Procjena duljine traga pukotine razlomljene stijenske mase pomoću algoritma SVM (algoritma stroja potpornih vektora)

Jointed rock masses modeling needs the geometrical parameters of joints such as orientation, spacing, trace length, shape, and location. The rock joint trace length is one of the most critical design parameters in rock engineering and geotechnics. It controls the stability of the rock slope and tunnels in jointed rock masses by affecting rock mass strength. This parameter is usually determined through a joint survey in the field. Among the parameters, trace length is challenging because a complete joint plane within rock mass cannot be observed directly. The development of predictive models to determine rock joint length seems to be essential in rock engineering. This research made an effort to introduce a support vector machine (SVM) model to estimate rock joint trace length. The SVM is an advanced intelligence method used to solve the problem characterized by a small sample, non-linearity, and high dimension with a good generalization performance. In this study, three data sets from the sedimentary, igneous, and metamorphic rocks were organized, which location of joints on the scanline, aperture, spacing, orientation (D/DD), roughness, Schmidt rebound of the joint’s wall, type of termination, trace lengths in both sides of the scanline and joint sets were measured. The results of SVM prediction demonstrate that predicted and measured results are in good agreement. The SVM model-based results were compared with those obtained from field surveys. The proposed SVM model-based model was very efficient in predicting rock joint trace length values. The actual trace length could be estimated; thus, the expensive, difficult, time-consuming, and destructive joint surveys related to obscured joints could be avoided.Za modeliranje razlomljenih stijenskih masa potrebni su geometrijski parametri pukotina kao što su orijentacija, razmak, duljina pukotine, oblik i lokacija. Duljina pukotine stijene jedan je od najkritičnijih projektnih parametara u inženjerstvu stijena i geotehnici. Ona kontrolira stabilnost nagiba stijene i tunela u razlomljenim stijenskim masama te utjecaj na čvrstoću stijenske mase. Obično se utvrđuje terenskim istraživanjima. Istraživanje duljine pukotine zahtjevno je, jer se cjelovita duljina unutar stijenske mase ne može promatrati izravno. Razvoj modela za predviđanje duljine traga pukotine iznimno je važan u inženjerstvu stijena. U okviru ovoga istraživanja predstavljen je algoritam SVM (engl. support vector machine, hrv. stroj potpornih vektora) za procjenu duljine pukotine. Riječ je o naprednoj metodi koja se koristi za rješavanje problema maloga uzorka, nelinearnosti i višestrukih dimenzija, s dobrim svojstvima generalizacije problema. Priređena su tri skupa podataka iz taložnih, magmatskih i metamorfnih stijena, koji uključuju položaj pukotina na liniji uzorkovanja, te njihov otvor, razmak, orijentaciju, hrapavost, Schmidtov odbojni test na stijenke pukotine, tip završetka, duljine traga na obje strane linije uzorkovanja i skupove pukotina. Rezultati predloženoga algoritma pokazuju podudarnost predviđenih i izmjerenih rezultata dobivenih terenskim istraživanjima. Rezultati pokazuju da je predloženi algoritam vrlo učinkovit u predviđanju vrijednosti duljine traga pukotine stijenske mase. Njegovim korištenjem može se dobro procijeniti stvarna duljina traga pukotine te izbjeći skupa, teška, dugotrajna i agresivna istraživanja razlomljenih stijenskih masa

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

A fractal fragmentation model for rockfalls

The final publication is available at Springer via http://dx.doi.org/10.1007/s10346-016-0773-8The impact-induced rock mass fragmentation in a rockfall is analyzed by comparing the in situ block size distribution (IBSD) of the rock mass detached from the cliff face and the resultant rockfall block size distribution (RBSD) of the rockfall fragments on the slope. The analysis of several inventoried rockfall events suggests that the volumes of the rockfall fragments can be characterized by a power law distribution. We propose the application of a three-parameter rockfall fractal fragmentation model (RFFM) for the transformation of the IBSD into the RBSD. A discrete fracture network model is used to simulate the discontinuity pattern of the detached rock mass and to generate the IBSD. Each block of the IBSD of the detached rock mass is an initiator. A survival rate is included to express the proportion of the unbroken blocks after the impact on the ground surface. The model was calibrated using the volume distribution of a rockfall event in Vilanova de Banat in the Cadí Sierra, Eastern Pyrenees, Spain. The RBSD was obtained directly in the field, by measuring the rock block fragments deposited on the slope. The IBSD and the RBSD were fitted by exponential and power law functions, respectively. The results show that the proposed fractal model can successfully generate the RBSD from the IBSD and indicate the model parameter values for the case study.Peer ReviewedPostprint (author's final draft