The number of accidents in the construction industry in Nigeria is very high. It is rare to disclose safety performance metrics required to draw government attention to these incidents. This research aims to develop a predictive safety analytical model for the construction industry's safety assessment. The activities of safety standards in the Nigerian construction industry and improvement programs expected to be introduced in the construction industry have been researched to recognize the risks of construction accidents. The building injury risks found were modelled as predictor variables while the response variable was the level of safety using multiple regression analysis. Spearman's Correlation Analysis identified predictor variables that correlate significantly at p˃0.05 to the safety level, while predictor variables with low correlations have been excluded. The safety level was calculated on a linguistic scale of 5 points. The predictive ability of the model was verified at a 5 percent significance range by the coefficient of determination (R2) and the t-test. Under Law and Regulation, Practices, Personal Factors and Encouragement, fifteen task-related construction accident risks were identified and listed. The developed multilinear regression model has an 84.8% R2 rating. A 92.1% correlation between the predicted values and the human-interpreted safety-level values was revealed by the model implementation. The study also shows that personal variables should be given the highest degree of consideration by site engineers. The t-test showed no substantial difference between the values predicted and the values perceived by the level of human protection. Due to the feasibility of the study, the constructed model will serve as a valuable instrument for monitoring the safety level of Nigeria's construction sites. It is recommended that the model be used by site Engineers and Safety personnel from Federal and State Ministries.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright (c) 2021 Arid Zone Journal of Engineering, Technology and Environment published by University of Maiduguri, Nigeria