Search
Abstract
The article concerns a problem of damages in family buildings, which result from traffic-induced vibrations. These vibrations arise from various causes and their size is influenced by many factors. The most important is the type of a road, type and weight of vehicles that run on the road, type and condition of the road surface, the distance from the house to the source of vibrations and many others which should be taken into account. Vibrations may cause different effects, i.e. plaster cracks, structural damage and even failure and collapse of the building. To determine the risk, one should perform time-consuming and expensive measurements using specialized equipment. Unfortunately, it is very difficult to conduct such measurements for each house located along the road. Modern technology, computer programs and the engineering knowledge gives us some possibilities to solve this problem. The best way to diagnose the influence of vibrations would be a program capable to assess (with a sufficiently high probability) a threat to a given residential building without performing field measurements. The aim of the present paper is to consider the artificial neural network as an example for such an approach. The method is based on the principle of the human brain and has been used to diagnose the impact of traffic-induced vibrations on family buildings.
Cite as
Full text
full text is not available in portal
Keywords
Details
- Category:
- Monographic publication
- Type:
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- Title of issue:
- Advances in Chemical and Mechanical Engineering. - Vol. I/II strony 217 - 221
- Language:
- English
- Publication year:
- 2012
- Bibliographic description:
- Jakubczyk-Gałczyńska A.: Diagnosis of damages in family buildings using neural networks// Advances in Chemical and Mechanical Engineering. - Vol. I/II/ ed. B. Ściborski, C. Fijało, P. Fijało. Gdańsk: Gdansk University of Technology, 2012, s.217-221
- Verified by:
- Gdańsk University of Technology
seen 111 times