| Issue |
MATEC Web Conf.
Volume 251, 2018
VI International Scientific Conference “Integration, Partnership and Innovation in Construction Science and Education” (IPICSE-2018)
|
|
|---|---|---|
| Article Number | 05002 | |
| Number of page(s) | 5 | |
| Section | Management in Construction | |
| DOI | https://doi.org/10.1051/matecconf/201825105002 | |
| Published online | 14 December 2018 | |
Organizational-technological decisions in construction based on neural network models
Moscow State University of Civil Engineering, Yaroslavskoe shosse, 26, Moscow, 129337, Russia
* Corresponding author: zharovyav@mgsu.ru
Increasing the quality of organizational and technological solutions in construction is one of the main tasks facing the construction industry. The actual methods of solving the problem of setting organizational and technological design are directly related to the integration of specialized software in the planning and design of construction projects, especially for unique, complex projects, projects implemented in tight time and construction sites in the current urban development. The existing need to process a significant amount of information at short intervals and to link design decisions to the dynamic environment of the construction site is not an easy task, but a realizable one. Within the framework of the research work carried out at the department of MGSU, the expediency of applying operational assessments of the parameters of organizational and technological solutions based on mathematical methods has been established. The proposed method for forecasting and evaluating the integral parameters of design solutions is based on a neural network model, the method used involves the formation of a training matrix comprising key indicators of implemented (pilot) ones.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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