Artificial intelligence approach for detecting material deterioration in hybrid building constructions

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

The Faculty of Civil Engineering, Lipetsk State Technical University, Moskovskaya street 30, 398600 Lipetsk, Russian Federation.



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.

Cite as:

Chesnokov, A. V., Mikhailov, V. V., & Dolmatov, I. V. (2021). Artificial intelligence approach for detecting material deterioration in hybrid building constructions. Computer Methods in Materials Science, 21(2), 83-94.

Article (PDF):


Hybrid constructions, Material deterioration, Artificial neural network, Semi-supervised machine learning


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