基于检测数据更新的混凝土碳化深度预测

Carbonation Depth Prediction of Concrete Structures Based on Inspection Data

混凝土碳化深度预测模型及模型参数的选择均存在不可忽略的主观不确定性和随机性,应用于实际工程时存在显著的误差,而实际检测数据往往样本数量少、缺乏足够的完备性而不能用于实际工程中混凝土碳化深度的预测。以几个碳化深度预测模型计算结果的加权平均值来预测混凝土碳化深度,用贝叶斯方法结合检测信息和先验预测模型,更新预测模型权重的概率分布和相应模型分布参数的概率分布,采用更新后的模型权重和参数后验分布,可以更加准确地对结构的碳化规律进行评估和预测。以一个10a期自然碳化试验结果为例,验证了本方法的有效性。

There are subjective uncertainty and randomness in concrete carbonation depth forecasting model and the model distribution parameters, which cause significant errors in application to practical engineering. Actual inspection data can not often be used to forecast concrete carbonation depth in the actual project due to its small sample size and lack of sufficient completeness. The weighted value of several model calculations was used to forecast the concrete carbonation depth. By using Bayesian approach, the inspection information and the prior prediction model were incorporated, and the prior model weights and model distribution parameters statistics were updated. It is more accurate to forecast the carbonation depth using the updated model weights and model distribution parameters. The procedure for updating the mechanical model selection and distribution parameter statistics was illustrated with a lO-year-long concrete carbonation test.