Filters

2017 - 201912020 - 20262
Reset filters

Settings

Publication Index: ScopusSubject: Artificial Neural Networks

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Article
    Sustainable Stabilization of Expansive Soils Using Waste Marble Powder and Expanded Polystyrene Beads: Experimental Evaluation and Predictive Modelling
    (Elsevier, 2026) Akis, Ebru; Citak, Mete; Lotfi, Bahram
    Expansive soils exhibit considerable volume changes with moisture fluctuations leading to serious challenges for civil infrastructure, causing structural instability, pavement distortion, and foundation damage. While lime and cement remain widely used stabilizers, recent research has increasingly focused on waste-derived materials such as marble powder (MP) and expanded polystyrene beads (EPSb) as promising alternatives. These materials provide a practical approach to soil stabilization while contributing to the reuse of industrial by-products. In this study, the engineering behavior of high-plasticity clay was improved through the inclusion of MP and EPSb as additive materials. MP was added at 0%, 5%, 10%, 15%, and 20%, and EPSb at 0%, 0.3%, and 0.9% by dry weight of the high plasticity clay. Both additives were used alone and in combination. Laboratory tests, including Standard Proctor, free swell (FS), and unconfined compressive strength (UCS), were conducted. The results confirmed that the additives effectively reduced the liquid limit (LL) by 20.1% and the plasticity index (PI) by up to 22.4%. Results showed that EPSb effectively reduced FS and UCS, while MP decreased FS and increased UCS up to an optimal content. The most effective mixes achieved a maximum reduction of 54.7% in free swell (FS) (at 20% MP and 0.9% EPSb content) and a maximum increase of 13.1% in unconfined compressive strength (UCS) (at 5% MP content) compared to the untreated soil. The compaction tests further revealed a general decrease in optimum moisture content (OMC) and a slight increase in maximum dry density (MDD) with increasing MP content. Accordingly, the free swell (FS) and unconfined compressive strength (UCS) of the treated soils were predicted using multiple linear regression (MLR) and artificial neural network (ANN) models, developed from both the current experimental dataset and previously published studies. Input variables included untreated FS and UCS values, additive percentages, and one index property. The ANN model demonstrated superior predictive capability, achieving R2 values of 0.955 and 0.874 for FS and UCS, respectively, compared to 0.411 and 0.618 obtained with MLR. These results highlight the robustness of ANN in capturing nonlinear soil behavior and underscore its reliability and accuracy, particularly under limited data conditions.
  • Thumbnail Image
    Article
    Citation - WoS: 2
    Citation - Scopus: 4
    Exploiting Visual Features in Financial Time Series Prediction
    (Igi Global, 2020) Karacor, Adil Gursel; Erkan, Turan Erman
    The possibility to enhance prediction accuracy for foreign exchange rates was investigated in two ways: first applying an outside the box approach to modeling price graphs by exploiting their visual properties, and secondly employing the most efficient methods to detect patterns to classify the direction of movement. The approach that exploits the visual properties of price graphs which make use of density regions along with high and low values describing the shape; hence, the authors propose the name 'Finance Vision.' The data used in the predictive model consists of 1-hour past price values of 4 different currency pairs, between 2003 and 2016. Prediction performances of state-of-the-art methods; Extreme Gradient Boosting, Artificial Neural Network and Support Vector Machines are compared over the same data with the same sets of features. Results show that density based visual features contribute considerably to prediction performance.
  • Thumbnail Image
    Conference Object
    Artificial Intelligence Techniques for Performance Simulation: Solid Oxide Fuel Cells V-J Curve Prediction Via Artificial Neural Networks
    (ENEA, 2017) Baldinelli,A.; Barelli,L.; Bidini,G.; Bonucci,F.; Iskenderoğlu,F.C.
    For their high flexibility of operation, Solid oxide fuel cells (SOFCs) are promising candidates to coach the transition towards cleaner and efficient energy generation. Yet, SOFC performance might be markedly affected by fuel composition variability and operative parameters. For that, a reliable simulation tool is necessary for SOFC performance, to optimize its working point and to provide a suitable control. Given the high variability ascribed to the fuel and the electrochemical system high nonlinearity, the implementation of artificial intelligence techniques, like Artificial Neural Networks (ANNs), is worth considering. In ANNs, the correlation between system inputs and outputs is handled by virtual neurons, establishing in-out correlations without entering in knotty kinetics and material properties issues. For what above, a suitably sized experimental campaign is to be designed to provide a large data-set. This to guarantee high ANN performance in the voltage estimation and, at the same time, a wide application domain of the neural simulator. © EFC 2017 - Proceedings of the 7th European Fuel Cell Piero Lunghi Conference.