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When confining pressure is low or absent, extensional fractures are typical, with fractures occurring on unloaded planes in rock. These “paradox” fractures can be explained by a phenomenological extension strain failure criterion. In the past, a simple empirical criterion for fracture initiation in brittle rock has been developed. But this criterion makes unrealistic strength predictions in biaxial compression and tension. A new extension strain criterion overcomes this limitation by adding a weighted principal shear component. The weight is chosen, such that the enriched extension strain criterion represents the same failure surface as the Mohr–Coulomb (MC) criterion. Thus, the MC criterion has been derived as an extension strain criterion predicting failure modes, which are unexpected in the understanding of the failure of cohesive-frictional materials. In progressive damage of rock, the most likely fracture direction is orthogonal to the maximum extension strain. The enriched extension strain criterion is proposed as a threshold surface for crack initiation CI and crack damage CD and as a failure surface at peak P. Examples show that the enriched extension strain criterion predicts much lower volumes of damaged rock mass compared to the simple extension strain criterion.
Die Nutzung von Prozessmodellierungsmethoden oder - werkzeugen kann erheblichen Einfluss auf die Effektivität von Prozessen haben. Das gilt insbesondere für Situationen, in denen Personen unter Stress stehen oder ungeübt sind. In diesen Fällen geben Prozessmodelle konkrete Empfehlungen, nach denen sich die handelnden Personen richten könnten. In Experimenten mit der Business-Simulation Apollo 13 haben wir den Effekt eines Einsatzes von Prozessmodellierungsmethoden und -werkzeugen untersucht. Bereits bekannte Theorien (z.B. über geeignete Verhältnisse von Kommunikationsinhalten) konnten bestätigt werden. Darüber hinaus haben wir eine besondere Bedeutung der Übertragbarkeit von Prozessmodellen in konkrete Handlungen identifiziert.
The progress in natural language processing (NLP) research over the last years, offers novel business opportunities for companies, as automated user interaction or improved data analysis. Building sophisticated NLP applications requires dealing with modern machine learning (ML) technologies, which impedes enterprises from establishing successful NLP projects. Our experience in applied NLP research projects shows that the continuous integration of research prototypes in production-like environments with quality assurance builds trust in the software and shows convenience and usefulness regarding the business goal. We introduce STAMP 4 NLP as an iterative and incremental process model for developing NLP applications. With STAMP 4 NLP, we merge software engineering principles with best practices from data science. Instantiating our process model allows efficiently creating prototypes by utilizing templates, conventions, and implementations, enabling developers and data scientists to focus on the business goals. Due to our iterative-incremental approach, businesses can deploy an enhanced version of the prototype to their software environment after every iteration, maximizing potential business value and trust early and avoiding the cost of successful yet never deployed experiments.