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Abstract

A sea floor investigation was performed in the fiord of Hornsund by means of the seismoacoustic profiling, echosounding and core sampling. The main seismoacoustic sea floor units were recognized (the methods used according to Kowalewski et al. 1987a) and characterized on the basis of their relations to geomorphology and geological evolution. The bathymetrical sketch and the resulting geomorphological description of the bottom were prepared. The surface of the sea bottom and the surface of the bedrock displayed an irregular high relief with large sills dividing the fiord sea floor into several basins. Four main types of the sills were distinguished: burried sills, accumulative sills, rock sills and rock-accumulative sills. Within the internal Basins I and II there were thick (up to 170 m) covers of the glaciomarine ice-front deposit with changing thin ( 1 -5 m) blanket of the glaciomarine muds at the bottom surface. The Basin III had a cover of the glacial and glaciomarine deposits of variable lithology, genesis and age. The most external Basin IV had a cover o f glaciomarine muds up to 4 0 - 5 0 m thick, deposited on the tills. Four main glacial episodes were recognized, most probably referring to the stadials of Lisbetdalen, Slaklidalen, Revdalen and to the Little Ice Age.

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Authors and Affiliations

Włodzimierz Kowalewski
Stanisław Rudowski
S. Maciej Zalewski
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Abstract

There were two aims of the research. One was to enable more or less automatic confirmation of the known associations – either quantitative or qualitative – between technological data and selected properties of concrete materials. Even more important is the second aim – demonstration of expected possibility of automatic identification of new such relationships, not yet recognized by civil engineers. The relationships are to be obtained by methods of Artificial Intelligence, (AI), and are to be based on actual results from experiments on concrete materials. The reason of applying the AI tools is that in Civil Engineering the real data are typically non perfect, complex, fuzzy, often with missing details, which means that their analysis in a traditional way, by building empirical models, is hardly possible or at least can not be done quickly. The main idea of the proposed approach was to combine application of different AI methods in a one system, aimed at estimation, prediction, design and/or optimization of composite materials. The paradigm of the approach is that the unknown rules concerning the properties of concrete are hidden in experimental results and can be obtained from the analysis of examples. Different AI techniques like artificial neural networks, machine learning and certain techniques related to statistics were applied. The data for the analysis originated from direct observations and from reports and publications on concrete technology. Among others it has been demonstrated that by combining different AI methods it is possible to improve the quality of the data, (e.g. when encountering outliers and missing values or in clustering problems), so that the whole data processing system will be giving better prediction, (when applying ANNs), or the newly discovered rules will be more effective, (e.g. with descriptions more complete and – at the same time – possibly more consistent, in case of ML algorithms).

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Authors and Affiliations

D. Alterman
J. Kasperkiewicz

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