Study on prediction of temperature characteristic parameters for subway train with multiple lateral openings and tnnnels

doi:10.16265/j.cnki.issn1003-3033.2026.01.1196

Abstract

Abstract:

Existing methods for predicting temperature from subway carriage and tunnel fires mainly rely on physical models and empirical methods that are valid only under narrowly-defined environmental conditions. To solve this issue, this study adopts an artificial-intelligence-based approach. A GA-BPNN network model is constructed by optimizing BPNN using a GA. The GA is employed to global optimize BPNN's weights and thresholds, after which the model is trained to predict the temperature distribution of both the subway carriage and the tunnel, thereby achieving intelligent inversion of the fire temperature field. The results show that, for subway carriage temperature prediction, GA-BPNN model yields a mean absolute error (MAE) of 8.17, a root mean square error (RMSE) of 9.76, and a coefficient of determination (R²) of 0.99. For tunnel temperature prediction, MAE is 3.95, RMSE is 5.63, and R² reaches 0.98. By comparing the results with those of the traditional BPNN, it is found that the GA-BPNN model outperforms the conventional BPNN in both prediction accuracy and generalization capability.

Key words: multiple lateral openings, subway train, temperature prediction, characteristic parameters, back-propagation neural network(BPNN), genetic algorithm(GA)

CLC Number:

X951交通运输安全

WU Zhenkun, PENG Min, ZHU Guoqing, LIU Lu, QIN Dongzi. Study on prediction of temperature characteristic parameters for subway train with multiple lateral openings and tnnnels[J]. China Safety Science Journal, 2026, 36(1): 130-137.

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序号	热释放速率/MW
1	2.5
2	5.0
3	7.5
4	10

序号	长×宽×高/(m×m×m)	网格总数
1	0.1×0.1×0.1	10 368 000
2	0.2×0.2×0.2	1 260 000
3	0.4×0.4×0.4	162 000
4	0.5×0.5×0.5	82 944

迭代次数	RMSE	R²
250	5.403 0	0.971 7
500	5.185 4	0.973 3
1 000	5.617 4	0.970 0
1 500	6.008 9	0.970 1
2 000	6.779 7	0.968 7
3 000	7.283 7	0.964 5