Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, 971 87 Luleå, Sweden.
Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, 971 87 Luleå, Sweden.
Water Res. 2021 Apr 15;194:116934. doi: 10.1016/j.watres.2021.116934. Epub 2021 Feb 15.
Efficient management of sewer blockages requires increased preventive maintenance planning. Conventional approaches to the management of blockages in sewer pipe networks constitute largely unplanned maintenance stemming from a lack of adequate information and diagnosis of blockage causative mechanisms. This study mainly investigated a spatial statistical approach to determine the influence of explanatory factors on increased blockage propensity in sewers based on spatial heterogeneity. The approach consisted of the network K-function analysis, which provided an understanding of the significance of the spatial variation of blockages. A geographically-weighted Poisson regression then showed the degree of influence that explanatory factors had on increased blockage propensity in differentiated segments of the sewer pipe network. Lastly, blockage recurrence predictions were carried out with Random Forest ensembles. This approach was applied to three municipalities. Explanatory factors such as material type, number of service connections, self-cleaning velocity, sagging pipes, root intrusion risk, closed-circuit television inspection grade and distance to restaurants showed significant spatial heterogeneity and varying impacts on blockage propensity. The Random Forest ensemble predicted blockage recurrence with 60-80% accuracy for data from two municipalities and below 50% for the last. This approach provides knowledge that supports proactive maintenance planning in the management of blockages in sewer pipe networks.
高效管理下水道堵塞需要增加预防性维护计划。传统的下水道管网堵塞管理方法主要是由于缺乏足够的信息和堵塞原因机制的诊断而进行的非计划性维护。本研究主要探讨了一种空间统计方法，以确定基于空间异质性的解释因素对下水道堵塞倾向增加的影响。该方法包括网络 K 函数分析，该分析提供了对堵塞空间变化意义的理解。然后，地理加权泊松回归显示了解释因素对下水道管网不同部分堵塞倾向增加的影响程度。最后，使用随机森林集进行了堵塞复发预测。该方法应用于三个城市。解释因素，如材料类型、服务连接数量、自清洁速度、下垂管道、根系入侵风险、闭路电视检查等级和与餐馆的距离，表现出显著的空间异质性和对堵塞倾向的不同影响。随机森林集对两个城市的数据进行堵塞复发预测的准确率为 60-80%，对最后一个城市的数据预测准确率低于 50%。该方法提供了支持下水道管网堵塞管理中主动维护规划的知识。
