Version 1
: Received: 17 October 2023 / Approved: 18 October 2023 / Online: 18 October 2023 (09:59:32 CEST)
How to cite:
Alsakka, F.; Yu, H.; Hamzeh, F.; Al-Hussein, M. Data- and Knowledge-Driven Cycle Time Estimation in Offsite Construction. Preprints2023, 2023101151. https://doi.org/10.20944/preprints202310.1151.v1
Alsakka, F.; Yu, H.; Hamzeh, F.; Al-Hussein, M. Data- and Knowledge-Driven Cycle Time Estimation in Offsite Construction. Preprints 2023, 2023101151. https://doi.org/10.20944/preprints202310.1151.v1
Alsakka, F.; Yu, H.; Hamzeh, F.; Al-Hussein, M. Data- and Knowledge-Driven Cycle Time Estimation in Offsite Construction. Preprints2023, 2023101151. https://doi.org/10.20944/preprints202310.1151.v1
APA Style
Alsakka, F., Yu, H., Hamzeh, F., & Al-Hussein, M. (2023). Data- and Knowledge-Driven Cycle Time Estimation in Offsite Construction. Preprints. https://doi.org/10.20944/preprints202310.1151.v1
Chicago/Turabian Style
Alsakka, F., Farook Hamzeh and Mohamed Al-Hussein. 2023 "Data- and Knowledge-Driven Cycle Time Estimation in Offsite Construction" Preprints. https://doi.org/10.20944/preprints202310.1151.v1
Abstract
Cycle times at workstations in offsite construction factories fluctuate widely due to various influencing factors. Consequently, relying on average rates, such as length per unit of time, for estimating cycle times proves to be inaccurate, often leading to significant deviations between production schedules and actual operations. To address this issue, this study proposes an estimation system that leverages machine-learning-based prediction, statistical methods, 3D simulation, and computer vision to predict cycle times at the workstation level. Testing of the system on a semi-automated wood-wall framing workstation in a panelized construction factory shows that it reduces the mean absolute error and sum of errors by approximately 36% and 68%, respectively, compared to the fixed rate method. The results also highlight the efficacy of using computer vision data for training machine-learning models for cycle time estimation, the importance of identifying and understanding the factors influencing cycle times, and the impact of random delays on the accuracy of cycle time estimation systems.
Keywords
offsite construction factories; cycle time estimation; machine learning; computer vision; 3D simulation; influencing factors
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.
