Prediction of Uniaxial Compressive Strength and Modulus of Elasticity in Calcareous Mudstones Using Neural Networks, Fuzzy Systems, and Regression Analysis

The uniaxial compressive strength (UCS) and modulus of elasticity (E) are two important rock geomechanical parameters that are widely used in rock engineering projects such as tunnels, dams, and rock slope stability. Since the acquisition of high-quality core samples is not always possible, researchers often indirectly estimate these parameters. In the present study, prediction of UCS and E was investigated in calcareous mudstones of Aghajari Formation using multiple linear regression (MLR), multiple nonlinear regression (MNLR), artificial neural networks (ANN), and adaptive neuro-fuzzy ınference system (ANFIS). For this purpose, 80 samples from calcareous mudstones were subjected to the point loading, block punch, and cylinder punch tests. The performance of developed models was assessed based on determination coefficients (R2), mean absolute percentage error (MAPE), and variance accounted for (VAF) indices. The comparison of the obtained results revealed that, among the studied methods, ANFIS is the most suitable one for predicting UCS and E. Moreover, the results showed that ANN and MLNR respectively predict UCS and E better than MLR and a meaningful relationship between the observed and estimated UCS values in all regressions

An ANN approach for the prediction of uniaxial compressive strength, of some sedimentary and igneous rocks in Eastern KwaZulu-Natal

Abstract: The Uniaxial Compressive Strength (UCS) of intact rocks is an essential index of strength in rock engineering. Laboratory based direct compressive strength estimation may be problematic, as obtaining fresh samples is not always feasible. Thus, the aim of indirect methods index test such as point load index test, and empirical correlations with UCS of indexes like the Brazilian indirect tensile strength test, serve as an alternative for many geotechnical engineering projects. The aim of this paper is to propose a relationship between UCS and indirect tests or indexes for some sedimentary and igneous rocks in KwaZulu-Natal using the technology of artificial intelligence. These tests include the point load index (Is (50)) test and Brazilian Tensile Strength (σt), test. Block samples were collected in KwaZulu Natal, among these include sedimentary rocks (sandstones, siltstone, tillite) and igneous rocks (granitoids and dolerite). A back propagation artificial neural network was developed and trained in order to predict UCS. The input parameters were unit weight γ, (Is (50)), (σt), and lithology. The lithology was introduced in the neural network as a qualitative input parameter, in order to indirectly incorporate in the model the mineralogical content. Training results returned, R value of 0.99% for the training set, and R = 0.92% for the test set, which is conveying to the conclusion that the approach is valid and could be used, as an alternative indirect approach to UCS estimation

Simple and Non-Linear Regression Techniques Used in Sandy-Clayey Soils to Predict the Pressuremeter Modulus and Limit Pressure: A Case Study of Tabriz Subway

Nowadays, some common field tests consist of SPT test and pressuremeter test are performed in investigating the geotechnical parameters of projects such as tunneling. Due to the high cost of pressuremter test performance and its time-consuming procedure, using some empirical relations between SPT and Pressuremeter tests are recommended for primarily study of the project. The purpose of this study is to perform regression analyses between the NSPT and the uniaxial compression strength test and the pressuremeter test parameters obtained from a geotechnical investigation performed in route of 2nd line of Tabriz metro. Correlations were carried out for sandy and clayey soils separately. A series of simple and nonlinear multiple regression analyses are performed and as a result of analyses, several empirical equations are developed. It is shown that the empirical equations developed in this study are statistically acceptable.&nbsp

Characterization of physical and mechanical properties of rocks from Otanmäki, Finland

Abstract. Physical and mechanical properties of rocks are important parameters for geological engineering and design of engineering structures, be it in the civil and/or mining sector. Rock physical properties include density, porosity, etc., and Young’s modulus, Poisson’s ratio and rock strength include some mechanical properties of rocks. These properties can be obtained by laboratory tests. This study aims at characterizing selected rock physical and mechanical properties to assist in predicting rock mass behavior when used in engineering structures, to discuss key rock petrographical features that affect strength and compare the prediction capacities of multiple linear regression and artificial neural network (ANN) models. The study investigates selected physical and mechanical properties from two igneous rock types, gabbro and granite, from the Otanmäki area, central Finland. The test results were used for the ANN and multiple regression models. In the analyses, a total of 25 cases from the two rocks were tested for uniaxial compression strength (UCS), Young’s modulus, Poisson’s ratio, Brazilian tensile strength (BTS), density, porosity and water content. Samples were also analyzed for petrographic and chemical compositions. Results from the analyses indicate the importance of adhering to testing standards because of inconsistencies and wide variations observed between nonstandardized as opposed to standardized specimens, and the need for large database for reliable predictive models. It presents ANN techniques as having a good generalization capacity for multi-variable nonlinear prediction

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)

Application of Leeb Hardness Test in Prediction of Dynamic Elastic Constants of Sedimentary and Igneous Rocks

The Leeb hardness test is a non-destructive and portable technique that can be used both in the laboratory and in-field applications. The main purpose of this study is to predict the dynamic elastic constants of the igneous and sedimentary rocks using Leeb dynamic hardness testing. For this purpose, three vital topics have been investigated and analyzed. First, the relationships between ultrasonic wave velocities and dynamic elastic constants with the Leeb hardness were investigated. Thereafter, by determining the rock quality index (IQ) using microscopic studies and by analyzing the quality index-porosity plot, the variation of the Leeb hardness values was studied. Eventually, the longitudinal waveform in rock samples with different quality indexes and Leeb hardness were analyzed. To achieve these outputs, 33 samples of igneous and sedimentary rocks with a wide range of physical, mechanical, and textural features were collected and tested. The results of the analyses show that in both igneous and sedimentary rocks, the dynamic modulus of elasticity (Ed) has a significant correlation with the Leeb hardness. Generally, based on the microscopic studies, it was observed that the existence of the porosity in sedimentary rocks and intracrystalline and intracrystalline fissures in igneous rocks sharply reduce the Leeb hardness and thus lead to changes in the form of the longitudinal waves

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

Numerical Study of Concrete

Concrete is one of the most widely used construction material in the word today. The research in concrete follows the environment impact, economy, population and advanced technology. This special issue presents the recent numerical study for research in concrete. The research topic includes the finite element analysis, digital concrete, reinforcement technique without rebars and 3D printing