Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks

Dehghan, Somaiyeh; Sattari, Gh.; Chelgani, S. Chehreh; Aliabadi, M

doi:10.1016/s1674-5264(09)60158-7

Cited by 110 publications

(71 citation statements)

References 26 publications

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“…Some of these researchers defined the basic engineering properties of travertines, limestones, and marbles and found statistical relationships between physical and mechanical properties of these rock types (Yaşar and Erdoğan, 2004;Yagiz, 2009). Dehghan et al (2010) carried out a series of tests to predict uniaxial compressive strength and modulus of elasticity of travertines using statistical analysis and artificial neural networks. Erdoğan (2011) studied the engineering properties of travertines in Turkey and found these properties to be compatible and useful for flooring, cladding, and construction purposes.…”

Section: Introductionmentioning

confidence: 99%

The effects of bedding directions on abrasion resistance in travertine rocks

Gökçe¹

2015

Turkish J Earth Sci

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IntroductionTravertines are widely used across the globe in building materials since they are found abundantly and have distinct decorative characteristics. They have had a broad range of use in architectural applications throughout history, including in government buildings, healthcare facilities, hotels, and restaurants. The Magna Graecia region (e.g., the Temple of Hera and the Temple of Segesta, Italy), Anatolia (e.g., Hierapolis, Turkey), and Rome are notable locations where remains of ancient travertinebuilt buildings can be found (Pedley, 2009). The architects of the Roman Empire used travertine in a number of celebrated buildings throughout its territory, especially in the city of Rome, such as the Temple of Concord, the Coliseum, the Ponte Sant' Angelo, and the Castel Sant' Angelo (Jackson et al., 2005;Pentecost, 2005). Local red travertine was used to build Spain's Roman Theater of Carthago Nova (Soler Huertas, 2005), while Hungary's aqueducts and amphitheaters in Budapest (Török, 2006a(Török, , 2006b are also some historic travertine-made buildings. The 17th century colonnades in St. Peter's Square in the Vatican, the 18th century Trevi Fountain in Rome, the 19th century Parliament Building in Budapest, and the 20th century monument to Victor Emmanuel II in Rome are some more recent examples of the architectural use of travertine in buildings. In the 20th and 21st centuries, Roman travertine was exported to the United States and used in the J. Paul Getty Museum Building in Los Angeles and the Lincoln Center in New York (Sidraba et al., 2004). Today, Turkey is one of the major suppliers of travertine to the United States and other countries.Travertines are hot-spring-sourced carbonate deposits characterized by high porosity, different colors, and a fine-grained banded structure. They are generally found in fault zones, in karstic caves, and around spring cones, being deposited by oozing or spring waters containing Ca 2+ and CO 3 2- (Pentecost, 2005). The use of travertine is closely related to its chemical, morphological, and industrial characteristics (Ayaz, 2002). The morphological characteristics are crucial for touristic purposes while the chemical and mechanical characteristics are important for industrial use (Wilson, 1979;Chafetz and Folk, 1984). Industrially, travertines can be used as building materials, especially for cladding and flooring purposes, as Abstract: Travertine rocks are currently used in various architectural applications, cut perpendicular or parallel to bedding planes. Travertine stones used without enough attention paid to the cutting directions easily wear off in locations where pedestrian traffic is heavy and can cause some unexpected complications in practice. This study was conducted to elucidate the effects of cutting directions on the abrasion resistance of travertine stones. Travertine blocks were cut perpendicular and parallel to bedding planes, and their abrasion resistances were determined by means of the Böhme abrasion loss test. Travertine blocks were chosen from 6 ...

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Section: Introductionmentioning

confidence: 99%

The effects of bedding directions on abrasion resistance in travertine rocks

Gökçe¹

2015

Turkish J Earth Sci

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“…In particular, for semistructural or non-structural problems, neural network models can provide very successful results. Furthermore, they are faster and more reliable than the traditional methods are [23].…”

Section: Arti Cial Neural Network (Ann)mentioning

confidence: 99%

“…This system is able to learn and generalize experimental data, even when the data are noisy, incomplete with a non-linear nature [23,24]. Unlike the conventional statistical models, the main advantage of ANN is that it does not require any prior knowledge related to the kind of relationship between input and output data.…”

Section: Introductionmentioning

confidence: 99%

Predicting Shear Wave Velocity of Soil Using Multiple Linear Regression Analysis and Artificial Neural Networks

et al. 2017

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Abstract. This paper investigates the correlation between shear wave velocity and some of the index parameters of soils, including Standard Penetration Test blow counts (SPT), FineContent (FC), soil moisture (W ), Liquid Limit (LL), and Depth (D). The study attempts to show the application of arti cial neural networks and multiple regression analysis to the prediction of the shear wave velocity (V S ) value of soils. New prediction equations are suggested to correlate VS with the mentioned parameters based on a dataset collected from Mashhad city in the north east of Iran. The results suggest that, in the case of ANN method use, highly accurate correlations in the estimation of VS are acquired. The predicted values using ANN method are checked against the real values of V S to evaluate the performance of this method. The minimum correlation coe cient obtained in ANN method is higher than the maximum correlation coe cient obtained from the MLR. In addition, the value of estimation error in the ANN method is much less than that in the MLR method, indicating the role of higher con dence coe cient of the ANN in estimating VS of soil.

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“…The magnitude of the range of data sets is significantly different for each input as well as across the inputs. This network training can be made more efficient by certain pre-processing steps [Dehghan et al, 2010].…”

Section: Es4004mentioning

confidence: 99%

Estimation of in-situ horizontal stresses using the linear poroelastic model and minifrac test results in tectonically active area

Hayavi¹,

Abdideh²

2016

Russ. J. Earth Sci.

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Accurate estimation of in situ stresses of a subsurface formation is important to get a basic knowledge of formation structure and position of anomalies, groundwater flows, performing fracturing operations, drilling operations, oil and/or gas production stimulation, wellbore stability analysis, and coupled geomechanics-reservoir simulation in petroleum engineering. In this paper, at first a new method for estimation of minimum and maximum horizontal stresses in tectonically active area based on the modification of linear poroelastic model and minifrac test results is presented. The rock mechanical properties used in poroelastic model are determinded using the artificial neural networks. Then, this method is applied to field data in order to verify the applicability of the modified linear poroelastic model. The results indicated that the agreement between the results of minifrac test and modified linear poroelastic model is satisfactory. Furthermore, application of artificial neural networks in this methodology increases the accuracy of linear poroelastic model for estimation of horizontal stresses. KEYWORDS: Minifrac tests; linear poroelastic model; horizontal stress; tectonically active area.Citation: Hayavi, Mohammad Tabaeh and Mohammad Abdideh (2016), Estimation of in-situ horizontal stresses using the linear poroelastic model and minifrac test results in tectonically active area, Russ.

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Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks

Cited by 110 publications

References 26 publications

The effects of bedding directions on abrasion resistance in travertine rocks

The effects of bedding directions on abrasion resistance in travertine rocks

Predicting Shear Wave Velocity of Soil Using Multiple Linear Regression Analysis and Artificial Neural Networks

Estimation of in-situ horizontal stresses using the linear poroelastic model and minifrac test results in tectonically active area

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