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Integration of industrial videogrammetry and artificial neural networks for monitoring and modeling the deformation or displacement of structures

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Abstract

The need to monitor structures permanently and the necessity of performing measurements in a continuous space are of the most important factors which cause some restrictions, such as the rising cost, the dependence on the environment conditions and requirement for high-tech tools for permanent control of displacement and deformation of structures. Videogrammetry, which is used in different fields of industry and manufacturing as a precise and low-cost measurement method, can be utilized in this field. But the use of videogrammetry to control a structure deformation or displacement has four problems including synchronization of cameras in a permanent and long-term monitoring, the need for approximate values of unknown parameters, the necessity for automating the prediction of structure deformation or displacement and the need for high-speed computing. The evaluation of the system designed and implemented in this research shows that using videogrammetry based on the method presented in the research and the integration of videogrammetry and artificial neural networks can solve these problems. The system can be applied to produce the data which are needed for decision support systems to control displacement or deformation of structures intelligently.

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Acknowledgments

This research is based on data collected with cooperating of Mr. Ali Rezghi. So, I would like to express my sincere gratitude to him for providing the required data.

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Authors and Affiliations

  1. Department of Geomatics, Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

    Farshid Farnood Ahmadi

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  1. Farshid Farnood Ahmadi
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Correspondence to Farshid Farnood Ahmadi.

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Farnood Ahmadi, F. Integration of industrial videogrammetry and artificial neural networks for monitoring and modeling the deformation or displacement of structures. Neural Comput & Applic 28, 3709–3716 (2017). https://doi.org/10.1007/s00521-016-2255-2

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