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Artificial intelligence approach for detecting material deterioration in hybrid building constructions

Chesnokov Andrei V., Mikhailov Vitalii V., Dolmatov Ivan V.

Computer Methods in Materials Science

2021

Vol. 21, No. 2

83--94

Hybrid constructions include heterogeneous materials with different behaviors under load. The aim is to achieve a so-called synergistic effect when the advantages of particular structural elements complement each other in a unified system. The building constructions considered in the research include high-strength steel cables, fiberglass rods, and flexible polymer membranes. The membrane is attached to the rods which have been elastically bent from the initially straight shape into an arch-like form. Structural materials inevitably deteriorate during a long operational period. The present study focuses on detecting material deterioration using Artificial Neural Networks (ANNs), which belong to the scope of intelligent techniques for data analysis. Appropriate ANN structures and required features are proposed. A semi-supervised learning strategy is used. The approach allows the training of the networks with normal data only derived from the construction without defects. Material degradationis detected by the level of reconstruction error produced by the network given the input data. The work contributes to the field of structural health monitoring of hybrid building constructions. It provides the opportunity to detect material deterioration given the forces in particular structural elements.

Evaluating lexicographer controlled semi-automatic word sense disambiguation method in a large scale experiment

Broda B., Piasecki M.

Control and Cybernetics

2011

Vol. 40, no 2

419-436

Word Sense Disambiguation in text remains a difficult problem as the best supervised methods require laborious and costly manual preparation of training data. On the other hand, the unsupervised methods yield significantly lower precision and produce results that are not satisfying for many applications. Recently, an algorithm based on weakly-supervised learning for WSD called Lexicographer-Controlled Semi-automatic Sense Disambiguation (LexCSD) was proposed. The method is based on clustering of text snippets including words in focus. For each cluster we find a core, which is labelled with a word sense by a human, and is used to produce a classifier. Classifiers, constructed for each word separately, are applied to text. The goal of this work is to evaluate LexCSD trained on large volume of untagged text. A comparison showed that the approach is better than most frequent sense baseline in most cases.