Eurocode 7 and Rock Engineering Design: The Case of Rockfall Protection Barriers

The Eurocode 7 or EC7 is the Reference Design Code (RDC) for geotechnical design including rock engineering design within the European Union (EU). Moreover, its principles have also been adopted by several other countries, becoming a key design standard for geotechnical engineering worldwide. It is founded on limit state design (LSD) concepts, and the reliability of design is provided mainly by a semi-probabilistic method based on partial factors. The use of partial factors is currently an advantage, mainly for the simplicity in its applicability, and a limitation, especially concerning geotechnical designs. In fact, the application of partial factors to geotechnical design has proven to be difficult. In this paper, the authors focus on the way to apply EC7 principles to rock engineering design by analyzing the design of rockfall protection structures as an example. A real case of slope subjected to rockfall is reported to outline the peculiarity connected to rock engineering. The main findings are related to the complementarity of the reliability-based design (RBD) approach within EC7 principles and the possibility of overcoming the limitations of a partial factor approach to this type of engineering problem.

An Overview of Opportunities for Machine Learning Methods in Underground Rock Engineering Design

Machine learning methods for data processing are gaining momentum in many geoscience industries. This includes the mining industry, where machine learning is primarily being applied to autonomously driven vehicles such as haul trucks, and ore body and resource delineation. However, the development of machine learning applications in rock engineering literature is relatively recent, despite being widely used and generally accepted for decades in other risk assessment-type design areas, such as flood forecasting. Operating mines and underground infrastructure projects collect more instrumentation data than ever before, however, only a small fraction of the useful information is typically extracted for rock engineering design, and there is often insufficient time to investigate complex rock mass phenomena in detail. This paper presents a summary of current practice in rock engineering design, as well as a review of literature and methods at the intersection of machine learning and rock engineering. It identifies gaps, such as standards for architecture, input selection and performance metrics, and areas for future work. These gaps present an opportunity to define a framework for integrating machine learning into conventional rock engineering design methodologies to make them more rigorous and reliable in predicting probable underlying physical mechanics and phenomenon.

Principles of Risk-Based Rock Engineering Design

In comparison with other types of construction, the development of rock engineering design codes has been slow. Codes must, however, be developed with relevant discipline-specific characteristics in mind. This paper, therefore, presents a generic design framework for rock engineering. The framework is based on the presumption that rock engineering design must be viewed as decision-making under uncertainty, which makes the design process subject to general risk management principles, as risk is defined as “effect of uncertainties on objectives” (ISO 31000). Thus, rock engineering design codes ultimately need to facilitate design processes that target the risk, to enable design of structures that not only are sufficiently safe and durable and cost-effectively constructed, but also imply safe and healthy work conditions during construction and an acceptably low environmental impact. The presented framework satisfies this fundamental requirement and the authors find codification of its principles to be rather straightforward, as long as the level of detail in the code is governed by a strict application of ISO’s general risk management principles. Further details on methods and practical recommendations can instead be supplemented in separate handbooks and application guidelines.