Information system based on multi-value classification of fully connected neural network for construction management

• Published in: IAES International Journal of Artificial Intelligence Vol. 12; no. 2; p. 593
• Main Authors: Honcharenko, Tetyana, Akselrod, Roman, Shpakov, Andrii, Khomenko, Oleksandr
• Format: Journal Article
• Language: English
• Published: Yogyakarta IAES Institute of Advanced Engineering and Science 01.06.2023
• Subjects: Artificial intelligence; Classification; Construction management; Datasets; Machine learning; Mathematical models; Neural networks; Parameters; Training
• ISSN: 2089-4872; 2252-8938; 2089-4872
• DOI: 10.11591/ijai.v12.i2.pp593-601

This study is devoted to solving the problem to determine the professional adaptive capabilities of construction management staff using artificial intelligence systems. It is proposed fully connected feed-forward neural network (FCF-FNN) architecture and performed empirical modeling to create a data set. Model of artificial intelligence system allows evaluating the processes in an FCF-FNN during the execution of multi-value classification of professional areas. A method has been developed for the training process of a machine learning model, which reflects the internal connections between the components of an artificial intelligence system that allow it to “learn” from training data. To train the neural network, a data set of 35 input parameters and 29 output parameters was used; the amount of data in the set is 936 data lines. Neural network training occurred in the proportion of 10% and 90%, respectively. Results of this study research can be used to further improve the knowledge and skills necessary for successful professional realization.