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2 Computer Methods in Materials Science

2 2021

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The influence of material aging on the structural behavior of a flexible roof with a polymer membrane shell

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

Computer Methods in Materials Science

2021

Vol. 21, No. 1

13--24

The roof structure considered in the research consists of continuous cables and a number of spreaders forming a three-dimensional frame. The frame is covered with a polymer membrane made of flexible architectural fabrics. The elements of the roof are compact and suitable for transportation to remote construction sites. The roof also has advantages for developing areas with harsh climatic conditions. The flexible elements of the roof, however, only provide transmission of tensile forces. Under compression, cables slacken and the membrane becomes wrinkled. Pre-tension of the flexible elements, which is introduced to retain the operability of the roof, tends to gradually diminish due to material aging. The aging results in the alteration of strength properties and creep elongation of the structural elements. It induces membrane tearing on local areas. Force alteration in primary structural members is examined in the present study, with statistical methods used for data analysis. They include significance hypothesis testing and correlation coefficients estimation. The data are obtained by the Finite Element simulation of the roof using EASY-2020 software. The results of the work may be used for life expectancy assessment of flexible roof structures, providing important information for the preliminary design stage. The work contributes to the safety enhancement of cable-membrane structures and the expansion of their scope in permanent building constructions.

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.