关键词: 有毒有害气体, GA-BP, 预测模型, 隧洞

Abstract: The construction site of diversion tunnels are often accompanied by toxic and harmful gases generated from blasting operations, which would affect the safe operation of construction. To address the above problem, a prediction model for toxic and harmful gas concentration based on GA-BP neural net⁃ work is proposed, which can improve the shortcomings of BP neural network, such as difficulty in obtain⁃ ing the initial threshold and weights, and the proneness of falling into local optimal value. The model is applied to the blasting construction site of a diversion tunnel in Chongqing for analysis and verification. The result shows that in the concentration prediction of carbon monoxide, methane and hydrogen sulfide, the convergence error and iteration steps of GA-BP neural network are smaller than BP neural network, and it fits better with the trend of measured toxic and harmful gas concentration, showing better general⁃ ization ability, which can provide effective protection to on-site safe operations after blasting

Key words: toxic and harmful gas, GA-BP, prediction model, tunnel