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

TL;DR

This paper presents a neural network-based artificial intelligence system designed to evaluate the professional adaptive capabilities of construction management staff through multi-value classification, using empirical modeling with a dataset of 936 entries.

Contribution

It introduces a fully connected neural network architecture and a training method tailored for assessing professional skills in construction management.

Findings

Neural network trained with 936 data points achieved effective classification.

The model can evaluate professional capabilities based on multiple input parameters.

Training data split of 10% for validation and 90% for training was used successfully.

Abstract

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 architecture and performed empirical modeling to create a Data Set. Model of artificial intelligence system allows evaluating the processes in an Fully Connected Feed-Forward Neural Network 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.