Title: Construction fatigue prediction model based on improved random forest algorithm

Authors: Fuhai Wu

Addresses: Logistics and Security Department, Gannan University of Science and Technology, Ganzhou, Jiangxi, China

Abstract: Construction workers are prone to construction fatigue in high-intensity working environments, and failure to receive effective rest may result in casualties and property damage. The study uses deep learning algorithms to construct an intelligent fatigue prediction model aimed at accurately assessing the fatigue status of construction workers. The study takes smartphones to collect basic data and inputs it into an improved random forest algorithm for fatigue feature recognition. Then, an intelligent construction fatigue recognition model is established based on the improved random forest algorithm. The research model had an accuracy rate of 94.7% in recognising different human movements, and an accuracy rate of 91% in predicting construction fatigue. The designed method accurately predicts the complete exhaustion, fatigue, concentration and excitement states of workers, and its predictive ability is superior to other prediction models. The research model can effectively assist construction managers in accurately detecting workers' fatigue status and taking timely intervention measures to reduce safety accidents.

Keywords: RF; PCA; PSO; fatigue; construction.

DOI: 10.1504/IJRS.2026.150487

International Journal of Reliability and Safety, 2026 Vol.20 No.1, pp.71 - 90

Received: 15 Oct 2024
Accepted: 13 Feb 2025
Published online: 15 Dec 2025 *

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