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MakaleAçık Erişim2026Scopus SJRQ4/r/munzur/1283
Sustainable land management under uncertainty: A hybrid Bayesian BWM and RPM framework for spatial prioritization
Konurhan, Zekeriya
Sustainable land management is a complex problem due to environmental constraints and decision-maker uncertainty. This study presents an integrated solution combining Geographic Information Systems (GIS), the Bayesian Best-Worst Method (Bayesian BWM), and the Ratio Product Model (RPM) to improve the accuracy of spatial prioritization in land-use decisions. The integration of the Bayesian BWM and RPM statistically minimizes the cognitive uncertainty in expert opinions whilst enabling the most suitable areas to be ranked with precision within their own categories, without any normalization constraints. The proposed model was developed for three main categories —residential, industrial, and agricultural— in the province of Balıkesir. The analysis results revealed that the ‘ease of access to services’ (0.5660) criterion was the most decisive factor for residential suitability, whilst ‘distance to power transmission lines’ (0.1322) and ‘average rainfall’ (0.1315) were the most decisive factors for industry and agriculture, respectively. The suitability maps show that the Bandırma-Susurluk corridor is strategically prioritized for industry, whilst the Manyas-Gönen basin is prioritized for agricultural production. RPM scores (0.2750 for settlement, 0.6036 for industry, and 0.3868 for agriculture) quantify implementation priorities, providing a reliable decision-support tool for managing uncertainty in spatial planning.
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MakaleAçık Erişim2026Scopus SJRQ1/r/munzur/1224
Sustainable material pathways: Utilizing biomass-derived bio-oils from agricultural wastes to restore aged asphalt binder properties
Yalcin, Beyza Furtana; Yilmaz, Mehmet; Yalcin, Erkut; Kok, Baha Vural
This study explores the valorization of agricultural biomass wastes into high-value bio-based materials by evaluating the performance of pyrolysis-derived bio-oils obtained from straw, pine cone, and olive pomace for the rejuvenation of aged asphalt binders. The effectiveness of these biomass-derived bio-oils was comparatively assessed against two commercial rejuvenators to enhance binder reusability using renewable resources. The optimum rejuvenator dosages were determined through penetration, viscosity, and dynamic shear rheometer (DSR) tests, while the associated physical and chemical modifications were investigated using FTIR, SEM-EDS, and elemental analyses. Penetration and viscosity results indicated that all rejuvenators significantly reduced binder stiffness and improved fluidity. DSR analysis indicated that increasing rejuvenator content reduced G∗/sin δ values, reflecting a decrease in binder stiffness and a softening effect of the rejuvenators, which contributes to restoring the viscoelastic balance of the aged binder. FTIR results demonstrated that biomass-derived bio-oils, particularly those from pine cone and straw, effectively reduced carbonyl (C=O) and sulfoxide (S=O) functional groups, indicating strong chemical rejuvenation. SEM-EDS analyses showed that the heterogeneous and porous morphology of bio-oils promoted improved interaction with the asphalt binder. Based on the combined results of the three experimental approaches, the optimum rejuvenator dosages were identified as 25% for commercial products, 35% for pine cone and olive pomace bio-oils, and 45% for straw bio-oil. Overall, the findings highlight that biomass-derived bio-oils produced via pyrolysis offer a sustainable and performance-effective pathway for converting agricultural residues into value-added materials, supporting circular bioenergy and biomass utilization strategies.
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MakaleAçık Erişim2026Scopus SJRQ2/r/munzur/1209
Synergistic Effect of Cylindrical Fins and Phase Change Material on the Thermal and Economic Performance of a Double-Slope Solar Distillation Device Under Tunceli Climatic Conditions
Tuğan, Volkan; Işık, Erdem; İnallı, Mustafa
Global water scarcity driven by population growth, climate variability, and rising freshwater demand increases the need for more efficient solar desalination systems. In this study, a double-slope solar distillation device supported by a phase change material pool was developed to improve freshwater production. The phase change material layer was placed directly beneath the absorber plate to store excess thermal energy during peak solar radiation and release it during low-radiation periods. To strengthen thermal interaction between the absorber surface and the storage medium, cylindrical fins were embedded within the phase change material pool. The development of a double-slope solar distillation device integrated with a finned phase change material pool, and the simultaneous comparison of the thermal efficiency and economic analyses of single-slope and double-slope solar distillation devices under the same climatic conditions, are the prominent features of this study. Additionally, the performance of solar distillation devices has not previously been investigated under the climatic conditions of Tunceli, which further motivates this research. The developed system reached a peak freshwater productivity of 2459.2 g/m2/day, providing a 21.4% enhancement over the conventional configuration. In addition, the thermal efficiency increased from 17.7% in the conventional unit to 21.4% in the phase change material-assisted design. The economic analysis showed that the cost of distilled water was $0.097/kg for the conventional system and $0.105/kg for the proposed system, with the corresponding payback periods being approximately 11 and 12 months, respectively.
AtıfScopus2WoS2OpenAlex2
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MakaleYalnız künye2026Scopus SJRQ2/r/munzur/1191
Influence of Low-SiC Content on the Tribological and Electrical Properties of Cu Composites Produced by Spark Plasma Sintering
Çelik, Ertuğrul
In this study, copper matrix composites reinforced with varying silicon carbide (SiC) contents (0.25, 0.5, 0.75, and 1 wt.%) were fabricated using spark plasma sintering (SPS). The effects of SiC reinforcement on the microstructure, hardness, porosity, electrical conductivity, and tribological performance were systematically investigated. Brinell hardness values increased with higher SiC content, reaching up to 76 HB for the 1 wt.% SiC composite. This improvement was accompanied by a gradual increase in porosity from 0.6 to 2.1%. Electrical conductivity decreased from 93% IACS to 66% IACS with increasing SiC content. Tribological tests revealed that the specific wear rate was significantly reduced, reaching as low as 1.1 × 10−4 mm3/N m at 15 N due to the formation of a protective tribolayer. These results demonstrate that incorporating small amounts of SiC can substantially improve hardness and wear resistance, while maintaining acceptable electrical performance, making Cu-SiC composites promising candidates for durable electrical contact applications.
AtıfScopus1WoS1OpenAlex1
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MakaleYayıncıda açık erişim2026Scopus SJRQ1/r/munzur/1123
Explainable AI-based deep learning architectures for hydrogen prediction in biomass syngas and photocatalysis processes: Towards a sustainable and efficient energy economy
Imak, Andaç
This study, focusing on sustainable energy production and particularly the hydrogen (H2) economy, presents an advanced artificial intelligence (AI) framework for accurately predicting H2 production from sucrose photocatalysis catalyzed by LaFeO3 (GLFO) and the H2 concentration in syngas derived from biomass conversion. The research was conducted along two main directions: (i) deep learning (DL) architectures comprising nine specially designed recurrent neural network-based models and (ii) six conventional machine learning (ML) algorithms. For both H2 production systems, the best-performing models were identified and further interpreted using explainable AI (XAI) based on SHapley Additive exPlanations (SHAP). Among all investigated algorithms, a hybrid One-dimensional Convolutional Neural Network–Bidirectional Long Short-Term Memory–Bidirectional Gated Recurrent Unit (1D-CNN–BiLSTM–BiGRU) model demonstrated the highest predictive performance. Using 10-fold cross-validation, the proposed model achieved outstanding accuracy, with an R2 value of 0.9987 ± 0.0011 for photocatalytic H2 production prediction and 0.9980 ± 0.0006 for syngas H2 concentration prediction. These results highlight the potential of the proposed framework as a precise, reliable, and practical methodological approach for modeling and optimizing complex thermochemical energy systems.
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MakaleYalnız künye2026Scopus SJRQ1/r/munzur/1071
Enhancing durability of slag-based alkali-activated composites with dunite sand and carbon fiber: Sulfate, freeze–thaw, and thermal resistance optimization
Benli, Ahmet; Bürlükkara, Sadullah; Öz, Ali; Karataş, Mehmet; Kaplan, Gökhan
The replacement of natural river sand with sustainable alternatives is essential to reduce environmental degradation associated with aggregate extraction. In this context, dunite sand (DS), a magnesium-rich ultramafic mineral, was investigated as an eco-efficient fine aggregate in thermally cured slag-based alkali-activated composites (AACs), in combination with carbon fiber (CF) reinforcement. The study aims to clarify how aggregate substitution and fiber addition interact to control matrix densification, crack-bridging efficiency, and durability under aggressive conditions. Sixteen mixtures incorporating varying DS (0–100%) and CF (0–1.00%) contents were prepared and cured at 80°C to promote rapid slag activation and microstructural development. Mechanical performance, high-temperature resistance, sulfate durability, and freeze–thaw behavior were systematically evaluated. The results demonstrate that moderate DS substitution (~25%) significantly improves matrix compactness and interfacial quality through a micro-filler effect, leading to enhanced strength and durability. CF effectively bridge micro cracks and improve flexural and thermal performance; however, excessive fiber contents impair workability and increase porosity due to fiber clustering. Durability results confirm that optimized DS–CF combinations reduce thermal degradation, limit sulfate-induced deterioration, and enhance resistance to cyclic freezing. Overall, 25% DS replacement combined with limited CF content (≤0.50%) provides the best balance between mechanical performance and durability. The findings highlight the importance of controlling aggregate–fiber synergy and curing strategy to develop durable, thermally stable, and sustainable alkali-activated composites for harsh service environments.
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MakaleAçık Erişim2026Scopus SJRQ1/r/munzur/1047
Sustainable hybrid activation: Synergistic effects and optimization of one-part alkali-activated slag using sodium sulfate and sodium carbonate
Orhan, Taha Yusuf; Balun, Bilal; Dener, Murat; Karataş, Mehmet
The development of one-part alkali-activated materials (AAMs) using near-neutral and moderately alkaline salts offers a promising, low-carbon alternative to hazardous two-part systems. However, overcoming delayed reaction kinetics and setting anomalies remains a critical challenge. This study systematically investigates the fresh state, mechanical, and microstructural evolution of one-part slag mortars activated by sodium sulfate (Na2SO4), sodium carbonate (Na2CO3), and their hybrid combinations, adopting a hybrid binder approach through modification with calcium-rich additives (Portland cement, hydrated lime, and quicklime). Results demonstrate that while Portland cement ensures a controlled dissolution rate and balanced setting times across all environments, the incorporation of 7.5% quicklime in pure carbonate media triggers an instantaneous flash set. This extreme reactivity leads to a severe structural collapse, plunging the 90-day compressive strength to 16.11 MPa. Microstructural analyses (TGA and FTIR) suggest a link between this mechanical failure and a massive accumulation of unreacted portlandite (4.51% mass loss) rather than cohesive C-(A)-S-H gel formation, a degradation further corroborated by a drastic attenuation in ultrasonic pulse velocity (UPV). Conversely, the synergistic implementation of hybrid sulfate-carbonate activators effectively mitigates these anomalies. Sulfate ions buffer early-age kinetics to prevent flash setting-recovering the collapsed strength up to 29.16 MPa while carbonate ions facilitate sustained late-age matrix densification. Ultimately, tailoring specific calcium boosters within a hybrid activator framework presents a highly robust and potentially low-carbon framework for developing sustainable binders for practical on-site construction applications.
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MakaleAçık Erişim2026Scopus SJRQ1/r/munzur/1015
Analyzing Failures In Wind–Solar Hybrid Energy Systems Using a Fuzzy-Based BWM-MARCOS Approach: Challenges and Solutions
Başhan, Veysi; Yucesan, Melih; Demirel, Hakan; Gul, Muhammet
The reliability of hybrid renewable energy systems (HRES) depends heavily on the identification and management of potential failure modes. This study employs a fuzzy-based BWM-MARCOS approach to systematically analyze and prioritize failure modes within wind–solar hybrid systems. The model aims to prioritize the failures considering four important risk parameters: (1) severity of the failure on system, staff, and failure, (2) failure occurrence chance, (3) effort and ease of detecting the cause of the failure, and (4) economic impact of the failure. In this context, four key risk indicators were evaluated to rank failures, revealing that SP1 (cell damage), ESS1 (battery degradation), and WT11 (battery fire) are the most critical due to their potential impact on system operations. Sensitivity analysis confirmed the stability of these rankings under varying parameter weights. Additionally, cross-method validation using fuzzy TOPSIS, SAW, and MARCOS demonstrated high correlation coefficients, underscoring the reliability of the results. Tailored mitigation strategies, including advanced diagnostics, durable materials, and robust monitoring systems, are proposed to address these critical failures. While the current methodology applies to various HRES configurations, future research should incorporate real-world operational data and machine learning techniques to enhance predictive capabilities and dynamic risk management.
AtıfScopus7WoS7OpenAlex8
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MakaleAçık Erişim2026Scopus SJRQ1/r/munzur/980
“A combined experimental numerical statistical framework to characterize fracture behavior of asphalt mixtures”
Atar, Muhammed; Ince, Ragip; Bakhshizadeh, Majid; Pirmohammad, Sadjad; Furtana Yalcin, Beyza; Yilmaz, Mehmet
This study investigates the fracture and viscoelastic behavior of asphalt mixtures through a combined experimental, numerical, and statistical approach. Semi Circular Bend (SCB) tests were performed on specimens with notch lengths of 10, 20, and 30 mm at two temperature levels (0 °C and room temperature) to determine fracture toughness (KIc), fracture energy, and stiffness parameters. The Extended Finite Element Method (XFEM) was employed to simulate crack initiation and propagation, providing a close match with experimental SCB results under various thermal and geometric conditions. To further interpret the effects of temperature and crack geometry, the Response Surface Methodology (RSM) was applied to the experimental data, enabling the identification of key interaction effects and optimization of fracture parameters. The results demonstrated that decreasing temperature and increasing notch length notably reduced fracture resistance, while XFEM accurately represented the nonlinear crack propagation observed in laboratory testing. Moreover, RSM provided predictive equations that were in strong agreement with XFEM outcomes. The proposed integrated SCB-XFEM-RSM framework presents an efficient and reliable methodology for evaluating and optimizing the fracture performance of asphalt mixtures, offering valuable insights for the design of durable and sustainable pavement systems under diverse environmental conditions.
AtıfScopus3OpenAlex3
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MakaleAçık Erişim2026Scopus SJRQ1/r/munzur/943
Assessing interrelationships among circular economy implementation strategies
Ahmadi, Hadi Badri; Gul, Muhammet; Gupta, Himanshu; Yucesan, Melih; Liou, James J.H.
Strategies for implementing a circular economy (CE) are designed to enhance resource efficiency, minimize the consumption of raw materials, and decrease waste. Although many articles have investigated CE implementation strategies, research focusing on the interactions and interrelationships among these strategies remains scarce. To address this issue, this study develops an evaluation framework, with the aim of assessing the interactions among strategies for CE implementation in the context of the manufacturing sector of an emerging economy using Decomposed Fuzzy Decision-Making Trial and Evaluation Laboratory (DF-DEMATEL) methodology. The findings suggest that “Investment in green technology” is the most critical strategy and should receive the greatest attention for successfully implementing CE. This research helps industrial decision-makers and specialists to invest in the most critical strategies within their supply chains and move towards sustainable development.
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MakaleAçık Erişim2026Scopus SJRQ2/r/munzur/878
Effect of Zinc Content on the Mechanical, Corrosion, Tribological and Electrical Properties of Spark Plasma-Sintered Copper/Graphene Composites
Özkaya, Serdar; Adabaş, Yaren; Çelebi, Müslim; Karabacak, Abdullah Hasan; Çelik, Ertuğrul
Copper-based hybrid metal matrix composites reinforced with graphene and zinc were developed to achieve a balanced combination of mechanical strength, corrosion resistance, wear performance, and electrical conductivity. In this study, Cu matrix composites containing a constant graphene content of 1 wt.% and varying Zn contents (0, 5, 10, and 15 wt.%) were fabricated through mechanical alloying followed by Spark Plasma Sintering (SPS). The effects of zinc content on microstructure, densification, hardness, corrosion behavior, tribological performance, and electrical conductivity were systematically investigated. Microstructural analyses revealed that the combined use of graphene and Zn significantly influenced grain refinement, interfacial stability, and densification behavior. The composite containing 10 wt.% Zn exhibited the highest relative density (~90.5%) and maximum hardness (62 HB), indicating an optimal reinforcement level. Corrosion tests conducted in 3.5 wt.% NaCl solution demonstrated that the 10 wt.% Zn composite showed the most noble corrosion potential and the lowest corrosion current density, which was attributed to reduced porosity and improved microstructural homogeneity. Tribological results confirmed that graphene contributed to a self-lubricating effect, while Zn enhanced load-bearing capacity, leading to improved wear resistance under increasing normal loads. Electrical conductivity measurements showed a gradual decrease with increasing Zn content, mainly due to solid-solution-induced electron scattering in the Cu matrix; however, the fixed graphene addition and effective SPS consolidation helped preserve conductive pathways, allowing all composites to retain acceptable conductivity levels. The results indicate that the hybrid Cu–graphene–Zn composites exhibit a balanced combination of mechanical, corrosion, tribological, and electrical properties, with 10 wt.% Zn emerging as the optimal composition.
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MakaleAçık Erişim2026Scopus SJRQ1/r/munzur/852
Enhancing the mechanical and durability properties of fly ash/GGBS-based geopolymer mortars using dunite and polypropylene fibers
Cemalgil, Selim; Gülmez, Nihan; Yön, Merve Şahin; Karataş, Mehmet
Polypropylene (PP) fiber-reinforced geopolymer mortar reduces the high brittleness of conventional geopolymer made with natural aggregates. However, the effects of different precursor materials on the performance of PP fiber-reinforced self-compacting geopolymer mortar (SCGM) remain unclear. This study investigates the feasibility of using dunite (D), a magnesium-rich ultramafic rock with consistent mineralogy and low processing energy demand that enhances thermal stability and durability, as an alternative precursor for fly ash (FA)/ground granulated blast furnace slag (GGBS)-based geopolymers. The study examined the workability, flexural and compressive strengths, and sorptivity of mortars cured at 20 ± 2°C and 60 ± 5% relative humidity, as well as their durability under freeze–thaw cycles and high-temperature exposure. Interfacial interactions between aggregates and the geopolymer matrix, along with microstructural and morphological changes caused by fire, were analyzed using scanning electron microscopy (SEM). X-ray diffraction (XRD) and thermogravimetric analysis (TGA) were also performed for detailed material characterization. The mix containing 30% dunite exhibited the highest flexural strength, reaching 5.70 MPa at 28 days, the highest among all mixes. Under fire exposure, the mix containing 10% dunite significantly reduced mechanical strength loss and retained about 33.3% higher residual flexural strength than the FA–slag control mix at 600°C.
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MakaleAçık Erişim2026Scopus SJRQ2/r/munzur/811
Hybrid Supervised Classification and Deep Embedding–Based Profiling Framework for Electricity Consumption Analysis
Gunay, Mihriban; Yildirim, Ozal; Demir, Yakup; Zhilevski, Marin; Mikhov, Mikho; Yordanov, Nikolay
This study proposes a hybrid deep learning framework that integrates supervised classification and unsupervised profiling for electricity consumption analysis. In the supervised phase, a one-dimensional Convolutional Neural Network combined with Long Short-Term Memory (1D CNN–LSTM) architecture is developed to classify daily load patterns. The performance of the proposed model is compared with traditional machine learning and deep learning approaches, including Support Vector Machine (SVM), k-Nearest Neighbors (KNN), a standalone Long Short-Term Memory (LSTM) model, a Transformer-based model, and a standalone 1D CNN model. Experimental results on the Precon house dataset and the CU-BEMS dataset demonstrate that the proposed hybrid architecture outperforms the benchmark models, achieving classification accuracies of 87.59% and 86.40%, respectively. In the unsupervised phase, the trained CNN–LSTM encoder is utilized as a deep feature extractor. The resulting 32-dimensional latent embeddings are clustered using K-Means, Gaussian Mixture Model (GMM), Agglomerative, Spectral, and Ensemble methods. Clustering robustness is evaluated through bootstrap-based stability analysis using the Adjusted Rand Index (ARI) and the Normalized Mutual Information (NMI). The results demonstrate stable and interpretable electricity consumption profiles, particularly in the residential dataset, where near-perfect clustering stability is observed for K-Means. The proposed framework provides both improved classification performance and robust consumption profiling based on deep embedding, offering a practical tool for energy management.
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MakaleAçık Erişim2026Scopus SJRQ1/r/munzur/797
Path Dependence and Spatial Spillovers in Regional Digitalization: Evidence from Dynamic Spatial Panel Analysis in Europe
Kazar, Görkemli; Kazar, Altuğ
Digitalization is the driver of regional competitiveness and sustainable development, but its geographical impacts differ significantly across Europe. This study was conducted to determine if digital transformation results in regional sustainability or if it increases spatial inequalities, concentrating on European NUTS-1 regions for the period 2021–2025. A composite Regional Digitalization Index was developed by means of Principal Component Analysis (PCA) based on indicators measuring internet access, internet usage, and the availability of digital public services. Dynamic spatial panel econometric models were used for empirical investigation, including a Spatial Autoregressive (SAR) model and a Spatial Durbin model (SDM), which facilitated the exploration of both temporal dependence and spatial spillover. Three main conclusions can be derived from the results, as follows: The level of digitalization in a region is highly stable over time, whereby the development depends most on previous paths. Subsequently, human capital is highly significant for digital development, and its effects are not only local but also spill over to neighboring regions. Lastly, spatial interactions consist of two opposite forces—the positive diffusion from digitally advanced neighboring regions and the competitive effects related to the economic strength of neighboring regions—that further intensify the core–periphery divide.
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MakaleYalnız künye2026Scopus SJRQ2/r/munzur/778
Damage diagnosis in CFRP/Al7075-T6 aeronautical structures using piezoelectric sensors: characterization and experimental monitoring
Gahlouz, Ibtissem; Guemmour, Mohamed Boutkhil; Chellil, Sadek; Chellil, Ahmed; Houari, Amin; Aribi, Chouaib; Polat, Alper; Adjout, Warda; Bey, Mohamed
Purpose – This study aims to design a damage detection and characterization system (targeting cracks and delamination) for aeronautical composite structures. Design/methodology/approach – This methodology relies on a multidisciplinary approach combining polymer-reinforced/metallic carbon fiber hybrid structures with an integrated network of PZT sensors. It leverages experimental data and employs advanced signal processing and imaging techniques to monitor structural integrity and assess the severity of damage. Findings – Verified experimental results confirm the method's reliability in real-life situations. Research limitations/implications – Research Limits: “The primary challenges involve signal processing complexity due to the hybrid nature of AL7075-T6 and CFRP materials, as well as the potential influence of environmental factors (temperature, pressure) on PZT sensor reliability”. Research Implications: “This work enables a shift toward condition-based maintenance, significantly reducing operational costs while enhancing structural safety and extending the service life of critical aerospace and maritime assets.” Practical implications – This study enables a shift from scheduled to real-time structural monitoring. There is a drastic reduction in inspection time and aircraft downtime. Monitoring the fatigue of hybrid AL/CFRP structures helps to safely extend their service life. The study helps in providing precise diagnostics to optimize repair strategies and minimize human error. Social implications – There is a drastic reduction of structural failure risks through early-stage damage detection. Cost savings in maintenance can lead to more affordable air travel for the general public. The study helps in promoting a circular economy by safely extending the lifespan of complex structures and reducing material waste and also in shifting maintenance roles toward high-tech data analysis and specialized software expertise. Originality/value – The study investigates the complex damage mechanics at the interface of AL7075-T6 and CFRP laminates and helps in combining PZT sensor networks with advanced imaging software for real-time damage characterization. Beyond mere detection, the system assesses “structural criticality, ” providing high-value diagnostic data for maintenance optimization and cost reduction.
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MakaleYalnız künye2026Scopus SJRQ1/r/munzur/773
Mechanical and durability properties of sustainable alkali-activated mortars with ceramic insulator waste and andesite powder as partial substitutes for GBFS
Cemalgil, Selim; Yön, Merve Şahin; Dener, Murat; Karataş, Mehmet
This study explores the use of andesite powder (AP) and ceramic waste powder (CWP), derived from discarded electrical insulators, as supplementary precursors to ground granulated blast furnace slag (GBFS) in alkali-activated self-compacting mortars. The originality of the work lies in the combined utilization of these regionally sourced aluminosilicate wastes in binary and ternary binder systems, aiming to reduce GBFS dependency while ensuring adequate mechanical and durability performance. AP and CWP were used to replace GBFS at up to 50%, and the mixtures were evaluated for flowability, mechanical strength, density, porosity, water absorption, and capillary sorptivity. Durability was assessed through freeze–thaw cycling and high temperature exposure, and microstructural characteristics were examined using scanning electron microscope analysis. Results showed that while partial substitution with AP or CWP reduced compressive strength compared to the GBFS control, the incorporation of CWP in ternary systems partially compensated for the strength losses associated with AP. High temperature resistance improved markedly with increased CWP content; at 900°C, the 50% CWP mixture retained 44% of its original compressive strength, in contrast to 8% for the control. Although all mixtures containing waste exhibited reduced freeze–thaw resistance, mortars containing CWP exhibited superior performance compared to those with only AP. These findings highlight the potential of CWP, particularly in ternary systems, for developing sustainable alkali-activated mortars.
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MakaleAçık Erişim2026Scopus SJRQ2/r/munzur/693
Data-Driven Prediction of Climate Variables in Agricultural Cities of India With Hybrid GA-TCN-LSTM Model
Utku, Anil
Accurate prediction of climate variables is important in reducing the effects of natural disasters and taking preventive measures for agriculture and food security, energy sector, public health, and water resources management. For agricultural production, it is essential for producers to determine strategies for their agricultural activities and to increase agricultural productivity. Issues such as determining planting and harvest times, determining pesticides to be used against agricultural pests, selecting products to be planted, irrigation, and sustainable agriculture are directly related to climate variables. In this study, a GA-TCN-LSTM hybrid prediction model was created to determine climate variables such as relative humidity, temperature, wind speed, and dew point in Bhopal, Indore, Kanpur, Ludhiana, and Patna, which are among the prominent cities of India in terms of agricultural production. The hyper-parameters of the developed model using the temporal convolutional networks (TCN) and long short-term memory (LSTM) were optimized with the genetic algorithm (GA), and the GA-TCN-LSTM hybrid model was created. GA-TCN-LSTM was extensively compared with the base TCN-LSTM, convolutional neural network (CNN), LSTM, TCN, and CNN-LSTM. The compared models were tested using approximately 15 years of hourly, up-to-date, and real-time data of the cities. Experiments showed that GA-TCN-LSTM outperformed the compared models and had above 0.9 R-Squared (R2) for the majority of cities and climate variables.
AtıfScopus2WoS1OpenAlex2
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MakaleAçık Erişim2026Scopus SJRQ2/r/munzur/636
Experimental and numerical analysis of ceramic fiber geopolymer concrete under blast effect
Dalğıç, Aras; Yılmazer Polat, Berivan; Savaş, Sedat
This study employs both experimental and numerical methods to investigate the blast resistance of ceramic fiber-reinforced geopolymer concrete (CFGC), a cement-free material often referred to as green concrete due to its environmentally friendly nature compared to Portland cement concrete (PCC). Geopolymer concrete specimens, formulated with ground granulated blast furnace slag, silica fume, and varying proportions of ceramic fibers, were subjected to blast tests using 50, 100, 150, 200, and 250 grams of trinitroglycerin (TNG). A standard PCC specimen was utilized as a control. The results demonstrate that the incorporation of 10% ceramic fibers significantly enhances the blast resistance of the geopolymer concrete, reducing crater diameters by up to 20% compared to conventional PCC. Furthermore, the finite element model developed in ANSYS Workbench exhibits a strong correlation with the experimental data, validating the predictive capability of the numerical simulations. Overall, this research highlights the immense potential of CFGC as a sustainable, highly durable alternative for structures exposed to blast loads.
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MakaleYalnız künye2026Scopus SJRQ2/r/munzur/623
Experimental ınvestigation of hybrid passive cooling strategies for photovoltaic modules using nano-enhanced PCM and graphene-coated finned structures under hot climate conditions
Özdemir, Osman; Yücel, Halit Lutfi; Kahraman, Gökhan
This study experimentally investigates the use of several passive cooling techniques to enhance the electrical performance of photovoltaic (PV) modules under high ambient summer temperatures in Elazığ, Turkey. Throughout the trial period of July, August, and September, a system comprising four photovoltaic panels, one designated as a reference, was developed. The first passive cooling arrangement utilized a paraffin-based phase change material (PCM) RT42HC. In the second configuration, a nano-enhanced PCM (NePCM) composite was employed, prepared by dispersing 1 mass% of Al2O3 nanoparticles into the paraffin matrix and stabilizing them with 3 mass% of sodium dodecyl sulfate (SDS) to ensure homogeneous distribution. The third, hybrid configuration integrated 14 graphene-coated parabolic pin-tipped fins mounted externally onto the aluminum enclosure housing the NePCM composite. Experimental results demonstrated that the implemented passive cooling techniques markedly decreased the panel surface temperature, thereby enhancing electrical power output and photovoltaic efficiency. The hybrid graphene-coated fin-NePCM system demonstrated superior performance, achieving a maximum surface temperature reduction of 12.65 °C, a sustained electrical efficiency increase of 6–13%, and an average daily electrical energy gain of 26.23 Wh day−1 compared to the reference module. These findings demonstrate that the integrated hybrid design is an effective and reliable passive cooling method for sustaining photovoltaic system performance under hot climate conditions.
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MakaleAçık Erişim2025/r/munzur/6193
Nevşehir taşı tozunun kendiliğinden yerleşen harçlarda portland çimentosu ikamesi olarak kullanım potansiyelinin değerlendirilmesi
Yon, Merve Sahin; Karataş, Mehmet; Demir, Mustafa
Portland çimentosunun üretimi atmosferdeki karbondioksit emisyonlarının artmasına neden olmaktadır. Bu nedenle, Portland çimentosu yerine puzolanik özelliklere sahip doğal kaynakların ve atıkların kullanılması zorunlu hale gelmiştir. Bu makalede, kendiliğinden yerleşen harç yapımında Portland çimentosunun yerine kullanılabilecek doğal olarak oluşan puzolanik bir malzeme olan Nevşehir taşı tozunun etkinliği incelenmiştir. Portland çimentosu yerine %5, %10, %15 ve %20 oranlarında Nevşehir taşı tozu ilave edilerek hazırlanan harcın performansının değerlendirilmesi amacıyla bir laboratuvar çalışması yapılmıştır. Kendiliğinden yerleşen harcın başlangıçtaki mekanik performansını incelemek için 40x40x160 mm boyutlarındaki prizmatik numuneler kullanılmıştır. Kendiliğinden yerleşen harçların yayılma çapları Avrupa İnşaat Kimyasalları ve Beton Sistemleri İhtisas Federasyonu standardı referans alınarak belirlenmiştir. Seçilen oranlarda Nevşehir taşı tozu ile oluşturulan numuneler, 23±2 ̊C sıcaklıktaki suya 3 gün süreyle daldırılmıştır. Kür süresi dolan numunelere 3 günün sonunda erken yaş eğilme ve basınç dayanımı testleri uygulanmıştır. Yapılan deneysel analiz sonucunda, en yüksek basınç dayanımlarının, referans numunelerinde ve çimentoyla %5 ikameli Nevşehir taşı tozuyla oluşturulan karışımlarda olduğu belirlenmiştir. Elde edilen sonuçlar, Nevşehir taşı tozunun eğilme dayanımı üzerinde olumlu bir etki sağladığını ancak basınç dayanımında düşüşlere neden olduğunu göstermektedir.
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