Gao Wei, Chen Xin, Chen Dongliang
Key Laboratory of Ministry of Education for Geomechanics and Embankment Engineering, College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, PR China.
J Adv Res. 2019 Jul 5;20:141-152. doi: 10.1016/j.jare.2019.07.001. eCollection 2019 Nov.
A new method for predicting the service life of tunnel structures subject to chloride-induced corrosion using data from real engineering examples and genetic programming (GP) is proposed. As a data-driven method, the new approach can construct explicit expressions of the prediction model. The new method was verified by comparing it with the chloride-ion diffusion model considering eight corrosion influence factors. Moreover, 25 datasets collected from tunnel engineering examples were used to construct the new prediction model considering 17 corrosion influence factors belonged to just one classification of engineering corrosion factors. In addition, the performance of the new model was verified through a comparative study with an artificial neural network (ANN) model which is frequently used in chloride-induced corrosion prediction for reinforced concrete structures. The comparison revealed that both the computational result and efficiency of the GP method were significantly better than those of the ANN model. Finally, to comprehensively analyze the new prediction model, the effects of the two main controlling parameters (population size and sample size) were analyzed. The results indicated that as both the population size and the sample size increased, their effect on the computation error decreased, and their optimal values were suggested as 300 and 20, respectively.
提出了一种利用实际工程实例数据和遗传规划（GP）预测受氯离子侵蚀的隧道结构使用寿命的新方法。作为一种数据驱动的方法，新方法可以构建预测模型的显式表达式。通过与考虑八个腐蚀影响因素的氯离子扩散模型进行比较，验证了该新方法。此外，从隧道工程实例中收集的25个数据集被用于构建考虑17个腐蚀影响因素的新预测模型，这些因素仅属于工程腐蚀因素的一个分类。此外，通过与常用于钢筋混凝土结构氯离子侵蚀预测的人工神经网络（ANN）模型进行对比研究，验证了新模型的性能。比较结果表明，GP方法的计算结果和效率均明显优于ANN模型。最后，为全面分析新的预测模型，分析了两个主要控制参数（种群大小和样本大小）的影响。结果表明，随着种群大小和样本大小的增加，它们对计算误差的影响减小，建议其最优值分别为300和20。
