Version 1
: Received: 8 July 2024 / Approved: 8 July 2024 / Online: 9 July 2024 (13:20:30 CEST)
How to cite:
Abdissa, D. B.; Legese, A. M.; Kebede, L. B.; Boru, Y. T. Developing Construction Labor Productivity Model for Concreting Activity Using Artificial Neural Networks. Preprints2024, 2024070674. https://doi.org/10.20944/preprints202407.0674.v1
Abdissa, D. B.; Legese, A. M.; Kebede, L. B.; Boru, Y. T. Developing Construction Labor Productivity Model for Concreting Activity Using Artificial Neural Networks. Preprints 2024, 2024070674. https://doi.org/10.20944/preprints202407.0674.v1
Abdissa, D. B.; Legese, A. M.; Kebede, L. B.; Boru, Y. T. Developing Construction Labor Productivity Model for Concreting Activity Using Artificial Neural Networks. Preprints2024, 2024070674. https://doi.org/10.20944/preprints202407.0674.v1
APA Style
Abdissa, D. B., Legese, A. M., Kebede, L. B., & Boru, Y. T. (2024). Developing Construction Labor Productivity Model for Concreting Activity Using Artificial Neural Networks. Preprints. https://doi.org/10.20944/preprints202407.0674.v1
Chicago/Turabian Style
Abdissa, D. B., Lelise Berhanu Kebede and Yada Tesfaye Boru. 2024 "Developing Construction Labor Productivity Model for Concreting Activity Using Artificial Neural Networks" Preprints. https://doi.org/10.20944/preprints202407.0674.v1
Abstract
There are various modeling techniques adopted for predicting the labor productivity that incorporate the influence of various factors, but neural networks are found to have strong pattern recognition and higher accuracy to get reliable estimates. To develop a construction labor productivity (CLP) model for concreting activities, data were collected through a questionnaire. The most critical influencing parameters were ranked according to their relative importance index (RII). Then the strength of the relationship between CLP and influencing parameters was analyzed using correlation coefficient results generated in Python program. Finally, the CLP model which represents the output of the crew within a certain factor was successfully developed using artificial neural networks (ANNs). Five objectives and six subjective critical influencing parameters were selected using RII. Crew experience, age of workers, and placement technique are the top three influencing parameters that have a strong relation with CLP with correlation coefficient values of 0.5681, 0.5349, and 0.5227 respectively. The developed model within these influencing parameters has a higher capability to predict the output of labor with a 92% coefficient of determination (R2) and a mean squared error of 0.316%. Therefore, the application of such a model for the accurate estimation of labor productivity is recommended.
Keywords
ANN; building projects; concreting activities; CLP model
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
