Design of building construction safety prediction model based on optimized BP neural network algorithm
Tao Shen, Yukari Nagai, Chan Gao
In order to solve the safety problem of the construction industry, the construction safety prediction model based on the optimized BP neural network algorithm is designed in this study. First, the characteristics of the construction industry were analyzed. As a labor-intensive industry, the construction industry is characterized by numerous factors such as large investment, long construction period and complicated construction environment. Due to the increasingly serious security problem, widespread concern over such problem has been aroused in society. Second, the problem of building construction safety management was summarized, six influencing factors were explored and a building construction safety prediction model based on rough set-genetic-BP neural network was established. Finally, the model was validated by a combination of multiparty consultation, empirical analysis and model comparison. The results showed that the model accurately predicted the risk factors during the construction process and effectively reduced casualties. Therefore, the model is feasible, effective and accurate.
It's very honored to get this award from JSSD, which has been undertaking activities to contribute to the advancement of academic research in the field of design. I really appreciate the forward-looking guidance from Prof. Nagai and the pleasant atmosphere in Nagai Lab. Design creativity is a vital and interesting research field, this award encourages me to build a more well-structured thesis in design creativity.