MATEC Web Conf.
Volume 117, 2017RSP 2017 – XXVI R-S-P Seminar 2017 Theoretical Foundation of Civil Engineering
|Number of page(s)||6|
|Published online||24 July 2017|
Statistical learning problem of artificial neural network to control roofing process
Moscow state university of civil engineering, Yaroslavskoye shosse, 26, Moscow, Russia, 129337
* Corresponding author: firstname.lastname@example.org
Now software developed on the basis of artificial neural networks (ANN) has been actively implemented in construction companies to support decision-making in organization and management of construction processes. ANN learning is the main stage of its development. A key question for supervised learning is how many number of training examples we need to approximate the true relationship between network inputs and output with the desired accuracy. Also designing of ANN architecture is related to learning problem known as “curse of dimensionality”. This problem is important for the study of construction process management because of the difficulty to get training data from construction sites. In previous studies the authors have designed a 4-layer feedforward ANN with a unit model of 12-5-4-1 to approximate estimation and prediction of roofing process. This paper presented the statistical learning side of created ANN with simple-error-minimization algorithm. The sample size to efficient training and the confidence interval of network outputs defined. In conclusion the authors predicted successful ANN learning in a large construction business company within a short space of time.
© The Authors, published by EDP Sciences, 2017
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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