E3S Web Conf.
Volume 110, 2019International Science Conference SPbWOSCE-2018 “Business Technologies for Sustainable Urban Development”
|Number of page(s)||8|
|Section||Energy Efficiency in the Construction|
|Published online||09 August 2019|
Improving construction duration forecasts and management of construction operations
1 Tuvan State University, Lenina street, 36, Kyzyl, 667000, Russia
2 St. Petersburg State University of Architecture and Civil Engineering, 2-nd Krasnoarmeiskaja street, 4, St.Petersburg, 190005, Russia
* Corresponding author: email@example.com
To address the relevant construction challenges, it is necessary both to improve the existing construction duration forecasting methods and to develop optimal construction management methods. That being said, effective implementation of general management functions is defined by information interaction between management subjects and objects as a system bringing together construction management stakeholders. Very often the construction practice reveals that some contractors’ statistics related to execution of works can altogether be absent or be unrepresentative. With this fact borne in mind, the authors of this material focus on using nonparametric statistic or distribution-free methods. On the other hand, the lack of representative statistical data indirectly indicates to ineffective management of the construction process. The article shows that the possible solution of the construction duration forecasting task is the methodology of neural and statistical simulation. We suggest that the efficiency of expeditious construction management should be assessed by calculating the information entropy, while regulatory action aimed at increasing managerial energy must be achieved by optimally allocating costs among the various elements oriented towards resource-based management. The presented model of improving construction duration forecasts allows the optimization of construction regulation process, which must increase its organizational and technological reliability.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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