Yönetim Bilişim Sistemleri Bölümü

Permanent URI for this communityhttps://hdl.handle.net/20.500.12416/6194

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Citation - WoS: 13
Citation - Scopus: 12
Deep Learning Method for Compressive Strength Prediction for Lightweight Concrete
(Techno-press, 2023) Nanehkaran, Yaser A.; Azarafza, Mohammad; Pusatli, Tolga; Bonab, Masoud Hajialilue; Irani, Arash Esmatkhah; Kouhdarag, Mehdi; Derakhshani, Reza; 51704
Concrete is the most widely used building material, with various types including high-and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code's requirement for specific characteristics. To overcome this issue, researchers have turned to new technologies like machine learning to develop proper methodologies for concrete specification. In this study, we propose a highly accurate deep learning-based predictive model to investigate the compressive strength (UCS) of lightweight concrete with natural aggregates (pumice). Our model was implemented on a database containing 249 experimental records and revealed that water, cement, water-cement ratio, fine-coarse aggregate, aggregate substitution rate, fine aggregate replacement, and superplasticizer are the most influential covariates on UCS. To validate our model, we trained and tested it on random subsets of the database, and its performance was evaluated using a confusion matrix and receiver operating characteristic (ROC) overall accuracy. The proposed model was compared with widely known machine learning methods such as MLP, SVM, and DT classifiers to assess its capability. In addition, the model was tested on 25 laboratory UCS tests to evaluate its predictability. Our findings showed that the proposed model achieved the highest accuracy (accuracy=0.97, precision=0.97) and the lowest error rate with a high learning rate (R2=0.914), as confirmed by ROC (AUC=0.971), which is higher than other classifiers. Therefore, the proposed method demonstrates a high level of performance and capability for UCS predictions.
Citation - WoS: 2
Citation - Scopus: 3
Economic Sentiment and Foreign Portfolio Flows: Evidence From Türkiye
(Central Bank Republic Turkey, 2024) Ozkan, Ibrahim; Erden, Lutfi; Gunes, Didem; 169580
The notable surge in capital flows in recent years has emerged as a key factor shaping the dynamics of international financial markets and influencing economic performance of emerging economies. Even though macroeconomic fundamentals of an economy can explain some of the patterns in international capital flows, behavioral factors also seem to be essential for positioning capital flows across countries. In this study, we aim to examine whether overall economic sentiment towards Turkish economy plays a significant role on net portfolio flows to Turkiye. To this end, we first construct a novel text-based sentiment index called "Turkish Economic Sentiment Index (TESI)", to capture the behavioral tendencies of international investors and media towards Turkiye. Our subsequent step integrates TESI into autoregressive distributed lag models (ARDL) alongside major pull-push determinants to assess whether market sentiment holds discernible influence on capital influx into Turkey. The results reveal that the TESI and VIX stand out as pivotal determinants influencing international portfolio flows. The TESI has a positive impact on portfolio flow dynamics, whereas the degree of global risk aversion inversely affects these flows. These findings align with the contention that a favorable sentiment can boost portfolio inflows to emerging markets. Conversely, heightened volatility expectations in global markets can prompt outflows from these economies.

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