Fracture of brittle multiphase materials by high energy water jets

High energy water jets are established in processing brittle, inhomogeneous materials like rocks and concrete. Despite their wide field of application, the failure mechanisms of these materials, especially the influence of inclusions, are not well known. This work examines the influence of grain inclusions on the fracture behaviour of a multiphase brittle material exposed to high energy water jet processing. The behaviour of the specimens is detected by mass removal measurements, microscopical observations and the mercury penetration technique. It is found that the failure is based on microcrack growth due to hydrostatic pressure. The fracture mechanical behaviour of the reference material changes considerably with the addition of aggregates. The addition of grains leads to a reduction of the threshold tool energy for the start of mass removal. On the other hand, the presence of inclusions permits a more reduced and controlled removal progress. The interfaces between matrix and grains are the preferred locations for crack growth and also for crack branching. The inclusions act as crack arresters and crack branchers. In the case of cracks growing through the grains, a higher amount of fracture energy is absorbed and the fracture performance is weakened.Feodor-Lynen Fellowship holder of the Alexander von Humboldt Foundation, Germany.     

Providing Answers to Questions from Automatically Collected Web Pages for Intelligent Decision Making in the Construction Sector

The construction sector is notorious for the dichotomy between (a) its intensive use of information in its decision-making processes and (b) its limited access to, and insufficient use of, the pertinent information that is potentially available. In the context of the potential availability of valid information on the Web, we have developed a question-and-answer system, which enables construction practitioners to seek for information by posing questions in English or French, instead of entering a list of relevant keywords. Based on a modular systems approach using natural language, relevant answers to questions are selected and presented in a convivial way, thus improving and speeding up the classical Web search procedure. Our system consists of two main components: An intelligent robot that traverses a Web space and decides whether a page is construction oriented or not, and a question-answer (Q-A) component (comprising three modules) that uses a domain-specific thesaurus to extract meaningful parts of a question and to detect, process, and present paragraph passages extracted from relevant Web pages. The robot is trained on positive and unlabeled examples using a machine learning approach, while the Q-A component uses natural language processing techniques. In our experiments, we show that our automatically collected database consists of approximately 16% of noise, while the performance of the Q-A component is 65.45% in mean reciprocal rank.