Neural tree for estimating the uniaxial compressive strength of rock materials

Uniaxial Compressive Strength (UCS) is the most important parameter that quantifies the rock strength. However, determination of the UCS in the laboratory is very expensive and time-consuming. Therefore, common index tests like point load (Is-50), ultrasonic velocity test (VP), block punch index (BPI) test, rebound hardness (SRH) test, physical properties, etc., have been used to predict the UCS. The objective of this work is to develop a predictive model using a neural tree predictor that estimate the UCS with high accuracy and assess the effectiveness of different index tests in predicting the UCS of rock materials. UCS and indices such as BPI, Is-50, SRH, VP, effective porosity and density were determined for the granite, schist, and sandstone. The constructed model predicted the UCS with high accuracy and in a quick time (9 seconds). Additionally, the destructive mechanical rock indices BPI and Is-50 proved to be the best index tests to estimate the UCS

PROCJENA JEDNOOSNE TLAČNE ČVRSTOĆE POMOĆU MODELA BAZIRANIH NA REGRESIJSKIM STABLIMA

This paper presents the estimation of the uniaxial compressive strength for mudstone and wackestone carbonates. The need for the estimation has occurred due to inability to fulfill the high quality requirements of sample treatment during direct determination of this physical and mechanical property on certain types of rocks. For the needs of modelling intact rock materials, extracted from six locations in Croatia, were tested. The following properties were examined: density, effective porosity, point load strength index, Schmidt rebound hardness, P-wave velocity and uniaxial compressive strength which was the target value of the used statistical models. The statistical models based on multiple linear regression and regression trees were considered and compared using cross validation, which showed that the most efficient estimation of the uniaxial compressive strength is obtained using random forestsOvaj rad bavi se procjenom jednoosne tlačne čvrstoće za karbonate tipa madston-vekston. Potreba procjene javlja se zbog nemogućnosti ispunjavanja propisane visoke kvalitete obrade uzoraka kod direktnog određivanja tog fizikalno-mehaničkog svojstva na nekim vrstama stijena. Za potrebe modeliranja, u ovom radu, ispitivan je intaktni stijenski materijal sa šest mjesta u Hrvatskoj. Ispitane značajke su: gustoća, efektivna poroznost, indeks čvrstoće, Schmidtova tvrdoća, brzina prolaza ultrazvučnog P-vala te jednoosna tlačna čvrstoća koja je bila i ciljana vrijednost procjene uspostavljenih modela. Prikazani modeli su načinjeni na temelju višestruke regresije i regresijskog stabla, a provedena unakrsna validacija, pokazala je kako najuspješniju procjenu jednoosne tlačne čvrstoće daje model slučajnih šuma (engl. random forests)

Mathematical Problems in Rock Mechanics and Rock Engineering

With increasing requirements for energy, resources and space, rock engineering projects are being constructed more often and are operated in large-scale environments with complex geology. Meanwhile, rock failures and rock instabilities occur more frequently, and severely threaten the safety and stability of rock engineering projects. It is well-recognized that rock has multi-scale structures and involves multi-scale fracture processes. Meanwhile, rocks are commonly subjected simultaneously to complex static stress and strong dynamic disturbance, providing a hotbed for the occurrence of rock failures. In addition, there are many multi-physics coupling processes in a rock mass. It is still difficult to understand these rock mechanics and characterize rock behavior during complex stress conditions, multi-physics processes, and multi-scale changes. Therefore, our understanding of rock mechanics and the prevention and control of failure and instability in rock engineering needs to be furthered. The primary aim of this Special Issue “Mathematical Problems in Rock Mechanics and Rock Engineering” is to bring together original research discussing innovative efforts regarding in situ observations, laboratory experiments and theoretical, numerical, and big-data-based methods to overcome the mathematical problems related to rock mechanics and rock engineering. It includes 12 manuscripts that illustrate the valuable efforts for addressing mathematical problems in rock mechanics and rock engineering

A Deep Learning Method for the Prediction of the Index Mechanical Properties and Strength Parameters of Marlstone

The index mechanical properties, strength, and stiffness parameters of rock materials (i.e., uniaxial compressive strength, c, ϕ, E, and G) are critical factors in the proper geotechnical design of rock structures. Direct procedures such as field surveys, sampling, and testing are used to estimate these properties, and are time-consuming and costly. Indirect methods have gained popularity in recent years due to their time-saving and highly accurate results, which are comparable to those obtained through direct approaches. This study presents a procedure for establishing a deep learning-based predictive model (DNN) for obtaining the geomechanical characteristics of marlstone samples that have been recovered from the South Pars region of southwest Iran. The model was implemented on a dataset resulting from the execution of numerous geotechnical tests and the evaluation of the geotechnical parameters of a total of 120 samples. The applied model was verified by using benchmark learning classifiers (e.g., Support Vector Machine, Logistic Regression, Gaussian Naïve Bayes, Multilayer Perceptron, Bernoulli Naïve Bayes, and Decision Tree), Loss Function, MAE, MSE, RMSE, and R-square. According to the results, the proposed DNN-based model led to the highest accuracy (0.95), precision (0.97), and the lowest error rate (MAE = 0.13, MSE = 0.11, and RMSE = 0.17). Moreover, in terms of R2, the model was able to accurately predict the geotechnical indices (0.933 for UCS, 0.925 for E, 0.941 for G, 0.954 for c, and 0.921 for φ)

OCJENA REZIVOSTI STIJENA S LANČANOM SJEKAČICOM PRIMJENOM TEHNIKE PROMATHEE

One of the most significant factors in the estimation of dimension stone quarry cost is the production rate of rock cutting machines. Evaluating the production rate of chain-saw machines is a very significant and practical issue. In this research, it has been attempted to evaluate and select the suitable working-face for a quarry by examining the maximum production rate in the Dehbid and Shayan marble quarries. For this purpose, fi eld studies were carried out which included measuring operational characteristics of the chain-saw cutting machine, the production rate and sampling for laboratory tests from seven active case studies. Subsequently, the physical and mechanical properties of rocks including: Uniaxial Compressive Strength (UCS), Brazilian Tensile Strength (BTS), Los Angeles abrasion, quartz content, water absorption percentage, porosity, Schmidt hardness and grain size for all sample measurements were studied after transferring the samples to a rock-mechanics laboratory. Finally, the sawability of the quarried working-faces was evaluated using the PROMETHEE multi-criteria decision-making (MCDM) model according to the physical and mechanical properties. The results of the study indicated that the number 1 and 5 working-faces from the Dehbid and Shayan quarries are the most suitable working-faces in terms of production rate with the maximum recorded production values (4.95 and 3.1 m2 /h), and with net fl ow rates (2.67 and -0.36) respectively.Jedan od najvažnijih čimbenika u procjeni cijene vađenja građevinskoga kamena jest njegov iznos pridobivanja tijekom strojnoga rezanja. Procjena iznosa proizvodnje takvih strojeva ima vrlo veliku i praktičnu ulogu. Ovdje je načinjena procjena i odabir prikladnoga radilišta unutar kamenoloma radi postizanja najvećega iznosa proizvodnje. Za analizu su odabrani kamenolomi mramora Dehbid i Shayan. Načinjena su terenska ispitivanja, tj. mjerenje operativnih svojstava sjekačice, iznosa pridobivanja te uzorkovanje za laboratorij na sedam smjestišta. Zatim su u laboratoriju za mehaniku stijena izmjerena fizička i mehanička svojstva stijena poput jednoosne kompresijske čvrstoće, brazilske vlačne čvrstoće, abrazije metodom Los Angeles, udjela kvarca, postotka apsorpcije vlage, šupljikavosti, Schmidtove čvrstoće i veličine zrna. Na kraju je ocijenjena rezivost materijala na radilištu kamenoloma uporabom tehnike PROMETHEE, koja predstavlja alat za donošenje odluka na temelju više kriterija koji obuhvaćaju fizička i mehanička svojstva. Rezultati su pokazali kako radilišta označena brojevima 1 i 5, na kamenolomima Dehbid i Shayan, imaju najbolja svojstva postizanja većega brutoiznosa (4,95 i 3,2 m2 /sat) i netoiznosa (2,67 i -0,36 m2 /sat) proizvodnje

Assessing the system vibration of circular sawing machine in carbonate rock sawing process using experimental study and machine learning

Predicting the vibration of the circular sawing machine is very important in examining the performance of the sawing process, as it shows the amount of energy consumption of the circular sawing machine. Also, this factor is directly related to maintenance cost, such that with a small increase in the level of vibration, the maintenance cost increases to a large extent. This paper presents new prediction models to assess the vibration of circular sawing machine. An evaluation model based on the imperialist competitive algorithm as one of the most efficient artificial intelligence techniques was used for estimation of sawability of the dimension stone in carbonate rocks. For this purpose, four main physical and mechanical properties of rock including Schimazek's F-abrasivity, uniaxial compressive strength, mean Mohs hardness, and Young's modulus as well as two operational parameters of circular sawing machine including depth of cut and feed rate, were investigated and measured. In the predicted model, the system vibration in stone sawing was considered as a dependent variable. The results showed that the system vibration can be investigated using the newly developed machine learning models. It is very suitable to assess the system vibration based on the mechanical properties of rock and operational properties