Yazarlar
- Can, Erol(29)
- Kumbasaroglu, Atila(17)
- Yalciner, Hakan(17)
- Bayata, Halim Ferit(14)
- Ting, Naim Suleyman(14)
- Katipoğlu, Okan Mert(13)
- Bayrak, Osman Ünsal(12)
- Sahin, Yakup(12)
- Turan, Ahmet İhsan(9)
- Baş, Fatih İrfan(8)
- Destek, Mehmet Akif(8)
- Manga, Müge(8)
- Çolak, Muhammed Ali(8)
- Bulut, H. Alperen(6)
- Manga, Muge(6)
- Tastimur, Canan(6)
- Akin, Erhan(5)
- Akoğuz, Harun(5)
- Akın, Fatih(5)
- Balli, Esra(5)
- Dede, Tayfun(5)
- Sarıgöl, Metin(5)
- Sarıçiçek, İnci(5)
- Aka, Salih(4)
- Ayasun, Saffet(4)
- Ayaz, Yaşar(4)
- Celik, Alper(4)
- Demirci, Oğuz Kürşat(4)
- Demirel, Soner(4)
- Kalkan, Orhan(4)
- Keleş, Ömer Faruk(4)
- Tortum, Ahmet(4)
- Tuysuz, Suat(4)
- Yavuz, Selahattin(4)
- Yazici, Ahmet(4)
- Yazıcı, Ahmet(4)
- Yesilyurt, Huseyin(4)
- Yılmaz, Mehmet(4)
- Çabuk, Umut Can(4)
- Özkan, Kemal(4)
Yayınlar
- ArticleAçık Erişim2026Evaluation of ground motions and seismic performance of RC buildings after 2023 Kahramanmaraş earthquakes: design and construction deficiencies
İnce, Elif Gökçe; Çakıl, Burak; Şimşek, Mutlu; İnce, Ozan
Two major earthquakes occurred on the East Anatolian Fault Zone in Türkiye on February 6, 2023. The epicenter of these earthquakes was Pazarcık and Elbistan, distinct from Kahramanmaraş province. These earthquakes occurred on the same day, 9 hours apart, and effected a very large area. This study evaluates the damage in reinforced concrete buildings during the Kahramanmaraş earthquakes. Firstly, this study examined Türkiye’s seismicity, fault lines, and recent earthquakes in Türkiye. Then, the fault rupture of the Kahramanmaraş earthquakes and the affected regions from these earthquakes were exam-ined. Acceleration records of the Pazarcık and Elbistan earthquakes were evaluated by comparing them with the Türkiye Building Earthquake Code-2018 (TBEC-2018). These acceleration records were compared with the largest earthquake (DD1) and design earthquake (DD2) defined in TBEC-2018. Requirements for earthquake-resistant building design and the deficiencies in the current Türkiye building stock were discussed. Damages in RC buildings were evaluated in terms of design and construction perspectives. It has been observed that deficiencies are frequently made in the application of reinforcement details in RC buildings. Brittle shear damage has been commonly observed in reinforced concrete buildings, even though they are expected to behave ductile. Design and construction deficiencies have been observed to be widespread even in buildings still under construction.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2026Compact and Cost-Efficient Dual-Buck NPC Inverter with Dead-Time Elimination and Shoot-Through Suppression
Sahin, Yakup; Blaabjerg, Frede; Hatas, Hasan; Ting, Naim Suleyman; Bakan, Ahmet Faruk; Ahmed, Md Rishad
Dead-time insertion is the conventional method for preventing shoot-through faults in neutral-point-clamped inverters (NPCIs), but it inevitably degrades output-voltage quality, lowers efficiency, and increases control complexity due to dead-time compensation. This article presents a novel transformer-based dual-buck NPCI (T-DB-NPCI) cell that inherently solves this problem at the topology level. The proposed structure not only eliminates the need for dead-time during normal switching transitions but also provides robust hardware suppression of destructive, long-duration shoot-through faults. The proposed cell is designed to safely withstand forced shoot-through faults of up to 2 μs, ensuring high reliability without requiring complex detection algorithms. The topology features a remarkably compact structure comprising only a single high-frequency transformer and one auxiliary diode, enabling all semiconductor devices to operate at only half the DC-link voltage. Furthermore, the streamlined structure allows for the use of standard sinusoidal pulse width modulation (SPWM), significantly reducing control complexity. Comprehensive results from a 425-W prototype achieving 97.4% peak experimental efficiency confirm excellent performance. A comparative analysis reveals an 8% improvement in power density (683 W/kg) and a 6% cost advantage over its closest counterpart. The T-DB-NPCI topology emerges as a highly promising solution where reliability and simplicity are critical.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2026Investigating the rubberized concrete-filled steel tube composite columns and developing artificial intelligence-based analytical models for ultimate axial strength prediction
Simsek, Oguz; Katipoğlu, Okan Mert; İpek, Süleyman; Güneyisi, Erhan; Güneyisi, Esra Mete
The application of rubberized concrete (RuC) in structural systems has led to the development of a novel composite system: the rubberized concrete-filled steel tube (RuCFST). The characteristics of infill materials, particularly RuC, significantly influence the structural behavior and performance of these composite systems. This study aims to evaluate the ultimate axial strength of RuCFST columns, considering rubber aggregate content up to 75% and infill concrete compressive strength up to 63 MPa, and propose artificial intelligence (AI)-based design models using deep learning (CNN, DNN, Autoencoder), machine learning (CatBoost), hybrid AI/neuro-fuzzy (ANFIS), and unsupervised neural network (SOM) to predict the ultimate axial strength. A robust dataset was compiled from the literature, comprising the results of 131 RuCFST column specimens. The experimental results underwent a comprehensive statistical assessment using various analytical methods, including Pearson correlation coefficient analysis, histogram analysis, distribution fitting curve analysis, and box-and-whisker plot analysis. Additional statistical metrics such as Kolmogorov–Smirnov, skewness, and kurtosis values were also used. Furthermore, by considering various variables covering the geometrical and material properties of the RuCFST columns, the design models were developed using AI techniques. The statistical evaluation results indicated that the compiled dataset is statistically significant and encompasses various input and output parameters. The design models developed in this study displayed varying prediction performance. However, those created using the Autoencoder and ANFIS methods showed statistically satisfactory performance in determining the load-carrying capacity of the columns.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2026Machinability Assessment of Forged, SLM and Heat-Treated Inconel 718 Under Dry and MQL Conditions Using Machine Learning Models
Cemaloğlu, Fulya; Özlü, Barış; Demir, Halil; Kara, Fuat
In this study, the milling performance of Inconel 718 alloys produced by forging (WP1), Inconel 718 produced by Selective Laser Melting (SLM) (WP2), and Inconel 718 (WP3) subjected to heat treatment after SLM, under different cooling/lubrication conditions, was evaluated using experimental and artificial intelligence-based approaches. Microstructural analysis showed a homogeneous fine-grained structure in WP1, while WP2 exhibited dendritic features and porosity. Heat treatment improved the microstructural homogeneity of WP3. The hardness values of WP1, WP2, and WP3 were 457 Hv, 303.33 Hv, and 391 Hv, respectively. Milling experiments yielded cutting forces of 336.5–1185.9 N, surface roughness values of 0.22–1.39 µm, and cutting temperatures of 168–658 °C. Compared with dry machining, MQL reduced average cutting force and cutting temperature by 15.5% and 18.65%, respectively, while improving tool wear and surface integrity. Machine learning models including LR, DTR, SVR, and GPR were developed to predict machining responses. GPR provided the highest prediction accuracy, achieving 98.72% for cutting force and 98.99% for cutting temperature. The results demonstrate that manufacturing route and cooling strategy significantly affect the machinability of Inconel 718 and that machine learning techniques can effectively support machining process optimization.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2026Energy-Aware Adaptive Altitude Control of UAVs via Fuzzy–PSO Optimization Within a Port-Hamiltonian Framework Under Icing and Sensor Noise
Can, Erol
Unmanned Aerial Vehicles (UAVs) are increasingly deployed in complex, safety–critical missions where robust control under environmental uncertainties—such as in-flight icing and sensor noise—is essential. These disturbances degrade aerodynamic performance, flight stability, and tracking accuracy, challenging conventional control methods. This paper presents a novel control framework that synergistically integrates a fuzzy logic controller (FLC) with online adaptive tuning via Particle Swarm Optimization (PSO), embedded within a port-Hamiltonian (PH) modeling structure. The PH framework ensures physically consistent, energy-aware representation of UAV dynamics, enabling rigorous Lyapunov-based stability analysis and passivity guarantees. The key innovation lies in combining adaptive fuzzy control with an energy-based PH system model, which together enhance robustness against complex, nonlinear disturbances, such as icing and sensor noise, while simultaneously optimizing energy efficiency. Extensive simulations under realistic environmental and parametric uncertainties demonstrate significant improvements over traditional PID and standalone fuzzy controllers, including up to 85.7% reduction in altitude tracking error and 31.6% decrease in total energy consumption. Importantly, the approach maintains computational tractability by employing a simplified planar 3-DOF model focusing on translational and pitch dynamics—acknowledged as a main limitation and direction for future work. This integrated methodology advances the state-of-the-art by bridging adaptive intelligent control with rigorous physics-based modeling, providing a scalable and robust solution for next-generation autonomous UAVs operating safely in uncertain and adverse conditions.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2026A Deep Learning-Based Decision Support System for Structural Component Detection From Architectural Floor Plans
Karakose, Perihan; Ince, Elif Gokce; Eren, Erkin; Atar, Muhammed; Cakil, Burak; Ince, Ozan
Earthquake-resistant design of reinforced concrete (RC) buildings is initiated during the architectural design stage, where the placement and directions of vertical load-bearing elements are defined. The configuration of these elements plays a critical role in the seismic performance of RC structures. In this study, a decision-support system is proposed to automatically identify load-bearing elements from architectural floor plans and to assess torsional irregularity and shear wall distribution. A dataset comprising 600 architectural floor plans of existing buildings was compiled, in which structural components were manually annotated and used to train a YOLO (You Only Look Once)-based detection model. The developed system detects vertical load bearing elements in the floor plan and subsequently evaluates their arrangement using structural engineering criteria, including torsional irregularity and shear wall ratio. The results show that the proposed approach enables rapid and accurate evaluation of architectural floor plans, offering effective support during the early design phase.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2026Scenario-based optimization of humanitarian supply chain distribution channels for a possible earthquake: a case study in Türkiye
Balkar, Oğuz Emre; Akar, Alper; Akar, Özlem
Earthquakes represent a serious risk to societies, economies, and critical infrastructure, highlighting the need for effective disaster preparedness and resilient humanitarian supply chains (HSCs). In seismically active regions such as Erzincan Province, Türkiye, HSC distribution networks must remain functional despite significant disruptions. This study proposes a five-stage mixed-method framework to design and optimize HSC distribution channels for a potential major earthquake in the central district of Erzincan. First, buildings and transportation links were evaluated in terms of seismic suitability to pre-select candidate main distribution centers (MDCs), local distribution points (LDPs), and low-risk routes. Proximity analyses were then conducted using Quantum GIS (QGIS) to identify potential LDPs within a 1–5 km service radius for 69 neighborhoods. These locations were further validated and optimized using Greenfield analyses in anyLogistix under a distance–demand cost minimization criterion. Four distribution channel scenarios were subsequently simulated over a two-month post-earthquake period in line with national and international disaster standards. Finally, the Shortest Path Method was applied to assign primary, secondary, and tertiary LDPs to each neighborhood. The results indicate that supplying nine LDPs (comprising schools and healthcare centers) through four MDCs, including a newly designated sports facility, minimizes transportation cost, travel distance, and lead time, while enabling 63.8% of neighborhoods to access their primary LDP within 3 km. The proposed framework offers a holistic approach by explicitly incorporating seismic compliance into the design of HSC distribution channels in earthquake-prone regions.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2026Functional Lightweight Concrete Systems for Sustainable Load-Bearing Applications: Experimental Assessment of Perlite-Modified Reinforced Concrete Beams
Polat, Melih Sakir; Turan, Ahmet İhsan; Kucuk, Mesut; Kumbasaroglu, Atila; Yalciner, Hakan; Ayaz, Yasar
The increasing demand for sustainable structural materials in the construction industry has intensified the need to develop high-performance and environmentally efficient reinforced concrete systems. In this context, the incorporation of functional material-enhanced concrete systems, including raw lightweight aggregates such as perlite, offers a promising pathway for reducing environmental impact while maintaining adequate structural performance. This study experimentally investigates the flexural behavior of reinforced concrete (RC) beams incorporating raw perlite aggregates within a functional material-enhanced structural framework. A total of four RC beams with identical geometry and reinforcement details were produced, including perlite aggregate beams (P25 and P40) and conventional aggregate beams (C25 and C40), corresponding to compressive strength classes of 25 MPa and 40 MPa. All specimens were tested under monotonic flexural loading conditions. The structural response was evaluated in terms of load–displacement behavior, stiffness degradation, ductility, energy dissipation capacity, moment–curvature response, and failure modes. The results indicate that perlite aggregate beams exhibit reduced stiffness and energy dissipation capacity compared to conventional counterparts at comparable mixture-average compressive-strength levels. However, the high-strength perlite beam (P40) demonstrated comparable flexural capacity and ductility to the normal-strength conventional beam (C25), highlighting its potential for sustainable structural applications. Furthermore, a building-scale environmental assessment based on a representative three-story reinforced concrete structure demonstrates that the use of raw perlite aggregates significantly reduces energy consumption, CO2-equivalent emissions, and water usage. From a functional materials perspective, the findings provide a basis for the future development of multifunctional load-bearing concrete systems, where lightweight aggregate substrates can be combined with protective or performance-enhancing coating technologies to achieve improved durability, environmental resistance, and structural efficiency. This integration could further enhance durability and long-term structural performance in load-bearing applications.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2026A novel multi-objective optimization model for the time–cost–environmental impact trade-off in construction projects using a strength Pareto-based whale optimization algorithm
Yılmaz, Mehmet; Dede, Tayfun
The construction industry, a key contributor to global economic development, significantly impacts environmental pollution through greenhouse gas emissions. While conventional construction project management prioritizes cost and time reduction, environmental considerations are increasingly critical. This study addresses the time-cost-environmental impact trade-off problem by introducing a strength Pareto-based whale optimization algorithm (SP2WOA). The algorithm enhances solution diversity and achieves well-distributed Pareto fronts by integrating strength Pareto operators with the whale optimization algorithm. Its effectiveness is demonstrated through three diverse construction projects. Compared to previous algorithms (non-dominated sorting genetic algorithm-II, opposition-based multi-objective differential evolution, golden ratio-based opposite teaching-learning-based optimization and enhanced multi-objective grasshopper optimization algorithm), SP2WOA achieves superior balances between time, cost and environmental impact, using 33-60% fewer function evaluations. Statistical analysis using Wilcoxon rank tests confirms SP2WOA's superiority in both hypervolume and spread metrics. The results demonstrate that SP2WOA offers a valuable tool for sustainable construction project management.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2026Process-Based Control of Heat Input to Mitigate Anisotropy in CMT-Based Wire Arc Additive Manufacturing of AISI 316LSi Stainless Steel
Kara, Levent; Yılmaz, Oğuzhan; Korkmaz, Habip Gökay; Kaş, Mustafa; Bol, Nevzat; Toros, Serkan
Additive manufacturing (AM) technologies are prone to microstructural and mechanical anisotropy, driven primarily by directional solidification and the non-uniform thermal history associated with their layer-by-layer fabrication. This problem is more pronounced in Wire Arc Additive Manufacturing (WAAM), where elevated heat input and large molten pools increase thermal gradients and favor columnar grain development accompanied by δ-ferrite segregation in stainless steels. To address these issues, post-processing strategies such as inter-pass rolling, hammer peening, and heat treatment are frequently implemented. Nevertheless, regardless of being partially effective, these methods will add more processing steps that will result in an increase of time and costs. In this context, the present study seeks solutions to commonly reported limitations in the WAAM fabrication of AISI 316LSi stainless steel, which is widely used in industrial applications, by varying the wire feed speed (WFS) to regulate heat input with the aim of improving microstructural characteristics and mechanical performance. Accordingly 3 different heat input was attained by changing WFS in the system of CMT based WAAM method. Based on this approach, subsequent microstructural analysis revealed changes in δ-ferrite morphology from skeletal δ-ferrite to more refined lathy and globular forms across the investigated conditions. Within the examined processing parameters, samples corresponding to higher heat input levels, associated with increased wire feed speed (WFS), showed reduced anisotropy in grain orientation. Furthermore, samples produced under these conditions exhibited yield strength values of approximately 390 MPa, ultimate tensile strength values of approximately 620 MPa, and enhanced impact toughness, accompanied by a reduction in anisotropy. Overall, the findings indicate that adjustment of heat input during WAAM provides a viable process-engineering route for microstructural refinement and anisotropy reduction in AISI 316LSi stainless steel.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2026Prediction of cutting forces in turning Ti6Al4V in different cooling environments using a multiple signal data fusion approach
Akdulum, Aslan; Sİyambaş, Yusuf; Çakiroğlu, Ramazan; Uzun, Gültekİn
This study investigates the relationship between cutting forces and real-time energy consumption signals during the turning of titanium alloys, as well as the impact of various cooling methods on these forces. A hybrid approach combining energy signal analysis and forward regression modeling was applied to model the cutting forces. This work aims to model and predict cutting forces using individual and multiple signal data fusion approaches using data obtained from energy signals, such as reactive power, power factor, apparent power, and active power. A random forest regressor was preferred for model set-up, and the Scopt Optimizer was preferred for fine-tuning the hyperparameters. Six methods were used to score the features based on their importance level. The results showed that multi-energy signal data fusion outperforms the models based on single signals in predicting cutting forces. This method offers a new approach for optimizing metrics, such as the main cutting force (Fc) and feed force (Ff). The MAPE value of 4.91% obtained in the modeling estimation of the Ff emphasizes the power of the proposed method. The presented framework has the potential to increase processing performance while reducing energy consumption. The findings highlight the effectiveness of combining energy signals and machine learning in developing sustainable manufacturing processes. The proposed model can be adapted to different processing scenarios and provide a basis for industrial applications.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2026Seismic performance of RC buildings with one-way joist slab: Insights from the 2023 Kahramanmaraş earthquakes
İnce, Elif Gökçe
Two major earthquakes (Pazarcık Mw: 7.7 and Elbistan Mw: 7.6) occurred on the East Anatolian Fault Zone in Türkiye on February 6, 2023. These earthquakes caused severe destruction to structures. Türkiye's building stock mostly consists of multi-storey reinforced concrete (RC) buildings. Field investigations conducted after the Kahramanmaraş earthquakes once again revealed the deficiencies in the existing building stock. While the majority of these buildings are designed with a beam-slab system, a one-way joist slab system is frequently encountered. One way a joist slab system generally has wide and shallow beams with low stiffness. Additionally, the use of heavy non-structural materials in this slab type results in a greater building mass. In this study, damages in RC buildings with one-way joist slabs were investigated following the Kahramanmaraş earthquakes. In the design of RC buildings with one-way joist slabs, the cases of regular external beams and the use of shear walls were evaluated through numerical analysis. The analysis results show that shear walls and regular external beams significantly increase the performance of buildings with one-way joist systems. However, the use of seismic shear walls was found to be more effective in increasing horizontal rigidity.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2026Double Deep Q-Network-Based Solution for the Dynamic Electric Vehicle Routing Problem
Taş, Mehmet Bilge Han; Özkan, Kemal; Sarıçiçek, İnci; Yazıcı, Ahmet
The Dynamic Electric Vehicle Routing Problem (D-EVRP) presents a framework that requires electric vehicles to meet demand with limited energy capacity. When dynamic demand flows and charging requirements are considered together, traditional methods cannot provide sufficient adaptation for real-time decision-making. Therefore, a learning-based approach was chosen to ensure that decision-making processes respond quickly to changing conditions. The solution utilizes a model with a Double Deep Q-Network (DDQN) architecture and a discrete valuation structure. Prioritized Experience Replay (PER) was implemented to increase model stability, allowing infrequent but effective experiments to contribute more to the learning process. The state representation is constructed using the vehicle’s location, battery level, load status, and current customer demands. Scalability is ensured by dividing customer locations into clusters using the K-means method, with each cluster handled by an independent representative. The approach was tested with real-world road data obtained from the Meşelik Campus of Osmangazi University in Eskişehir. Experiments conducted under different demand levels and data sizes have shown that the PER-assisted DDQN structure produces more stable and shorter route lengths in dynamic scenarios, but random selection, greedy method and genetic algorithm experience significant performance losses as dynamicity increases.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2026Machine learning and traffic simulation for corridor-level optimization in Intelligent Transportation Systems: reducing travel times, delays, and carbon dioxide emissions
Deniz, Fatih Ahmet; Çolak, Muhammed Ali
Increased individual vehicle ownership has led to longer travel times, increased delays, and higher CO2 emissions in traffic networks. This study aims to optimize intersection designs, lane counts, and signal timings within an Intelligent Transportation System (ITS)-based traffic corridor to reduce travel time, minimize delays and decrease instantaneous CO2 emissions. To achieve this, the current conditions of a seven-intersection corridor were simulated, resulting in 27,647 different scenarios with various combinations of vehicle volumes, intersection properties, lane configurations, and signalization. Incorporating real intersection vehicle count data and signal timings further enhanced the accuracy of the simulations, providing a more realistic representation of traffic flow and helping to refine the optimization of intersection designs, lane counts, and signal settings. In total, 27,648 scenarios, including the baseline scenario, were analyzed, resulting in three datasets that focus on delay time, instantaneous CO2 emissions, and total travel time. These datasets were modeled using various algorithms, including linear regression, support vector regression, elastic net, decision trees, random forests, AdaBoost, gradient boosting, XGBoost, and CatBoost. Among these models, CatBoost was found to be the most effective in explaining delay time, instantaneous CO2 emissions, and total travel time. Feature importance analysis using CatBoost revealed the optimal designs for reducing travel time, minimizing delays, and lowering emissions: modern roundabouts at the Dortyol and Old Police School intersections, and a grade-separation at the Terzibaba intersection. The findings demonstrate that integrating machine learning with traffic simulation can support ITS by improving traffic flow, reducing congestion, and lowering CO2 emissions.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2026Multi-objective time-cost-safety risk trade-off optimization for the construction scheduling problem
Yılmaz, Mehmet; Dede, Tayfun
Purpose – The purpose of this study is to enable the planning of construction projects with simultaneous consideration of time, cost and safety risks. It also aims to improve the decision-making process by evaluating the effectiveness of the Rao-2 algorithm in solving multi-objective time-cost-safety risk problems. In the end, this model is designed to support project managers in enhancing management approaches by addressing project challenges and constraints more efficiently. Design/methodology/approach – In this study, the Rao-2 algorithm, along with Grey Wolf Optimization (GWO) and Whale Optimization algorithm (WOA), were improved using the crowding distance-based non-dominated sorting method. Rao-2 was first compared to GWO and WOA. Subsequently, it was compared with well-established algorithms in the literature, including genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE). The C-metric, hypervolume and spread metrics were employed for performance measurement. The performance of the algorithms was evaluated on four case studies consisting of 11, 13, 18 and 25 activities. Findings – The results revealed that Rao-2 performs better than other algorithms as the number of activities increases, when compared using the Hypervolume, Spread and C-metric measures. In terms of performance measures, the GWO algorithm outperformed Rao-2 in some evaluation metrics for the instance involving 11 activities. However, as the number of activities grew, the Rao-2 method consistently generated higher-quality Pareto fronts and outperformed GWO and WOA in all evaluation metrics. The solutions generated by Rao-2 were also superior to those obtained from GA, PSO and DE in all case studies, further demonstrating the capability of our framework to produce a wide range of optimal solutions with high diversity across different case studies. Originality/value – This research demonstrates that Rao-2 not only improves solution quality when generating Pareto fronts but also achieves better results with fewer function evaluations compared to GA, PSO and DE. The algorithm's efficiency makes it particularly well-suited for optimizing time, cost and safety risks in large-scale construction projects, which in turn positions Rao-2 as a better choice for such projects by producing superior results compared to other algorithms. By providing high-quality solutions with reduced computational demands, Rao-2 offers a faster and more resource-efficient tool for decision-making, contributing to advancements in both the theory and practice of construction project management.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2026Machine Learning-Based Performance Analysis of Solar Thermal Storage Tanks with Fin-Configured Phase Change Materials
Çolak, Andaç Batur; Kılınç, Cuma
Solar thermal energy storage systems play a vital role in bridging the gap between intermittent solar availability and continuous energy demand; however, their efficiency is severely constrained by the inherently low thermal conductivity of phase change materials. Integrating physical heat transfer enhancements, such as radial fins, offers a practical solution, but evaluating these non-linear thermal dynamics across diverse design configurations typically incurs heavy computational costs. To address this challenge, this research investigates an artificial intelligence-based predictive framework capable of accurately modeling complex phase change dynamics in fin-configured storage tanks. Utilizing high-fidelity 2D Computational Fluid Dynamics simulation data of a stainless-steel double-tube storage tank filled with RT-50 paraffin wax across 10, 20, and 29 fin configurations, a Multi-Layer Perceptron Artificial Neural Network trained with the Bayesian Regularization algorithm was developed. The model predicts liquid fraction, latent heat distribution, and buoyancy-driven natural convection (Reynolds number) based on fin count and time. The optimal architecture, featuring 30 hidden neurons, achieved exceptional predictive precision, yielding a coefficient of determination of 0.99999, along with individual Mean Squared Error values of 1.29 × 10−3 for liquid fraction, 5.73 × 10−1 for latent heat distribution, and 2.64 × 10−5 for Reynolds number, with average prediction deviation rates consistently below 0.5%. These results demonstrate that high-precision surrogate modeling can effectively replace computationally intensive numerical simulations, offering significant practical implications for the real-time thermal monitoring, rapid design optimization, and intelligent control of advanced solar energy storage technologies.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2026Comparative study of diverging and converging coaxial jet nozzles for thermal management of H-CPV modules
Demir, Fatih; Kalkan, Orhan
Jet impingement cooling is widely recognized as one of the most effective methods for achieving high heat transfer rates in compact systems, making it particularly suitable for cooling photovoltaic devices. This paper investigates the effects of newly designed coaxial nozzle configurations on the thermal management of a high-concentration photovoltaic system. Four different nozzle geometries, such as square, circular, hexagonal, and triangular, are considered, with constant outer cross-sectional areas, while the inner sections are designed to diverge or converge. Simulations are performed for Reynolds numbers of 2000–4000 and dimensionless coaxial nozzle-heat sink distances of 0.5–3. The results reveal that the coaxial nozzle enhances heat transfer performance compared to conventional nozzles. The diverged triangular coaxial nozzle achieves the best heat transfer at a Reynolds number of 4000, with a 19 % increase in the modified Nusselt number compared to the conventional triangular nozzle. The highest temperature uniformity (6.39 °C) is observed in the converged hexagonal coaxial nozzle, while the lowest maximum and average cell temperatures (46.1 °C and 44 °C) are achieved with the diverged triangular coaxial nozzle. The lowest pressure drop (1297 Pa) is found in a diverged hexagonal coaxial nozzle, corresponding to the lowest net electrical power (17.53 W) and cell efficiency (38.97 %). Despite the pressure drops, the diverged triangular coaxial nozzle achieves the highest electrical efficiency of 39.41 %, as its geometry promotes favorable power generation. The coaxial configuration increases the cell efficiency by up to 1.13 % and improves temperature uniformity by 23.3 %. Total exergy follows the same trend as electrical exergy, with the converged hexagonal coaxial nozzle exhibiting the highest total exergy efficiency of 38.35 %. The findings demonstrate that coaxial nozzles can significantly improve thermal uniformity and system efficiency in high-concentration photovoltaic cooling applications.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2026Experimental evaluation of reinforced concrete columns produced with natural perlite aggregates under cyclic loading
Atlı, Sefa; Yalciner, Hakan; Kumbasaroglu, Atila; Ayaz, Yaşar; Turan, Ahmet İhsan; Bulut, H. Alperen; Naimi, Sepanta
A total of four reinforced concrete (RC) columns were tested to compare the structural capacities of RC columns produced by natural perlite aggregates and conventional concrete. The structural performance of the RC columns made from natural perlite aggregates was compared to conventional RC columns regarding the same concrete strength levels, sectional properties, and reinforcement layouts of conventional RC columns. The RC columns were tested under a combined constant axial load ratio of 30% with cyclic loading for two concrete strength levels, namely, 25 and 40 MPa. Load–displacement curves, energy absorption capacities, stiffness degradation, the moment-curvature relationship, the plastic hinge mechanism, and the ductility index were experimentally obtained at two different concrete types along with the measured stress–strain properties of the conventional and perlite concrete. The test results showed that the construction of the RC columns using only perlite may become possible in the future by providing an adequate concrete stress–strain capacity, which will be improved. Additionally, the use of natural perlite aggregates can contribute to sustainable construction practices by reducing the reliance on conventional aggregates.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2026Development and analysis of machine learning-based hybrid optimization framework for cutting temperature-based energy consumption estimation in turning of titanium alloys
Akdulum, Aslan; Siyambaş, Yusuf; Çakıroğlu, Ramazan; Uzun, Gültekin
This study develops a machine learning-based hybrid optimization framework to predict energy consumption from cutting temperature in turning of Ti6Al4V alloy under different cooling environments. Due to the poor thermal conductivity of titanium alloys, heat accumulation in the cutting zone increases energy consumption and reduces process efficiency. Cutting temperatures were recorded in real time using a thermal camera, and energy consumption was measured with a power analyzer. Statistical features extracted from temperature data served as inputs for energy prediction. A hybrid optimization framework was developed by integrating feature selection methods, machine learning algorithms, and hyperparameter optimizers. Among six algorithms evaluated, CatBoost combined with SHAP-based feature selection and Optuna hyperparameter optimization achieved the best performance. The proposed hybrid model (CAT+SHAP+Optuna) provides a 91% improvement in Mean Squared Error compared to the benchmark model, demonstrating high accuracy in energy consumption estimation. This approach offers significant potential for enhancing energy efficiency and promoting sustainable manufacturing in the machining of difficult-to-cut materials.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2026Waste Management Strategies for Environmental Wellbeing: Exploring the Roles of Environmental Regulations and Green Finance in Reducing Carbon Intensity
Caglar, Abdullah Emre; Destek, Mehmet Akif; Demirdag, Ismail; Manga, Muge; Aydın, Sercan
Rising industrial activity, urban expansion, and economic growth continue to intensify waste generation and greenhouse gas emissions, placing growing pressure on environmental sustainability. In this setting, reducing the carbon intensity of electricity generation has become a key policy priority, particularly in advanced economies pursuing long-term decarbonization. Grounded in Ecological Modernization Theory and the Porter Hypothesis, this study examines the effects of waste management, environmental regulations, green finance, and economic growth on the carbon intensity of electricity generation in the G-6 economies, namely France, Germany, Italy, Japan, the United Kingdom, and the United States. This article contributes to the literature in two ways. First, the paper ensures a focused assessment of the direct roles of waste management, environmental regulations, and green finance in shaping carbon intensity in advanced economies. Second, it investigates whether the effectiveness of green finance depends on the stringency of environmental regulation. Utilizing panel data of the 2000–2020 period, drawn from Our World in Data, the World Development Indicators, the OECD, and the IEA, the analysis employs the CS-ARDL and AMG estimators to address issues of cross-sectional dependence and heterogeneity. The findings show that waste management, environmental regulations, and economic growth reduce carbon intensity in the long run, whereas green finance on its own does not have a statistically significant effect. However, green finance becomes effective when combined with stricter environmental regulations. These findings underscore the importance of coordinated policy design and offer practical insights to advance progress toward SDGs 11, 12, and 13.
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