Defect based deterioration model for sewer pipelines using Bayesian belief networks
Defect based deterioration model for sewer pipelines using Bayesian belief networks
复制标题
使用贝叶斯信念网络的下水道管道基于缺陷的恶化模型
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
T. Zayed
中科院分区：
文献类型：
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作者：
Mohamed Elmasry;A. Hawari;T. Zayed
本文提出了一种基于缺陷的劣化模型，以概率方式确定污水管道的状态等级。使用贝叶斯信念网络（BBN）建立了一个静态模型，该模型使用了从现有污水管网观测到的发生概率和条件概率。利用逻辑回归作为构建动态贝叶斯信念网络（DBN）所需的时间链接，将时间维度引入所建立的贝叶斯信念网络模型。使用实际数据检验模型预测的准确性，其中BBN模型的平均绝对误差和均方根误差在结构、操作和总体条件下分别为0.67、1.06、0.56和1.05、1.60、0.95。对于DBN模型，管道将达到某个条件状态的年份所获得的值接近验证数据集的实际值。
A defect based deterioration model to determine the condition ratings in a probabilistic manner for sewer pipelines is presented in this paper. Bayesian belief network (BBN) is used to develop a static model using probabilities of occurrences, and conditional probabilities from observations of existing sewage network. Time dimension is introduced to the developed BBN model by using logistic regression as temporal links required to construct a dynamic Bayesian belief network (DBN). The accuracy of the model’s prediction is examined using actual data where the mean absolute error and root mean square error for the BBN model resulted in values of 0.67, 1.06, 0.56 and 1.05, 1.60, 0.95 for structural, operational, and overall conditions, respectively. As for the DBN model, values achieved for the year at which a pipeline would reach a certain condition state were close to the actual values from the validation dataset.