Predicting construction accidents on sites: An improved atomic search optimization algorithm approach

Abstract

Construction accidents in the construction industry cause a large amount of property damage and human casualties. Therefore, avoiding construction accidents as much as possible is a problem that engineers have been working on for a long time. Currently, few construction managers use artificial intelligence methods for construction safety management. The purpose of this article is to propose a new artificial neural network (ANN) prediction model to provide early warning for future construction and to provide reference for construction organization decision-makers. In the proposed method, atomic search optimization algorithm is used to optimize the weights and thresholds of back propagation neural network, and the Tent chaotic mapping is used to initialize the population to increase the diversity of the population. The statistical data of production safety accidents of housing and municipal engineering in China from 2015 to 2019 are used as an example, and the prediction results of the proposed model are compared with back-propagation neural network (BPNN) and wavelet neural network (WNN). The mean absolute error (MAE) of predicting construction accidents is 0.2225, with small fluctuations in the predicted results. The mean absolute percentage error (MAPE) of the predictions is 0.6048%. The research results indicate that IASO-BPNN has higher prediction accuracy than standard BPNN and WNN, providing judgment methods for ensuring construction progress and decision support for construction organization decision-makers.