为掌握隧道结构在山区环境中的安全状态，提出一种基于多准则妥协解排序法(VIKOR)决策模型和麻雀搜索算法(SSA)优化极限学习机(ELM)算法的山区隧道结构安全评价方法。通过调研山区隧道结构安全影响因素的文献，建立山区隧道结构安全的评语集、指标体系与指标基准；利用群体决策层次分析法(AHP)、熵权法(EWM)+CRITIC法和博弈论对评价指标进行权重计算；采用VIKOR决策模型对隧道结构安全等级进行量化，并将使用MATLAB生成的构造样本转化为用于机器学习训练的训练样本；根据参数寻优的结果，构建SSA-ELM模型，并收集48个已进行现场勘察并确定安全等级的工程实例样本进行安全预测，同时与未优化的ELM和运用粒子群算法(PSO)优化的ELM模型进行对比分析。结果表明，SSA-ELM模型的预测准确率更高。
Herein, a safety evaluation method of mountainous
tunnel structures is established using an extreme learning machine (ELM)
improved by multi-criteria compromise solution sorting
method (VIKOR) and sparrow search algorithm (SSA). Previous researches on the
influencing factors of tunnel structure safety in mountainous areas are
analyzed to build an evaluation set, indicator system, and benchmarks for the
safety of tunnel structures in mountainous areas. Then, the analytic hierarchy process, entropy
weight method + criteria importance through intercriteria correlation method,
and game theory are employed to calculate the weights of evaluation indicators.
The VIKOR decision model is adopted to quantify the safety levels of tunnel
structures, and the constructed samples generated by MATLAB are transformed
into training samples for machine learning. Based on the parameter optimization
results, an SSA-ELM model is constructed, and 48
engineering instance samples, which had undergone field surveys and safety level
determinations, are collected for simulation prediction. Comparative analysis
is conducted with the original ELM and the particle warm optimization algorithm-optimized ELM model. The results indicate that the SSA-ELM model has higher prediction accuracy.
