Spatial estimation model of porosity

Tutmez, Bulent; Hatipoglu, Z.

doi:10.1016/j.cageo.2006.07.008

Cited by 16 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Geostatistical methods are widely used in mining, geology, soil science, environmental science, hydrology, meteorology and recently in engineering sciences for remote sensing, surface texture modelling, reliability and risk analysis, service life design and data assessment of non-destructive testing results (Balaguer et al 2010;Goovaerts 1999;Li et al 2014;Mlynarczuk 2010;Stewart, Mullard 2007;Tutmez, Hatipoglu 2007;Webster, Oliver 2007). The spatial variability analysis is well-described in the geostatistical literature for a long time; the geostatistical literature is very rich and several well known textbooks are widely available.…”

Section: Role Of Geostatistical Methods In Structural Diagnosticsmentioning

confidence: 99%

NDT Assessment of Existing Concrete Structures: Spatial Analysis of Rebound Hammer Results Recorded in-Situ

Borosnyói

2015

Engineering Structures and Technologies

View full text Add to dashboard Cite

A comparative spatial analysis of surface hardness of structural concrete is introduced. Main objective of the paper is to make a repeatability comparison of three types of the still most popular non-destructive testing devices for concrete: L-type original Schmidt rebound hammer, N-type original Schmidt rebound hammer and N-type Silver Schmidt rebound hammer. Results indicate that the surface hardness measurement uncertainty is related to the weight of the hammer mass and is apparently not related to the impact energy of the rebound hammer devices. It is observed that the measure of surface hardness for the Silver Schmidt rebound hammer (Q-value) does not have positive correlation to the original rebound index (R). Results indicate the best performance of the N-type original Schmidt rebound hammer in terms of stability and normality of data. Geostatistical analysis of the measured data (in terms of empirical semivariograms) highlights different statistical behaviour for the mechanical recording rebound hammers and for the electro-optical recording rebound hammer.

show abstract

Section: Role Of Geostatistical Methods In Structural Diagnosticsmentioning

confidence: 99%

NDT Assessment of Existing Concrete Structures: Spatial Analysis of Rebound Hammer Results Recorded in-Situ

Borosnyói

2015

Engineering Structures and Technologies

View full text Add to dashboard Cite

show abstract

“…Mersin contains agricultural, industrial, and settlement regions, and groundwater is used as a main source of water in this region [20]. In addition, the area needs an efficient groundwater management, which requires the understanding of groundwater storage capacity [1].…”

Section: Area Description and Data Setmentioning

confidence: 99%

“…Aquifer porosity is defined as the ratio of the non-solid volume to the total volume of the material, and its value ranges between 0 and 1. It can also be represented in percent terms by multiplying the fraction by 100% [1]. In order to determine the aquifer porosity, many tools can be used.…”

Section: Introductionmentioning

confidence: 99%

Assessment of porosity using spatial correlation-based radial basis function and neuro-fuzzy inference system

Tutmez

2010

Neural Comput & Applic

View full text Add to dashboard Cite

Aquifer porosity indicates the storage groundwater capacity and groundwater quality. It may be measured via different techniques. This paper presents a novel spatial methodology based on radial basis function (RBF) and neuro-fuzzy inference system for modelling the porosity. Use of the point cumulative semimadogram in RBF as a spatial measure is a novel contribution. In addition, the methodology examines the use of a neural network-based fuzzy inference system for porosity estimation. Performance comparisons with conventional methods show that the proposed spatial model has high modelling and generalization capability.

show abstract

“…In recent years, fuzzy inference systems have become more popular in the areas of rock mechanics and engineering geology (Alvarez Grima & Babuska, 1999;Finol et al, 2001;Gokceoglu, 2002;Nefeslioglu et al, 2003Nefeslioglu et al, , 2006Sonmez et al, 2003;Gokceoglu & Zorlu, 2004;Tutmez & Hatipoglu, 2007). In this study, the Mamdani fuzzy inference system ( Figure 6) was employed to construct a prediction model for the uniaxial compressive strength.…”

Section: Fuzzy Inference Systemmentioning

confidence: 99%

Estimating the uniaxial compressive strength of some clay‐bearing rocks selected from Turkey by nonlinear multivariable regression and rule‐based fuzzy models

Gökçeoğlu¹,

Sönmez²,

Zorlu

2009

Expert Systems

View full text Add to dashboard Cite

Although the use of predictive models in rock engineering and engineering geology is an important issue, some simple and multivariate regression techniques traditionally employed in these areas have recently been challenged by the use of fuzzy inference systems and artificial neural networks. The purpose of this study was to construct some predictive models to estimate the uniaxial compressive strength of some clay-bearing rocks, depending on examination of their slake durability indices and clay contents. For this purpose, the simple and nonlinear multivariable regression techniques and the Mamdani fuzzy algorithm are compared in terms of their accuracy. To increase the accuracy of the Mamdani fuzzy inference system, the weighted if-then rules are extracted. To compare the predictive performances of the models, the statistical performance indices (root mean square error and variance account for) are calculated and the results are discussed. The indices reveal that the fuzzy inference system has a slightly higher prediction capacity than the regression models. The basic reason for the higher performance of the fuzzy inference system is the flexibility of the fuzzy approach.

show abstract

Spatial estimation model of porosity

Cited by 16 publications

References 13 publications

NDT Assessment of Existing Concrete Structures: Spatial Analysis of Rebound Hammer Results Recorded in-Situ

NDT Assessment of Existing Concrete Structures: Spatial Analysis of Rebound Hammer Results Recorded in-Situ

Assessment of porosity using spatial correlation-based radial basis function and neuro-fuzzy inference system

Estimating the uniaxial compressive strength of some clay‐bearing rocks selected from Turkey by nonlinear multivariable regression and rule‐based fuzzy models

Contact Info

Product

Resources

About