natgillin/translations-raw huggingface dataset:
Extremely weak rock 0,25 - 1
Ekstremno slaba
- Indented by thumbnail.
- Materijal se može grepsti i udubljivati noktom.
stiff fault gouge
tvrda ispuna diskontinuiteta
UCS - uniaxial compressive strength [MPa]; IS(50) - strength index [MPa]
UCS - jednoosna tlačna čvrstoća [MPa]; IS(50) - indeks čvrstoće [MPa]
Determination of uniaxial compressive strength by hand held accessories
Procjena vrijednosti jednoosne tlačne čvrstoće priručnim pokusom
Figure 2 UCS to E ratio, [3]
Dijagram međuovisnost E sa UCS, [3]
Schmidt hardness and UCS ratio, [4]
Dijagram međuovisnosti između Schmidtove tvrdoće i UCS, [4]
The diagram published in Miller's dissertation [4] (Figure 3) can be used to estimate the UCS value of intact rock based on the Schmidt hardness (SRH) and unit weight of rock, regardless of rock type that is being tested.
Dijagramom (slika 3.) objavljenim u Millerovoj disertaciji [4] može se obaviti procjena UCS intaktne stijene na temelju Schmidtove tvrdoće (SRH) i jedinične težine stijene bez obzira na vrstu stijene koja se ispituje.
Simple regression equations
Jednadžbe jednostruke regresije
Simple regression equations comprise relations defined for the estimation of UCS and E values as dependent variables based on the tested value of another property that constitutes an independent variable.
U jednadžbe jednostruke regresije ubrajaju se utvrđene relacije za procjenu vrijednosti UCS i E kao zavisnih varijabli na osnovi ispitane vrijednosti neke druge značajke koja predstavlja nezavisnu varijablu.
Uniaxial regression equations with porosity
Jednadžbe jednostruke regresije s poroznosti
Simple regression equations with strength index
Jednadžbe jednostruke regresije s indeksom čvrstoće
Type of rock Authors
Vrsta stijene Autori
UCS = 183 - 16,55 n granite
UCS = 183 - 16,55 n granit
Turgul and Zarif, 1999.
Turgul i Zarif, 1999.
UCS = 195,0 e -0,21 n
Tugrul, 2004.
[10] UCS - unconfined compressive strength [MPa]; E - elastic modulus [GPa]; n - porosity [%]
UCS - jednoosna tlačna čvrstoća [MPa]; E - modul elastičnosti [GPa]; n - poroznost [%]
Type of rock
Vrsta stijene Autori
D'Andrea, and ost.,
D'Andrea, i ost.,
sedimentary rocks
sedimentne stijene
UCS = (20 to 25) IS(50)
UCS = (20 do 25) IS(50)
all rocks
sve stijene
weak rocks
slabe stijene
limestone, sandstone, marl
vapnenac, lapor. vapnenac, pješčenjak, lapor
UCS = 24,8 IS(50) - 39,6 rocks with n < 1 %Kahraman and ost.,
UCS = 24,8 IS(50) - 39,6 stijene s n<1% Kahraman i ost.,
UCS = 10,2 IS(50) + 23,4 rocks with n > 1 % UCS = 10,58 IS(50)
UCS = 10,2 IS(50) + 23,4 stijene s n>1% UCS = 10,58 IS(50)
Tsallas and ost.,
Tsallas i ost.,
igneous rocks
magmatske stijena
metamorphic rocks
metamorfne stijene
UCS - unconfined compressive strength ([MPa]; IS(50) - strength index [MPa]
UCS - jednoosna tlačna čvrstoća ([MPa]; IS(50) - indeks čvrstoće [MPa]
The determination of strength index IS(50) by point load test is an index test that has been developing from the very start for the very purpose of estimating the UCS value, so that there are many papers in which the corresponding equations are published.
Određivanje indeksa čvrstoće IS(50) postupkom opterećenja u točki indeksno je ispitivanje koje se od početka razvijalo upravo radi procjene UCS, tako da postoje brojni radovi u kojima su objavljene jednadžbe s tom svrhom.
The most significant equations for estimating the UCS value of various rocks similar to those found in Croatia are presented in Table 3.
U tablici 3. prikazane su najznačajnije jednadžbe kojima se procjenjuju vrijednosti UCS različitih stijena koje su slične onima u Hrvatskoj.
It should be noted with regard to Table 3 that exponential equations are more accurate than linear-form equations for almost all rock types, except for metamorphic rocks where linear equation has proven to be slightly more accurate [18].
Vezano uz tablicu 3. važno je naglasiti kako su eksponencijalne jednadžbe točnije od linearnog oblika jednadžbi za gotovo sve vrste stijena, osim metamorfnih stijena kod kojih se linearna jednadžba pokazala malo točnijom [18].
Other than the strength index, the hardness of materials determined by Schmidt hammer has also often been used in these estimations.
Osim indeksa čvrstoće, veliku primjenu u procjenama našla je tvrdoća materijala koja se određuje pomoću Schmidtovog čekića.
Local experience in the UCS and E estimation [19], gained during intensive motorway infrastructure development in Croatia, when 518 boreholes over 7000 m in total length were analysed, reveal that the dependence between the UCS and Schmidt hardness greatly deviates in [4, 20, 21] from correlations presented in this paper.
Domaća iskustva u istraživanju UCS i E [19] provedena za vrijeme intenzivne infrastrukturne izgradnje mreže autocesta u Hrvatskoj, kada je analizirano 518 bušotina ukupne duljine jezgrovanja preko 7000 m, pokazuju kako međuovisnosti UCS sa Schmidtovom tvrdoćom značajno odstupaju od korelacijskih odnosa koji su ovdje prikazani prema radovima [4, 20, 21].
Simple regression equations with Schmidt hardness
Jednadžbe jednostruke regresije sa Schmidtovom tvrdoćom
Simple regression equations with P-wave velocities
Jednadžbe jednostruke regresije s brzinom prolaska P-vala
Simple regression equations that were used to estimate the UCS and E values were linear and nonlinear in form.
Nelinearni oblici poput jednadžbe opće potencije ili eksponencijalne jednadžbe pokazali su bolju uspješnost procjene.
It was established that better estimates were obtained with nonlinear forms such as general power equations or exponential equations.
Valja naglasiti kako se u znanstvenim radovima modeli jednostruke regresije često ocjenjuju preko koeficijenta korelacije i/ili koeficijentom determinacije.
It should be noted that simple regression models are often evaluated in research papers through correlation coefficients and/or coefficients of determination.
U nekim radovima ističu se gotovo idealne vrijednosti pa koeficijent determinacije za E iznosi čak 0,99 [21], a 0,98 za UCS [22].
Almost ideal values have been obtained by some authors, and so the coefficients of determination for E and UCS amount to as much as 0.99 [21] and 0.98 [22], respectively.
To može zavarati jer bi upotreba rigoroznije metode ocjene poput prilagođenog koeficijenta determinacije (engl. adjusted R-squared), korijena srednje kvadratne pogreške (engl.
Practical usability of a model where complex sample preparation is required for the independent variable determination, such as in VP determination, is questionable.
Upitna je praktična iskoristivost modela gdje određivanje nezavisne varijable zahtijeva složenu pripremu uzoraka, kako je to slučaj s određivanjem vP.
Multiple regression equations
Jednadžbe višestruke regresije
The multiple linear regression is generally presented with equation (1):
Općenito uzevši, višestruka linearna regresija predstavlja se izrazom (1)
where is: Y
gdje je Y
- dependent variable X1, X2, …, Xk - independent variables
- zavisna varijabla X1, X2, …, Xk - nezavisne varijable
- denotes contribution of the independent variable Xi
- određuje doprinos nezavisne varijable Xi
- random error [27].
- slučajna pogreška [27].
Models presented below are the models made for rock material similar to that prevailing in Croatia, and the UCS estimation is given in MPa, while E is determined in GPa.
U nastavku su predstavljeni modeli koji su načinjeni za stijenski materijal sličan onome u Hrvatskoj te daju procjenu UCS u MPa, a E u GPa.
Thus Alvarez Grimaa and Babuška [28] prepared a multiple regression model (2) based on test results for materials classified as sandstones, limestones, dolomites, granites, and granodiorites.
Tako su Alvarez Grimaa i Babuška [28] izradili višestruki regresijski model (2) na temelju rezultata ispitivanja materijala koji su pripadali pješčenjacima, vapnencima, dolomitima, granitima i grandioritima.
The multiple regression model is presented as follows:
Višestruki regresijski model dan je izrazom:
where is:
gdje je:
Ls - hardness defined with the Equotip hardness tester ρ - density [kg/m3] n - porosity [%].
Ls - tvrdoća utvrđena Equotip uređajem ρ - gustoća [kg/m3] n - poroznost [%].
Type of rock
Vrsta stijene
E = 1,94 SRH - 33,93 33 limestone types Sachpazis, 1990 [20]
E = 1,94 SRH - 33,93 33 vrste vapnenca Sachpazis, 1990 [20]
chalk, two limestone types, sandstone, marble, syenite, granite
kreda, dvije vrste vapnenca, pješčenjak, mramor, sijenit, granit
Katz and ost.,
Katz i ost.,
gypsum
gips
Yilmaz and Sendir, 2002.
Yilmaz i Sendir, 2002.
travertine, limestone, dolomitic limestone and schist
travertin, vapnenac, dolomitični vapnenac i škriljavac
UCS - uniaxial compressive strength [MPa]; E - elastic modulus [GPa]; SRH - Schmidth hardness
UCS - jednoosna tlačna čvrstoća [MPa]; E - modul elastičnosti [GPa]; SRH - Schmidtova tvrdoća
dolomite, sandstone, marl, limestone, diabase, serpentinite
dolomit, pješčenjak, lapor, vapnenac, dijabaz, serpentinit
dolomite, marble and limestone
dolomit, mramor i vapnenac
Yasar and Erdogan, 2004.
Yasar i Erdogan, 2004.
limestone, marble and sandstone
vapnenac, mramor i pješčenjak
Moradian and Behnia, 2009.
Moradian i Behnia, 2009.
UCS - uniaxial compressive strength [MPa]; E - elastic modulus [GPa]; vP - P-wave velocity [km/s]
UCS - jednoosna tlačna čvrstoća [MPa]; E - modul elastičnosti [GPa]; vP - brzina prolaska P-vala [km/s]
GS - grain size
GS - veličina zrna
RT - rock type.
RT - tip stijene.
Gokceoglu and Zorlu [30] prepared multiple regression models (4) and (5) for weak, fractured, and thin-bedded rocks that are represented with the following expressions:
Gokceoglu i Zorlu [30] su pak izradili višestruke regresijske modele (4) i (5) za slabe, razlomljene i tanko uslojene stijene koji su dani izrazima:
where is: vP -ultrasonic velocity of P-waves [m/s] BPI - block punch strength index [MPa] IS(50) - strength index defined by the point load test [MPa] TS - tensile strength [MPa]
gdje je: vP - brzina prolaska ultrazvučnog P-vala [m/s] BPI - Block Punch indeks čvrstoće [MPa] IS(50) - indeks čvrstoće utvrđen opterećenjem u točki [MPa] TS - vlačna čvrstoća [MPa]
where is: SRH - Schmidt hardness
gdje je: SRH - Schmidtova tvrdoća
- strength index defined by the point load test [MPa] vP - velocity of ultrasound waves [km/s].
- indeks čvrstoće određen postupkom opterećenja u točki [MPa] vP - brzina prolaska ultrazvučnih valova [km/s].
VBP - the volume percent of fragments [%] ρ - density [kg/m3] vS - velocity of ultrasonic S-waves [km/s] vP - velocity of ultrasonic P-waves [km/s].
VBP - volumni udio fragmenata [%] ρ - gustoća [g/cm3] vS - brzina prolaska ultrazvučnog S-vala [km/s] vP - brzina prolaska ultrazvučnog P-vala [km/s].
Yilmaz and Yuksek developed the models (9) and (10) for natural gypsum [33]
Yilmaz i Yuksek su razvili modele (9) i (10) za prirodni gips [33]
UCS -uniaxial compressive strength [MPa] n - porosity [%]. vp - velocity of ultrasonic Pp-waves [km/s] ρ - density [kg/m3].
UCS - jednoosna tlačna čvrstoća [MPa] n - poroznost [%] vp - brzina prolaska ultrazvučnih P-valova [km/s] ρ - gustoća [g/cm3].
where is: n - porosity [%].
gdje je: n - poroznost [%]
Cfc - ferrous carbonate binder percentage [%] Cf - percentage of iron oxide in cement [%] Mp - percentage of mica [%].
Cfc - postotak željeznog karbonatnog veziva [%] Cf - postotak cementa željeznog oksida [%] Mp - postotak tinjaca [%].
Equations for estimating the UCS (13) and E (14) values which use, as independent variables, the frequently determined physicomechanical properties, were developed based on testing conducted on 29 types of carbonate rock materials from 19 localities in the Republic of Croatia.
Na temelju ispitivanja 29 vrsta karbonatnog stijenskog materijala s 19 lokaliteta u Republici Hrvatskoj načinjene su jednadžbe za procjenu UCS (13) i E (14) koje kao nezavisne varijable koriste češće određivana fizikalno-mehanička svojstva [36]:
where is: ρ - density [kg/m3] n - porosity [%].
gdje je: ρ - gustoća [kg/m3] n - poroznost [%]
The equations (15) and (16) were developed for the mudstone and wackestone type limestones originating from Croatia [37].
UCS = -106,2093 - 0,04868 ρ + 11,5110 IS(50) + 0,052 vP (15)
UCS = -106,2093 - 0,04868 ρ + 11,5110 IS(50) + 0,052 vP (15)
UCS = -240,0109 + 1,5087 n + 11,5916 IS(50)+ 0,0522 vP (16)
UCS = -240,0109 + 1,5087 n + 11,5916 IS(50)+ 0,0522 vP (16)
gdje je: ρ - gustoća [kg/m3] n - poroznost [%]
where is: ρ - density [kg/m3] n - porosity [%]
IS(50) - indeks čvrstoće određen postupkom opterećenja u točki
IS(50) - strength index defined by the point load test [MPa] vP - velocity of ultrasonic P-waves [m/s].
[MPa] vP - brzina prolaska ultrazvučnih P-valova [m/s].
It can be seen from the above presented multiple regression models that researchers mostly used physicomechanical properties as independent variables, while petrographic properties of rock material were less often used.
Na temelju prikazanih modela višestruke regresije vidljivo je kako su istraživači većinom kao nezavisne varijable koristili vrijednosti ispitivanja fizikalno-mehaničkih svojstava, a manje su korištena petrografska svojstva stijenskog materijala.
It is interesting to note that the density and porosity are used together as independent variables in the models (2), (3), (11), (13), and (14) although, due to great physical connection of these properties, it can not be claimed that these variables are independent from one another, as is required by the mathematical model of multiple regression.
Zanimljivo je što se u modelima (2), (3), (11), (13) i (14) kao nezavisne varijable koriste zajedno gustoća i poroznost, iako se zbog velike fizikalne povezanosti tih svojstava ne može tvrditi da su varijable neovisne jedna o drugoj, kako to zahtijeva matematički model višestruke regresije.
Models based on fuzzy logic analysis
Modeli utemeljeni na analizi neizrazitih skupova
In the traditional "crisp" logic, a rational claim can be either true or false.
U klasičnoj izrazitoj logici neka smislena tvrdnja može biti ili istinita ili lažna.
According to fuzzy logic, no claim is fully true or fully false, but rather a "level of truthfulness" can be attributed to it.
Prema neizrazitoj (engl. fuzzy) logici tvrdnja nije niti posve istinita niti posve lažna, nego joj se može pripisati "stupanj istinitosti".
Rules have been set for using this logic, and these rules are a generalisation of normal Boolean algebra [38].
Uvedena su pravila za korištenje tom logikom, koja predstavljaju poopćenje normalne Booleove algebre [38].
Although fuzzy logic was initially used in social sciences, it is now increasingly used in technical sciences as well, e.g. in risk management on construction projects [39], but also in various evaluations.
Iako se neizrazita logika na početku primjenjivala u društvenim znanostima, danas je svoju primjenu našla i u tehničkim znanostima, npr. u upravljanju rizicima građevinskih projekata [39], ali i pri raznoraznim procjenama.
Most significant and the most often used models based on fuzzy logic and fuzzy reasoning are the Mamdani and Sugeno models.
Najznačajniji i najviše korišteni modeli koji rade na principu neizrazite logike i zaključivanja su Mamdani i Sugeno modeli.
Based on Mamdani model, Gokceoglu and Zorlu developed a model for estimation of USC and E values based on the input values similar to those used in models (4) and (5) [30].
Gokceoglu i Zorlu su na bazi Mamdani modela izradili model za procjenu USC i E koji je te značajke procjenjivao na temelju istih ulaznih vrijednosti kao u modelima (4) i (5) [30].
The same type of fuzzy model was used by Karakus and Tutmez for development of the UCS estimation model.
Isti tip neizrazitog modela koristili su Karakus i Tutmez pri izradi modela za procjenu UCS.
The investigation was made for nine different rock types and for a total of 305 samples.
Istraživanja su proveli na devet različitih tipova stijena i ukupno 305 uzoraka.
The input data for the model were laboratory testing results as in model (6) [31].
Ulazni podatci u model bili su rezultati laboratorijskih ispitivanja kao u modelu (6) [31].
By comparing fuzzy model with the multiple regression model, these researchers concluded that a better UCS estimation is obtained by using the fuzzy model [30, 31].
Spomenuti istraživači su, uspoređujući neizraziti model s modelom višestruke regresije, zaključili kako neizraziti model daje bolju procjenu UCS [30, 31].
Alvarez and Babuska developed the UCS estimation model using theoretical premises from TakagiSugeno model based on results of testing involving 226 intact rock material samples classified as sandstones, limestones, dolomites, granites and granodiorites.
Alvarez i Babuska izradili su model za procjenu UCS pomoću teorijskih postavki Takagi-Sugeno modela na temelju rezultata ispitivanja provedenih na 226 uzoraka intaktnog stijenskog materijala koji su pripadali pješčenjacima, vapnencima, dolomitima, granitima i grandioritima.
Input data were the same as independent variables from equation (2).
Ulazni podatci bili su isti kao nezavisne varijable u jednadžbi (2).
Estimation results were compared with the multiple regression modelling results, and also with the results obtained via neural network that was established using the same input data.
Rezultate procjene usporedili su s rezultatima procjene modela višestruke regresije i rezultatima koje je dala neuronska mreža, uspostavljena na temelju istih ulaznih podataka.
It was concluded that better estimation was obtained by fuzzy model compared to multiple regression model, while the neural network provided better estimation
Zaključili su da neizraziti model ima bolju procjenu od modela višestruke regresije, a neuronska mreža ima bolju procjenu od neizrazitog modela za niske i
than fuzzy model for low and high UCS values [28].
visoke vrijednosti UCS [28].
Subsequent development of programming techniques resulted in the situation in which fuzzy models were not often developed on their own, but rather the fuzzy logic became a part of other methods, e.g. in neural networks.
Daljnji razvoj računalne tehnike programiranja uzrokovao je da se neizraziti modeli nisu u većoj mjeri radili zasebno, već je neizrazita logika postala dio drugih metoda npr. u neuronskim mrežama.
Estimation by neural networks
Procjena pomoću neuronskih mreža
Neural networks consist of the systems of input and output values constituting nodes or neurons and the links or synapses between them, through which attempts are made to artificially simulate the way the human brain functions.
Neuronske mreže sastoje se od sustava ulaznih i izlaznih vrijednosti koje predstavljaju čvorove ili neurone i ostvarenih veza ili sinapsa između njih, čime se na umjetan način nastoji simulirati funkcioniranje ljudskog mozga.
In most cases, neural networks are not realised as hardware systems but rather as software programs, i.e. using the programming code.
U većini slučajeva neuronske mreže nisu realizirane hardverski nego softverski, odnosno pomoću programskog koda.
Neural networks are not programmed as algorithms with accurately determined relationships, but rather as algorithms capable of learning through examples.
Neuronske mreže ne programiraju se kao algoritmi s točno utvrđenim vrijednostima relacija, nego kao algoritam s mogućnošću učenja na primjerima.
They are presented with examples and solutions to these examples, and algorithms can then automatically generate empirical rules.
"Pokazuju" im se primjeri i rješenja tih primjera pa algoritam može automatski generirati empirijska pravila.
Before such algorithms can be used for estimating certain values, computer programs have to be "trained" on as set of known and required values [38].
Prije upotrebe takvih algoritama na procjenama traženih veličina, računalni programi moraju "trenirati" na skupu vrijednosti poznatih i traženih veličina [38].
Classification of neural networks
Klasifikacija neuronskih mreža
Meulenkamp and Alvarez developed the UCS estimation model based on the feedforward neural network in which the output error back propagation algorithm was used for training.
Meulenkamp i Alvarez izradili su model za procjenu UCS na temelju unaprjeđene neuronske mreže, koja je za učenje koristila algoritam povratnog prostiranja izlazne pogreške.
Input values used in this model are similar to those used in model (3).
Ulazne veličine u model su iste kao u modelu (3).
The network was "trained" using the Levenberg-Marquardt algorithm [29].
Mreža je uvježbavana pomoću Levenberg-Marquardt algoritma [29].
Sonmeza et al described the use of artificial neural networks in the estimation of E value for different rock types, based on the input values of UCS and material density [45].
Sonmeza i suradnici opisali su korištenje umjetne neuronske mreže u procjeni E za različite vrste stijena na temelju ulaznih vrijednosti UCS i gustoće materijala [45].
Kahraman et al used neural networks in the estimation of the UCS and E values for tectonic breccias.
Kahraman i suradnici su koristili neuronske mreže pri procjeni UCS i E za tektonske breče.
In the UCS estimation model, the neural network was structured as 2-3-1 (number of inputs, number of neurons in the hidden layer, output value).
Kod modela za procjenu UCS neuronska mreža imala je strukturu 2-3-1 (broj ulaza, broj neurona u skrivenom sloju, izlazna vrijednost).
The network input was formed of the volume part of fragments and the velocities of ultrasonic S-waves.
Ulaz mreže činili su volumni udio fragmenata i brzina prolaska ultrazvučnog S-vala.
The network of the model developed for estimating the E value was structured as 4-3-1, and the input was formed of: volume part of fragments, density, velocity of ultrasonic S-waves, velocity of ultrasonic P-waves, roundness of fragments, and the average grain size factor [32].
Mreža modela načinjenog za procjenu E imala je strukturu 4-3-1, a ulaz su činile: volumski udio fragmenata, gustoća, brzina prolaska ultrazvučnog S-vala, brzina prolaska ultrazvučnog P-vala, zaobljenost fragmenata i faktor prosječnog promjera zrna [32].
The use of fuzzy neural networks in the estimation of UCS and E for natural gypsum was described by Yilmaz and Yuksek.
Primjenu neizrazitih neuronskih mreža u procjeni UCS i E za prirodni gips opisali su Yilmaz i Yuksek.
Input parameters for the model were the following values obtained by laboratory testing: Schmidt hardness, strength index based on point load test, water content, and velocity of ultrasonic P-waves.
Parametri ulaza u model bile su vrijednosti dobivene laboratorijskim ispitivanjem tvrdoće Schmidtovim čekićem, indeksa čvrstoće određenog postupkom opterećenja u točki, udjela vode te brzine prolaska ultrazvučnih P-valova.
After comparison of results, these authors concluded that the best results in the estimation of UCS and E values for gypsum rocks were obtained by the fuzzy type of ANFIS neural networks, and by the model with the feedforward type of neural networks, while results were the worst when the multiple regression model was used [33].
Na osnovi usporedbe rezultata došli su do zaključka da u procjeni UCS i E za stijene gipsa najbolje rezultate postiže neizrazita vrsta neuronskih mreža ANFIS, zatim model s unaprjeđenom vrstom neuronskih mreža, a najslabije rezultate postiže višeregresijski model [33].
Heidari et al described the use of the MLP and RBF structured neural networks in the estimation of the E value for limestones, dolomites and marls from the Lorestan area in Iran.
Heidari i suradnici opisali su upotrebu neuronskih mreža MLP i RBF arhitekture u procjeni E za vapnence, dolomite i lapore s područja Lorestan u Iranu.
The following properties were input parameters for the model: density, porosity, velocity of ultrasonic P-waves, and uniaxial compressive strength.
Ulazni podatci u model bile su vrijednosti gustoće, poroznosti, brzine prolaska ultrazvučnih P-valova i jednoosne tlačne čvrstoće.
Better results were obtained by the model based on MLP architectures and the LevenbergMarquardt algorithm [34].
Model napravljen na bazi MLP arhitekture pomoću Levenberg-Marquardt algoritma pokazao se boljim [34].