Concrete is the safest and sustainable construction material wh ich is most widely used in the world as it provides superior fire resistance, gains strength over time and gives an extremely long service life. Unfortunately high performance concrete is undoubtedly one of the most innovativ e materials in construction. Its Designing involves the process of selecting suitable ingredients of concrete (water, cement, fine and aggregates and a number of additives like mineral and chemical ad mixture) and determining their relat ive amounts with the objective of producing a high performance concrete of the required, strength, durability, and workab ility as economically as possible. Their proportions have a high influence on the final strength of the product. These relations do not seem to follow a mathematical formu la and yet their knowledge is crucial to optimize the quantities of raw materials used in the manufacture of high performance concrete. Therefore, it would be important to have a tool to numerically model such relationships, even before pro cessing. In this aspect the main purpose of this paper is to predict the compressive strength of the high performance concrete by using classification algorith ms. For building these models, training and testing using the available experimental results for 1030 specimens produced with 8 d ifferent mixture proportions are used. The result fro m this study suggests that weighted Support Vector Machines (wSVM) based models perform remarkably well in predict ing the compressive strength of the concrete mix.
The actual challenge for the requalification of existing offshore structures through a rational process of reassessment leads to state the importance of Risk Based Inspection methodology. This paper points out the inspection results modelling and their contribution to decision aid tools. The study of the impact of through cracks on structural integrity of jacket platforms is still a challenge. The detection of large cracks is first addressed. In order to minimize inspections and maintenance costs, all the available data from inspection results, such as probability of detection and probability of false alarm, must be addressed, as well as the probability of crack presence. This can be achieved by the use of the decision theory. These capabilities of Non Destructive Testing give a first input for the risk study. A cost function is suggested to introduce this modelling into a risk analysis and is devoted to help rank the NDT tools. The case of large through-wall cracks is specifically addressed.
International audienceWhen performing risk analysis, it is often uneasy to find the link between limit state and consequences. This paper focuses on efficiency based limit states in case of large cracks on offshore structures. Randomness and uncertainties on loading as well as on crack measurement and detection are introduced.Les analyses de risque sont souvent délicates par manque de lien direct entre la fonction d’état et les conséquences. Cet article propose des fonctions d’état de type performantiel (déplacement) dans le cas d’apparitions de fissures traversantes dans des tubes métalliques de structures offshore. Les aléas sur le chargement, la mesure de la fissure et la performance des inspections sont intégrés dans l’analyse de risque
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