地下工程混凝土衬砌结构耐久性寿命的智能预测研究

The Artificial Intelligence Prediction Study of Durability Life on Underground Concrete Structures

各类隧道及地下工程混凝土结构的耐久性问题已愈来愈严重,但对其研究工作很少.影响地下结构耐久性的因素较多且较复杂,各种因素对耐久性的作用是交叉影响的.结合实际情况,选取常见的有代表性的几个因素,如与周边岩土介质及施工条件等相关的应力状态及水平、混凝土材料配合比、碳化环境、服役期限等,分析它们交叉影响下地下结构耐久性的劣化状况,结合所建立的耐久性寿命评判准则,预测结构的耐久性寿命.在试验基础上获得各影响因素与碳化深度的相关数据,采用人工智能的方法,建立神经网络预测模型.模型经验证可有效用于对同类工程条件下地下结构耐久性寿命的评判与预测,并可泛化推广至其它各环境影响因素作用下的耐久性寿命的类似分析.

Durability problem of tunnels and other underground concrete structures is becoming more and more serious.But there are few related research results or documents in this field.The influence factors of underground structures durability are relatively numerous and complicated.These factors act one another by the manner of crossed influence.According to actual circumstances,several familiar representative factors,such as stress state and its level interrelated with surrounding rock and soil medium or construction conditions,mixture ratio of concrete materials,carbonization environment,service time,etc,were selected to analyze the durability of underground structures based on the crossed influence of factors,and to predict durability life of structures when the judgment rule of durability life was established.Artificial Neural Networks Model was constituted by gaining the related data of influence factors and carbonization depth based on experiments.The model can be efficiently used in judging and predicting the durability life of underground structures under similar engineering conditions,and can be generalized to the analogous analysis of durability life under the action of other environment influence factors.