Open AccessRegression assessment of labor cost standards based on neural network simulation
Author(s) -
Sergey Bolotin,
Hussein Khoshnaw Yousif Babakr,
Haitham Bohan
Publication year - 2020
Publication title -
vestnik graždanskih inženerov
Language(s) - English
Resource type - Journals
ISSN - 1999-5571
DOI - 10.23968/1999-5571-2020-17-3-127-133
Subject(s) - documentation , computer science , scheduling (production processes) , duration (music) , artificial neural network , schedule , data collection , operations research , process (computing) , cost estimate , regression analysis , work (physics) , engineering , operations management , artificial intelligence , machine learning , systems engineering , statistics , mechanical engineering , art , literature , mathematics , programming language , operating system
Construction scheduling is based on data on the duration of works, which are directly related to the values of labor costs. This ensures obtaining of the most accurate results of scheduling implemented both at the stage of construction design and in the process of its operational management. In the Russian Federation and in other leading foreign countries, there have been developed databases for standardized labor costs in the field of construction. However, many developing countries, for example, the Republic of Iraq, are not provided with their own standards for labor costs, and therefore, they are forced to use either the standards of other countries or determine the duration of work with the help of expert estimates, which leads to a significant error in the construction planning schedule. To create databases on labor standards, various methods are used that are either based on timing measurements, or on physiological methods for measuring anthropological parameters associated with the energy costs of workers. Since the use of these approaches requires significant costs and time, a technique is proposed as an acceptable alternative. This technique is based on the collection of statistical data included in the executive documentation for construction, expert data that determine the feasibility of regulatory conditions for the performance of work, and regression processing of the final results based on the use of modeling capabilities of artificial neural networks.