Information systems in geotechnics

The article considers the information modeling of buildings together with the foundation within the information system and the stages of its implementation. The workflow for building a 3D geotechnical model includes surface relief data, field and laboratory test data, soil lithology, geometric characteristics of the foundation structure and load. Automated systems with processing and interpretation of test data are used to determine the characteristics of soils. Mathematical modeling of the behavior of the foundations of the foundations with various input data is performed using analytical solutions and numerical methods. The natural heterogeneity of soil properties and its impact on the behavior of buildings is estimated by the sensitivity indicator of the foundation-foundation system by introducing virtual workings between the existing normative ones and the subsequent calculation of the precipitation and roll of the foundation.

Comparisons of Two Approaches for Geotechnical Model Calibration with Scarce Data

Geotechnical models are usually built upon assumptions and simplifications, inevitably resulting in discrepancies between model predictions and measurements. To enhance prediction accuracy, geotechnical models are typically calibrated against measurements by bringing in additional empirical or semiempirical correction terms. Different approaches have been used in the literature to determine the optimal values of empirical parameters in the correction terms. When measured data are abundant, calibration outcomes using different approaches can be expected to be practically the same. However, if measurements are scarce or limited, calibration outcomes could differ significantly, depending largely on the adopted calibration approach. In this study, we examine two most commonly used approaches for geotechnical model calibration in the literature, namely, (1) purely data-catering (PDC) approach, and (2) root mean squared error (RMSE) method. Here, the purely data-catering approach refers to selection of empirical parameter values that minimize coefficient of variation of model factor while maintains its mean value of one, based solely on measured data. A real case of calibrating the Federal Highway Administration (FHWA) simplified facing load model for design of soil nail walls is illustrated to thoroughly elaborate the differences in practical calibration and design outcomes using the two approaches under scarce data conditions.

Geotechnical Engineering for Mass Mining

Editor’s note: The Geology and Mining series, edited by Dan Wood and Jeffrey Hedenquist, is designed to introduce early-career professionals and students to a variety of topics in mineral exploration, development, and mining, in order to provide insight into the many ways in which geoscientists contribute to the mineral industry. Abstract The rock mass response to mining is governed by the rock mass characteristics and the mining-induced changes that drive its behavior. To be able to study and accurately predict the response of the rock mass to mining, it is imperative that both the orebody and the enclosing country rocks are well characterized through the collection and analysis of large quantities of good-quality, representative geologic, structural, geotechnical, and hydrogeological data. These are the fundamental constituents of a good geotechnical model whose reliability improves as the mining project matures and moves from exploration and study phases, passes the decision to develop, and proceeds into construction and then operations. Each phase provides greater exposure to the rock mass, reduces uncertainty, and increases reliability in the geotechnical model and in an understanding of the rock mass behavior. The quest of the geotechnical engineer is to understand the rock mass behavior and is no different from that of the geologist who defines the mineral resource, and it warrants (at the very least) the same level of rigor in data collection, analysis, and reporting. Just as the geologist continues to improve the orebody model through grade reconciliation during mining, so the geotechnical engineer must continually revisit and calibrate the geotechnical model during the operational phase of mining through geotechnical monitoring. The increasing demand by investors and stakeholders that the performance of a mine does not deviate from plan due to unforeseen geotechnical surprises warrants a significant shift in the level of geotechnical data collection, analyses, and rock mass monitoring through all stages of study and operations. This demand warrants supporting budgets and assurance processes that are commensurate with the complexity and extent of the geotechnical uncertainties.