Sürdürülebilir Kalkınma Amaçları

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Yazarlar

• Akpolat, Hacer(1)
• Akpolat, Mehmet Z.(1)
• Barineau, Mark(1)
• Chen, Yu-Ju(1)
• Dede, Tayfun(1)
• Demirtaş, Betül(1)
• Francis, David M.(1)
• Jackson, Keith A.(1)
• Rodriguez-Saona, Luis E.(1)
• Türker, Temel(1)
• Yanik, Yusuf(1)

• MakaleYalnız künye2026Scopus SJRQ2/r/bayburt/11398
  Frequency-Based Damage Detection with ANN and Seismic Behaviour Assessment of a Historical Masonry Tower

  Demirtaş, Betül; Türker, Temel; Yanik, Yusuf; Dede, Tayfun

  Nowadays, Artificial Neural Networks (ANNs) are widely used in damage detection because of their powerful computational and exceptional ability to recognize patterns in historical structures. This paper presents the seismic behaviour assessment and structural damage detection performance of the ANN trained with natural frequencies to identify the damage on the Hagia Sophia Bell Tower in Trabzon, Türkiye. Reduction in the stiffness of the structures causes changes in the structural dynamics. Natural frequencies, being key indicators in damage detection, were utilized as input parameters to train the ANN in this study. The study’s scope included updating of the initial finite element (FE) model constituted with SAP2000 software according to the experimental dynamic characteristics obtained from the Civil Engineering Vibration System developed by our work team. The seismic behaviour of the tower was also examined through time history analyses under the effect of strong earthquakes. The required data for the ANN analyses was obtained from the FE modal analysis of the tower. The ANNs dataset was generated separately by simulating both single and double damage scenarios. Possible twelve damage zones were determined according to the places where the highest stresses occurred in the earthquake analysis of the tower. The reduction in the elasticity modulus of each region defined on the tower was considered as damaging effect and used as ANN output parameter. It was shown that ANN trained with natural frequencies can effectively identify the location and severity of damage.

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  AtıfScopus6·OpenAlex7

  İncele
• MakaleAçık Erişim2020Scopus SJRQ2/r/bayburt/12033
  High-throughput phenotyping approach for screening major carotenoids of tomato by handheld raman spectroscopy using chemometric methods

  Akpolat, Hacer; Barineau, Mark; Jackson, Keith A.; Akpolat, Mehmet Z.; Francis, David M.; Chen, Yu-Ju; Rodriguez-Saona, Luis E.

  Our objective was to develop a rapid technique for the non-invasive profiling and quantification of major tomato carotenoids using handheld Raman spectroscopy combined with pattern recognition techniques. A total of 106 samples with varying carotenoid profiles were provided by the Ohio State University Tomato Breeding and Genetics program and Lipman Family Farms (Naples, FL, USA). Non-destructive measurement from the surface of tomatoes was performed by a handheld Raman spectrometer equipped with a 1064 nm excitation laser, and data analysis was performed using soft independent modelling of class analogy (SIMCA)), artificial neural network (ANN), and partial least squares regression (PLSR) for classification and quantification purposes. High-performance liquid chromatography (HPLC) and UV/visible spectrophotometry were used for profiling and quantification of major carotenoids. Seven groups were identified based on their carotenoid profile, and supervised classification by SIMCA and ANN clustered samples with 93% and 100% accuracy based on a validation test data, respectively. All-trans-lycopene and β-carotene levels were measured with a UV-visible spectrophotometer, and prediction models were developed using PLSR and ANN. Regression models developed with Raman spectra provided excellent prediction performance by ANN (rpre = 0.9, SEP = 1.1 mg/100 g) and PLSR (rpre = 0.87, SEP = 2.4 mg/100 g) for non-invasive determination of all-trans-lycopene in fruits. Although the number of samples were limited for β-carotene quantification, PLSR modeling showed promising results (rcv = 0.99, SECV = 0.28 mg/100 g). Non-destructive evaluation of tomato carotenoids can be useful for tomato breeders as a simple and rapid tool for developing new varieties with novel profiles and for separating orange varieties with distinct carotenoids (high in β-carotene and high in cis-lycopene).

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  AtıfScopus40·WoS32·OpenAlex48

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