Open AccessComparison of Modelling Tools in Assessing Safety Performance of Construction Site
Author(s) -
S. Ajith,
S. Chandrasekaran,
V. Arumugaprabu
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/955/1/012016
Subject(s) - flexibility (engineering) , artificial neural network , genetic programming , computer science , linear regression , range (aeronautics) , key (lock) , regression analysis , performance indicator , machine learning , reliability engineering , engineering , statistics , mathematics , computer security , management , economics , aerospace engineering
Safety performance is the key factor in construction sites as it figures out the unsafe act, condition and supervision of the worksite. Three different modelling tools such as Artificial Neural Network (ANN), Genetic Programming (GP) and Multi-linear Regression (MLR) are used to assess the performance of the site. Furthermore, the performance of these models is also analysed with respect to various conditions to assess its suitability. Results indicates that all the three models can be used to predict the safety performance but GP based models have higher flexibility in extrapolating the results outside the training range with highest accuracy. Thus, it is recommended to employ GP based models in predicting safety performance in construction sites.