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RISS 인기검색어
- 검색키워드
- 저자 : Francisco F. Martins (검색결과 3 건)
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Prediction of the mechanical properties of granites under tension using DM techniques
Martins, Francisco F.,Vasconcelos, Graca,Miranda, Tiago Techno-Press 2018 Geomechanics & engineering Vol.15 No.1
The estimation of the strength and other mechanical parameters characterizing the tensile behavior of granites can play an important role in civil engineering tasks such as design, construction, rehabilitation and repair of existing structures. The purpose of this paper is to apply data mining techniques, such as multiple regression (MR), artificial neural networks (ANN) and support vector machines (SVM) to estimate the mechanical properties of granites. In a first phase, the mechanical parameters defining the complete tensile behavior are estimated based on the tensile strength. In a second phase, the estimation of the mechanical properties is carried out from different combination of the physical properties (ultrasonic pulse velocity, porosity and density). It was observed that the estimation of the mechanical properties can be optimized by combining different physical properties. Besides, it was seen that artificial neural networks and support vector machines performed better than multiple regression model.
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Compressive strength prediction of CFRP confined concrete using data mining techniques
Aires Camões,Francisco F. Martins 사단법인 한국계산역학회 2017 Computers and Concrete, An International Journal Vol.19 No.3
During the last two decades, CFRP have been extensively used for repair and rehabilitation of existing structures as well as in new construction applications. For rehabilitation purposes CFRP are currently used to increase the load and the energy absorption capacities and also the shear strength of concrete columns. Thus, the effect of CFRP confinement on the strength and deformation capacity of concrete columns has been extensively studied. However, the majority of such studies consider empirical relationships based on correlation analysis due to the fact that until today there is no general law describing such a hugely complex phenomenon. Moreover, these studies have been focused on the performance of circular cross section columns and the data available for square or rectangular cross sections are still scarce. Therefore, the existing relationships may not be sufficiently accurate to provide satisfactory results. That is why intelligent models with the ability to learn from examples can and must be tested, trying to evaluate their accuracy for composite compressive strength prediction. In this study the forecasting of wrapped CFRP confined concrete strength was carried out using different Data Mining techniques to predict CFRP confined concrete compressive strength taking into account the specimens’ cross section: circular or rectangular. Based on the results obtained, CFRP confined concrete compressive strength can be accurately predicted for circular cross sections using SVM with five and six input parameters without spending too much time. The results for rectangular sections were not as good as those obtained for circular sections. It seems that the prediction can only be obtained with reasonable accuracy for certain values of the lateral confinement coefficient due to less efficiency of lateral confinement for rectangular cross sections.
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Heriberto Granados‑Becerra,Víctor H. López‑Morelos,Alberto Ruiz,Rafael García‑Hernández,Francisco F. Curiel‑López,Martin R. Barajas‑Alvarez 대한금속·재료학회 2022 METALS AND MATERIALS International Vol.28 No.6
The Ramberg–Osgood equation was applied to model the stress–strain behavior of heat-treated and welded specimens ofInconel 600 up to 0.2 and 0.175 mm/mm of strain, respectively. As-received and aged plates of Inconel 600 were gas metalarc welded to simulate repair of a component in operation. Dog bone shape specimens were tensile tested at a cross headspeed of 0.005 s−1 and the deformation was measured with an extensometer with a gage length of 20 mm. Microstructuralobservations were made in the optical and scanning electron microscope. Fitting the experimental data in the plastic regionof stress–strain curves to a novel three slopes Ramberg–Osgood model enabled estimation of the hardening coefficient, n. The value of n varied from 0.07 to 0.3 depending on the metallurgical condition of the specimen. A notorious increase wasobserved in the value of n for samples heat-treated beyond 25 h and in as-welded samples. The results of this work showeda very good correlation between the model and the experimental curves.
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