The paper presents possibilities of application of selected regression methods (classification trees, support vector machines, K-nearest neighbours, artificial networks) for classification of sewers’ damages. Operational data from the time span 2006–2011 obtained from water utility were used for deterioration analysis. On the basis of the following independent variables, the modelling was carried out: diameter, depth, year of construction, material and season of damage’s occurring. The following kinds of damages were classified: corrosion, crack, longitudinal crack, displacement, unsealing, failure, collapse. The main aim of the paper was to check if prediction methodology could be useful for classification of different kinds of sewers’ damages. The obtained results pointed out that proposed classification methods are not appropriable in quality analysis of registered damages of sewers. Moreover, it is recommended for water and sewerage companies to register types of failures using unified notation which make easier preliminary classification before applying modelling approach. The calculations were performed in Statistica 13.1 software.

本文提出了应用所选回归方法（分类树、支持向量机、K-最近的邻居，人工网络）用于对下水道的损坏进行分类。从供水公司获得的 2006 年至 2011 年时间跨度的运行数据用于劣化分析。在以下自变量的基础上，进行了建模：直径、深度、建造年份、材料和损坏发生的季节。损坏类型分为腐蚀、裂纹、纵向裂纹、位移、开封、失效、倒塌。该论文的主要目的是检查预测方法是否可用于对不同类型的下水道损坏进行分类。所得结果表明，所提出的分类方法不适用于下水道登记损坏的质量分析。而且，建议供水和污水处理公司使用统一符号记录故障类型，以便在应用建模方法之前更容易进行初步分类。计算在 Statistica 13.1 软件中进行。