best performance that the ultimate load resistance was 60% higher than that of the specimen without fiber. After splitting failure, the beam specimens with binary hybridization of macro steel fiber and polyvinyl-alcohol (PVA) fiber and also, the specimens with ternary hybridization of macro steel fiber, micro steel fiber with 13 mm in length (OL 13/.16) and PVA fiber showed a gradually drop in performance with increasing deflections. Besides, results indicate that the least improvement in bond strength was observed in the specimen having quaternary fiber hybridization of macro steel fiber, OL 13/.16 and micro steel fiber with 6 mm in length (OL 6/.16) and PVA fiber. The bond strength results were also compared with the ones calculated from the existing prediction equations. It was found that Zuo and Darwin and Esfahani and Rangan equations gave better results than the equations of Orangun et al. and ACI 318 on the hybrid fiber reinforced SCC. Based on the results, it was indicated that in these proposals, a new parameter was necessary for the fiber content so in this study, a new empirical equation was derived by using fiber reinforced index for fiber reinforced SCC. The proposed equation gave better estimation in the specimens with single fiber and binary and ternary fiber hybridization.
Comparison of extreme learning machine and deep learning model in the estimation of the fresh properties of hybrid fiber-reinforced SCC
(Springer London Ltd, 2021) Kina, Ceren; Turk, Kazim; Atalay, Esma; Donmez, Izzeddin; Tanyildizi, Harun
This paper studied the estimation of fresh properties of hybrid fiber-reinforced self-compacting concrete (HR-SCC) mixtures with different types and combinations of fibers by using two different prediction method named as the methodologies of extreme learning machine and long short-term memory (LSTM). For this purpose, 48 mixtures, which were designed as single, binary, ternary and quaternary fiber-reinforced SCC with macro-steel fiber, two micro-steel fibers having different aspect ratio, polypropylene (PP) and polyvinylalcohol (PVA), were used. Slump flow, t(50) and J-ring tests for designed mixtures were conducted to measure the fresh properties of fiber-reinforced SCC mixtures as per EFNARC. The experimental results were analyzed by Anova method. In the devised prediction model, the amounts of cement, fly ash, silica fume, blast furnace slag, limestone powder, aggregate, water, high-range water-reducer admixture (HRWA) and the fiber ratios were selected as inputs, while the slump flow, t(50) and the J-ring were selected as outputs. Based on the Anova analysis' results, the macro-steel fiber was the most important parameter for the results of slump-flow diameter and t(50), while the most important parameter for the results of J-ring was fly ash. Furthermore, it was found that the use of more than 0.20% by volume of 6/0.16 micro-steel fiber positively influenced the fresh properties of SCC mixtures with hybrid fiber. On the other hand, the inclusion of steel fiber instead of synthetic fiber into SCC mixture as micro-fiber was more advantageous in terms of workability of mixtures as result of hydrophobic nature of steel fibers. This study found that extreme learning machine model estimated the slump flow, t(50) and J-ring with 99.71%, 81% and 94.21% accuracy, respectively, while deep learning model found the same experimental results with 99.18%, 77.4% and 84.8% accuracy, respectively. It can be emphasized from this study that the extreme learning machine model had a better prediction ability than the deep learning model.
Comprehensive experimental research on SCC with low hybrid fiber content: From workability to mechanical properties
(Ernst & Sohn, 2026) Ari, Abdulkerim; Katlav, Metin; Turk, Kazim
Hybrid fiber-reinforced self-compacting concrete (SCC) is gaining widespread popularity in construction engineering applications owing to its superior hardened concrete properties, while it isn't valid for workability, especially when it includes a high volume of fibers. While the incorporation of fibers can enhance the performance of SCC, it often challenges key fresh properties such as flowability and passing ability, thereby necessitating mix adjustments that result in additional costs. Furthermore, the high cost of fibers requires careful determination of the optimal fiber type combination and content. In this context, the aim and objective of this work is to comprehensively investigate the effects of different types (steel and synthetic) and combination (single, binary and ternary) of fibers on the workability (slump-flow, T500, J-ring and V-funnel) and mechanical (compressive strength, splitting tensile strength, elastic modulus, shear strength, and flexural tensile strength) properties of SCC mixes with low hybrid fiber content (total by volume 0.75%). After defining the workability properties of all mixes, mechanical property tests were performed on samples with curing periods of 7-, 28-, and 56-days. According to the results obtained, all fiber-reinforced SCC mixes exhibit high performance in terms of both workability and mechanical properties. In particular, binary hybrid fiber systems containing long hooked-end steel fibers and short straight steel fibers provided the best overall performance, yielding more effective outcomes compared to other mixes. In conclusion, this work has demonstrated that SCC mixes with low hybrid fiber content can successfully meet a wide range of engineering requirements, from workability to mechanical performance. This also implies that these mixes offer significant advantages in terms of both cost-effectiveness and ease of application due to workability superior of SCC mixes with low hybrid fiber content. These outcomes emphasize that SCC mixes with low hybrid fiber content can be safely used in structural concrete members subjected to complex loads such as punching, shear, and bending, and may offer more economical and sustainable alternatives to high-fiber content systems.
Coupled effects of limestone powder and high-volume fly ash on mechanical properties of ECC
(Elsevier Sci Ltd, 2018) Turk, Kazim; Nehdi, Moncef L.
Owing to its exceptional strain capacity, which can reach hundreds of times that of normal concrete, and its reduced crack width, engineered cementitious composites (ECC) are a very promising solution for mitigating many of the problems that generate colossal backlogs of deteriorated concrete structures worldwide. However, research is needed to develop more sustainable ECC with flexible formulation that uses local materials. This paper investigates the coupled effects of using limestone powder in ECC as partial or total replacement for silica sand aggregate, coupled with using high-volume fly ash as a binder. The compressive and flexural strengths and fracture toughness for the formulated ECCs were examined at 3, 28 and 90 days. The results of this study demonstrate that sustainable ECC for resilient structural applications can be produced. It is aimed that more flexible formulations of ECC using local materials with lower environmental footprint could emerge and contribute to more durable and sustainable civil infrastructure. (C) 2017 Elsevier Ltd. All rights reserved.
Deep learning and machine learning-based prediction of capillary water absorption of hybrid fiber reinforced self-compacting concrete
(Ernst & Sohn, 2022) Kina, Ceren; Turk, Kazim; Tanyildizi, Harun
Deep auto-encoders and long short-term memory methodology (LSTM) based on deep learning as well as support vector regression (SVR) and k-nearest neighbors (kNN) based on machine learning models for the capillary water absorption prediction of self-compacting concrete (SCC) with single and binary, ternary, and quaternary fiber hybridization were developed. A macro and two types of micro steel fibers having different aspect ratios, and PVA fiber were used. One hundred and sixty-eight specimens produced from 24 mixtures were used in the prediction models. The input was the content of cement, fly ash, silica fume, fine and coarse aggregate, water, superplasticizer (SP), macro and micro steel fibers, PVA, time that the specimen was immersed in water, and splitting tensile strength. Water absorption was used as output. As per the ANOVA analysis of the experiment results, the most effective parameters were macro steel fiber and time for tensile strength and water absorption, respectively. Finally, binary hybridization of 1% macro steel fiber and PVA improved the splitting tensile strength while the use of PVA as binary, ternary, and quaternary fiber hybridization increased the water absorption of SCC specimens. The auto-encoder, LSTM, SVR, and kNN models predicted the water absorption of fiber reinforced SCC with 99.99%, 99.80%, 94.57%, and 95.50% accuracy, respectively. The performance of deep autoencoder in the estimation of water absorption of fiber-reinforced SCC was superior to the other prediction models.
Durability of Engineered Cementitious Composites Incorporating High-Volume Fly Ash and Limestone Powder
(Mdpi, 2022) Turk, Kazim; Kina, Ceren; Nehdi, Moncef L.
This study investigates the effects of using limestone powder (LSP) and high-volume fly ash (FA) as partial replacement for silica sand (SS) and portland cement (PC), respectively, on the durability properties of sustainable engineered cementitious composites (ECC). The mixture design of ECC included FA/PC ratio of 1.2, 2.2 and 3.2, while LSP was used at 0%, 50% and 100% of SS by mass for each FA/PC ratio. Freeze-thaw and rapid chloride ions penetrability (RCPT) tests were performed to assess the durability properties of ECC, while the compressive and flexural strength tests were carried out to appraise the mechanical properties. Moreover, mercury intrusion porosimetry (MIP) tests were performed to characterize the pore structure of ECC and to associate porosity with the relative dynamic modulus of elasticity, RCPT and mechanical strengths. It was found that using FA/PC ratio of more than 1.2 worsened both the mechanical and durability properties of ECC. Replacement of LSP for SS enhanced both mechanical strengths and durability characteristics of ECC, owing to refined pore size distribution caused by the microfiller effect. It can be further inferred from MIP test results that the total porosity had a vital effect on the resistance to freezing-thawing cycles and chloride ions penetration in sustainable ECC.
The effect of hybrid fiber and shear stud on the punching performance of flat-slab systems
(Elsevier, 2023) Bassurucu, Mahmut; Turk, Kazim; Turgut, Paki
In this paper, for the first time, the binary/ternary hybrid fiber and/or shear stud reinforcement as a measure was used to improve the punching performance of flat-slab systems by innovative selfcompacting concrete (SCC). Because in these slab systems, sudden and brittle punching failure can be seen due to application and design errors, early removal of formwork, changes in the purpose of use of the building, earthquakes, etc. Besides, numerous studies investigated the punching performance of the single fiber and/or shear stud reinforced flat-slab systems, but research into the measures of the hybrid fiber or the combined use of hybrid fiber and shear stud reinforcement, which were the variable parameters of this study, was quite lacking. For this purpose, the half-scale slab-column connection elements were produced from SCC containing different punching measures (binary/ternary hybrid and/or shear stud) and tested to investigate the punching performance of flat-slab systems. In conclusion, it was found that hybrid fiber reinforcement was the best punching measure to improve the punching performance of slabcolumn connection elements with/out shear stud. Besides, 3D graphs were drawn so that designers and researchers could estimate the punching strength and energy absorption capacity for flat-slab systems with/out shear stud based on the parameters of micro fiber type and total volume fraction. On the other hand, empirical formulas were developed to predict the punching strength of binary/ternary hybrid fiber reinforced flat-slab systems with/out shear stud by compressive strength, fiber reinforcement index, the slab useful height, and the punching perimeter parameters.
Effect of limestone powder on the rheological, mechanical and durability properties of ECC
(Taylor & Francis Ltd, 2017) Turk, Kazim; Demirhan, Serhat
This paper presents the results of an investigation on the influence of a replacement of limestone powder (LSP) by silica sand (SS) on properties of engineered cementitious composites (ECC). For this purpose, five different ECC mixtures were adopted: ECC mixture with only SS (M1) for control purposes and four ECC mixtures in which SS is partially replaced by four levels of replacements (25, 50, 75 and 100% by weight of total SS) of LSP. The mechanical properties of ECC were investigated for 3, 28 and 90 days, while the durability tests were performed for 90 days. It was concluded that increase in LSP content resulted in a decrease in fluidity of ECC mixtures indicating longer flow times. Increase in the LSP content had a positive effect on the performance of the compressive strength, fracture toughness and flexural strength at the ages of 3 and 28 days, while this was not valid at the age of 90 days when compared to the reference mixture M1. Moreover, it can be said that the use of LSP instead of SS in ECC mixtures had the positive effect on ductility and good dispersion of fibres due to its fine particle structure compared to SS. On the other hand, the mass loss due to acid attack and the sorptivity coefficient of ECC specimens decreased, while the carbonation resistance increased in all ECC mixes compared to the reference mixture M1 with only SS when LSP content in ECC mixtures increased.
Effect of macro and micro fiber volume on the flexural performance of hybrid fiber reinforced SCC
(Techno-Press, 2020) Turk, Kazim; Kina, Ceren; Oztekin, Erol
The aim of this study is to investigate the flexural performance of hybrid fiber reinforced self-compacting concrete (HFRSCC) having different ratio of micro and macro steel fiber. A total of five mixtures are prepared. In all mixtures, the sum of the steel fiber content is 1% and also water/binder ratio is kept constant. The amount of high range water reducer admixture (HRWRA) is arranged to satisfy the workability criteria of self-compacting concrete. Four-point bending test is carried out to analyze the flexural performance of the mixtures at 28 and 56 curing days. From the obtained load-deflection curves, the load carrying capacity, deflection and toughness values are investigated according to ASTM C1609, ASTM C1018 and JSCE standards. The mixtures containing higher ratio of macro steel fiber exhibit numerous micro-cracks and, thus, deflection-hardening response is observed. The mixture containing 1% micro steel fiber shows worst performance in the view of all flexural parameters. An improvement is observed in the aspect of toughness and load carrying capacity as the macro steel fiber content increases. The test results based on the standards are also compared taking account of abovementioned standards.
Effect of rebar arrangements on the structural behavior of RC folded plates manufactured from hybrid steel fiber-reinforced SCC
(Elsevier, 2024) Turk, Kazim; Katlav, Metin; Turgut, Paki
In this paper, for the first time in the literature, the effect of different rebar arrangements on the structural behavior of reinforced concrete (RC) folded plates was investigated. For this purpose, RC folded plate specimens having various rebar arrangements fabricated from high-strength hybrid steel fiber-reinforced self-compacting concrete (HSFR-SCC) were tested by subjecting them to four-point bending loading. Then, the structural behavior properties of RC folded plates, such as crack patterns, failure mode, load-midspan displacement relationship, flexural stiffness, ductility, load-strain behavior, and moment-curvature response, were compared and thoroughly assessed. According to the experimental results, different rebar arrangements except for the detailing of transverse rebar induced the RC folded plates to exhibit a combined plate/beam-slab movement behavior, which resulted in higher load-carrying capacity. Notably, it can be said that the detailing of 90-degree hooked steel was generally more effective on the structural behavior of RC folded plates. In addition, the use of hybrid steel fiber instead of transverse rebar provided resistance against shear stresses at the joints and prevented the plates from separating. This application, in constructing the RC folded plates, can reduce labor costs, increase costeffectiveness, and shorten erection time. Therefore, it is obvious that the use of hybrid steel fiber as an alternative to the detailing of transverse rebar for the construction of RC folded plates will ensure some advantages. In conclusion, it is thought that the findings of the study can provide important guidance on the rebar arrangement of RC folded plates in structural engineering applications for structural engineers and designers.
Electrical conductivity and heating performance of hybrid steel fiber-reinforced SCC: The role of high-volume fiber and micro fiber length
(Elsevier, 2023) Turk, Kazim; Cicek, Nazli; Katlav, Metin; Donmez, Izzeddin; Turgut, Paki
Recently, it has become very popular to develop electrically conductive concrete composites for active deicing and snow-melting of transportation infrastructure. These composites should have stable electrical conductivity and a uniform heating performance, as well as high mechanical and durability properties, for a sustainable solution. In this context, the main motivation of this study is to develop an electrically conductive hybrid steel fiber reinforced self-compacting concrete (HSFR-SCC) composite for ice and snow removal applications. The electrical conductivity and heating performance of self-compacting concrete (SCC) mixtures having different fiber volumes (1.00, 1.25, and 1.50%) and the combination of macro steel fiber with micro steel fibers having lengths of 13 and 6 mm as single and hybrid were experimentally investigated for the first time. For this purpose, a total of ten SCC mixtures were designed, one of which was non-fiber Control, the others had steel fiber volumes of 1.00, 1.25, and 1.50% and different fiber combinations. Workability (slump-flow, T500 and J-ring) tests on HSFR-SCC mixtures were performed with reference to EFNARC. Then, mechanical (compressive and flexural strengths), electrical resistivity, and heating performance tests of 90-day HSFR-SCC samples were carried out. Before the electrical resistivity and heating performance tests, HSFR-SCC samples were kept in an oven at 105 & PLUSMN; 5 degrees C for 24 h to measure their most critical state (dry) performance. Moreover, using multiple linear regression analysis, empirical equations and contour plots were developed to predict the electrical resistivity and temperature increase values of HSFR-SCC samples depending on fiber volumes and combinations. Considering the experimental results, electrically conductive HSFR-SCC mixtures with satisfactory workability and high strength were obtained. The addition of various volumes and combinations of steel fiber to SCC significantly improved the electrical conductivity and heating performance of the concrete, while the mixtures with hybrid fiber were the best for all fiber volumes. As for the different micro fibers added to the HSFR-SCC mixtures, the 13 mm length micro steel fiber was much more effective in improving the electrical resistivity and heating performance of the samples compared to the 6 mm length micro steel fiber. Also, it was found that the fiber-reinforced index of electrically conductive HSFR-SCC samples could be 0.87 for effective and efficient electrical conductivity.
Estimation of strengths of hybrid FR-SCC by using deep-learning and support vector regression models
(Ernst & Sohn, 2022) Kina, Ceren; Turk, Kazim; Tanyildizi, Harun
In this work, to estimate the compressive, splitting tensile, and flexural strength of self-compacting concrete (SCC) having single fiber and binary, ternary, and quaternary fiber hybridization, the deep-learning (DL) and support vector regression (SVR) models were devised. The fiber content and coarse aggregate/total aggregate ratio (CA/TA) were the variables for 24 designed mixtures. Four different fibers, which were a macro steel fiber, two types of micro steel fibers with different aspect ratio, and polyvinyl alcohol (PVA) fiber, were used in SCC mixtures. The specimens of each mixture were tested to measure the engineering properties for 7, 28, and 90 days. The amount of cement, fly ash, fine aggregate, CA, high-range water-reducing admixture, water, macro steel fiber, PVA fiber, two types of micro steel fibers, and curing time were selected as input layers while the output layers were strength results. The experimental results were compared with the estimation results. The engineering properties were estimated using the SVR model with 95.25%, 87.81%, and 93.89% accuracy, respectively. Furthermore, the DL model estimated compressive strength, tensile strength, and flexural strength with 99.27%, 98.59%, and 99.15% accuracy, respectively. It was found that the DL estimated the engineering properties of hybrid fiber-reinforced SCC with higher accuracy than SVR.
An experimental and statistical investigation on the fresh and hardened properties of HFR-SCC: the effect of micro fibre type and fibre hybridization
(Taylor & Francis Ltd, 2023) Bassurucu, Mahmut; Turk, Kazim
In this study, experimental and statistical analyses were conducted to reveal the effect of micro steel and/or polypropylene (PP) fibre with macro steel fibre as binary and ternary hybridization on the fresh, mechanical and flexural performance of hybrid fibre reinforced self-compacting concrete (HFR-SCC). For this purpose, some tests were conducted related to fresh and hardened properties. It was seen that PP had negative effect on the fresh properties of HFR-SCC mixtures compared to micro steel fibre. Moreover, multiple linear regression (MLR) was used to estimate the fresh and hardened properties of HFR-SCC as function of the percent of fibres by volume while ANOVA analysis determined the contributions of parameters. It was obtained from statistical analysis that there was a good correlation between experimental results and predicted values with approximately R-2=0.91 except for compressive strength. Finally, the use of PP with micro steel fibre as ternary hybridization increased the compressive, splitting, flexural tensile strengths, toughness and ductility of HFR-SCC with 1.1%, 13.2%, 18.8%, 14.9% and 26.3%, respectively, while the inclusion of PP into the mixture as binary hybridization had less positive effect on the hardened properties compared to binary steel fibre reinforced SCC with 0.25% micro steel fibre.
Experimental investigation and explainable artificial intelligence-based modeling of punching shear behavior in self-compacting concrete flat-slabs with low hybrid fiber content
(Pergamon-Elsevier Science Ltd, 2026) Ari, Abdulkerim; Katlav, Metin; Donmez, Izzeddin; Turk, Kazim
Flat-slab systems manufactured with self-compacting concrete (SCC) incorporating low hybrid fiber content offer a promising alternative for improving punching shear performance while enhancing constructability in building applications. In this paper, the punching shear behavior of flat-slabs produced with single, binary, and ternary fiber-reinforced SCC was experimentally investigated in terms of load-deflection response, ductility, toughness, cracking behavior, and failure mode. In parallel, a comprehensive database comprising 268 fiber-reinforced concrete flat-slab test results collected from the literature was established, and artificial intelligence (AI)based predictive models were developed to estimate punching shear capacity (Vpun). Model performance was evaluated using statistical indicators, whereas SHapley Additive exPlanations (SHAP) feature importance and partial dependence plots (PDPs) were employed to enhance interpretability and reveal the governing parameters influencing punching capacity. The outcomes demonstrate that binary hybrid fiber systems provide the most effective enhancement in punching capacity and post-cracking performance, even at low fiber contents, outperforming conventional solutions such as shear studs. Among the developed AI models, the Extra Trees Regressor and Random Forest algorithms exhibited the highest prediction accuracy for the Vpun. Finally, the AI models were integrated into a user-friendly graphical interface to facilitate practical engineering applications. Overall, this research contributes by experimentally validating low-fiber SCC flat-slabs as an efficient punching solution and by proposing an explainable, data-driven decision-support framework for engineering design.
Explainable artificial intelligence (XAI)-powered design framework for lightweight strain-hardening ultra-high-performance composites (SH-UHPC)
(Wiley, 2026) Katlav, Metin; Turk, Kazim
Lightweight strain-hardening ultra-high-performance concrete composite (SH-UHPC) is an outstanding alternative for engineering applications and infrastructure thanks to its outstanding strength, toughness, ductility, and low density. The integration of artificial intelligence (AI)-based modeling strategies into engineering problems can substantially accelerate material design processes while reducing experimental costs and time. Within this scope, the main motivation of this study is to predict the compressive strength (CS) of lightweight SH-UHPC via a grey wolf optimization (GWO)-integrated machine learning (ML)-based modeling that offers high accuracy and reliability, thereby reducing experimental cost and time requirements while supporting environmental and economic sustainability. The overall results demonstrate that all developed GWO-ML models achieved impressive performance levels in predicting the CS of lightweight SH-UHPC. In particular, the GWO-Extra Trees Regressor (GWO-ETR) model demonstrated superior performance compared to other GWO-ML models in terms of performance metrics (RMSE = 6.99, MAPE = 3.25%, and R-2 = 0.929), scatter plots (all points remained within a 10% margin of error), and uncertainty analysis (U-95 = 10.0) during the testing phase. In addition, SHapley Additive exPlanations-based feature importance analysis, as well as individual conditional expectation analysis and partial dependence plots, provided valuable insights and design suggestions for engineers and practitioners. Finally, an interactive graphical user interface has been developed to enable the application of similar data-driven analyses on a larger scale and to obtain rapid predictions; however, it is suggested that the database be continuously updated to improve model performance and extend its generalization capacity in the future.
Explainable ensemble algorithms with grey wolf optimization for estimation of the tensile performance of polyethylene fiber-reinforced engineered cementitious composite
(Elsevier, 2025) Tabar, Mehmet Emin; Katlav, Metin; Turk, Kazim
This paper extensively examines the applicability of optimized ensemble machine learning (ML) algorithms via grey wolf optimization (GWO) to estimate the tensile performance of polyethylene fiber-reinforced engineered cementitious composites (PE-ECC). A robust and credible dataset is utilized for the establishment of the models based on available studies in the literature: The dataset includes 132 instances of PE-ECC mixes with 11 input features and 2 target output. Moreover, feature importance, Shapley additive explanation (SHAP) and partial dependence (PDP) analyses are implemented to enhance the explainability of the estimation models and to address the black box challenge of ML models. Based on the obtained results, the optimized extreme gradient boosting (XGBoost) and categorical boosting (CatBoost) models with GWO estimated the tensile performance of PE-ECC more effectively and accurately in comparison with other ensemble models. This has been extensively evaluated and proved through various approaches such as performance indicators, Taylor diagram, error analysis, and score analysis. To give a quantitative example, in the testing phase, for the prediction of tensile strain capacity, the GWO-XGBoost model reached the highest accuracy values with R2= 0.785 and RMSE= 1.077, whereas for the GWO-CatBoost model, these performance indicators were 0.764 and 1.129, respectively. In terms of tensile strength prediction, the GWO-XGBoost model achieved a high prediction accuracy with R2= 0.930 and RMSE= 1.004, while for the GWO-CatBoost model, R2 and RMSE were 0.932 and 0.987, respectively. Meanwhile, SHAP and PDP analyses were employed to identify the most influential features on output, and thus providing precious insight for designers to improve the tensile performance of PE-ECC. Additionally, a userfriendly graphical user interface (GUI) was constructed for estimating the tensile performance of PE-ECC and validated with new experimental datasets, illustrating the efficiency of the models. All in all, the importance of this work highlights the superior performance of the advanced GWO-ML models and GUI for estimating the tensile performance of PE-ECC and is thought to be a valuable contribution for further research in this area.
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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