Organizational-technological decisions in construction based on neural network models

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...

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Main Author: Zharov Iaroslav
Format: Article
Language:English
Published: EDP Sciences 2018-01-01
Series:MATEC Web of Conferences
Online Access:https://doi.org/10.1051/matecconf/201825105002
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spelling doaj-0b296f907a934c80a549e1ba19d3075a2021-03-02T10:05:49ZengEDP SciencesMATEC Web of Conferences2261-236X2018-01-012510500210.1051/matecconf/201825105002matecconf_ipicse2018_05002Organizational-technological decisions in construction based on neural network modelsZharov Iaroslav0Moscow State University of Civil EngineeringIncreasing 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.https://doi.org/10.1051/matecconf/201825105002
collection DOAJ
language English
format Article
sources DOAJ
author Zharov Iaroslav
spellingShingle Zharov Iaroslav
Organizational-technological decisions in construction based on neural network models
MATEC Web of Conferences
author_facet Zharov Iaroslav
author_sort Zharov Iaroslav
title Organizational-technological decisions in construction based on neural network models
title_short Organizational-technological decisions in construction based on neural network models
title_full Organizational-technological decisions in construction based on neural network models
title_fullStr Organizational-technological decisions in construction based on neural network models
title_full_unstemmed Organizational-technological decisions in construction based on neural network models
title_sort organizational-technological decisions in construction based on neural network models
publisher EDP Sciences
series MATEC Web of Conferences
issn 2261-236X
publishDate 2018-01-01
description 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.
url https://doi.org/10.1051/matecconf/201825105002
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