Advanced Water Management Centre, The University of Queensland, Australia.
Civil, Architectural and Environmental Engineering Department, Illinois Institute of Technology, USA.
J Environ Manage. 2019 Mar 15;234:431-439. doi: 10.1016/j.jenvman.2018.12.098. Epub 2019 Jan 11.
Concrete corrosion is one of the most significant failure mechanisms of sewer pipes, and can reduce the sewer service life significantly. To facilitate the management and maintenance of sewers, it is essential to obtain reliable prediction of the expected service life of sewers, especially if that is based on limited environmental conditions. Recently, a long-term study was performed to identify the controlling factors of concrete sewer corrosion using well-controlled laboratory-scale corrosion chambers to vary levels of HS concentration, relative humidity, temperature and in-sewer location. Using the results of the long-term study, three different data-driven models, i.e. multiple linear regression (MLR), artificial neural network (ANN), and adaptive neuro fuzzy inference system (ANFIS), as well as the interaction between environmental parameters, were assessed for predicting the corrosion initiation time (t) and corrosion rate (r). This was performed using the sewer environmental factors as the input under 12 different scenarios after allowing for an initiation corrosion period. ANN and ANFIS models showed better performance than MLR models, with or without considering the interactions between environmental factors. With the limited input data available, it was observed that t prediction by these models is quite sensitive, however, they are more robust for predicting r as long as the HS concentration is available. Using the HS concentration as a single input, all three data driven models can reasonably predict the sewer service life.
混凝土腐蚀是污水管道失效的主要机制之一，会显著缩短污水管道的使用寿命。为了便于污水管道的管理和维护，必须能够可靠地预测污水管道的预期使用寿命，特别是如果这是基于有限的环境条件。最近，进行了一项长期研究，使用经过良好控制的实验室规模腐蚀室来改变 HS 浓度、相对湿度、温度和污水管道内位置的水平，以确定控制混凝土污水管道腐蚀的因素。利用长期研究的结果，评估了三种不同的数据驱动模型，即多元线性回归（MLR）、人工神经网络（ANN）和自适应神经模糊推理系统（ANFIS），以及环境参数之间的相互作用，用于预测腐蚀起始时间（t）和腐蚀速率（r）。在考虑或不考虑环境因素相互作用的情况下，在 12 种不同情况下，将污水环境因素作为输入，允许起始腐蚀期后进行预测。ANN 和 ANFIS 模型的性能优于 MLR 模型，无论是否考虑环境因素之间的相互作用。由于可用的输入数据有限，观察到这些模型的 t 预测非常敏感，但是只要 HS 浓度可用，它们在预测 r 方面就更稳健。使用 HS 浓度作为单一输入，所有三种数据驱动模型都可以合理地预测污水管道的使用寿命。
