ABSTRACT
Most cities face the problem of an aging infrastructure in need of extensive and ongoing repair, renovation or replacement. Since the 1980s, CCTV has been the industry standard for sewer system inspection and the main source of information for structural performance evaluation. Due to low inspection rates and the lack of information about sewer condition, deterioration models have been developed to simulate the condition of non-inspected sewers and assess the influence of several rehabilitation scenarios. This paper presents an innovative modelling tool for long-term sewer rehabilitation planning based on the integration of a deterioration and a rehabilitation model. The tool is demonstrated in full scale using CCTV and sewer data of the city of Sofia, in Bulgaria. Results provide tangible proofs of investment needs for sewer rehabilitation and support the utility in the negotiation of budgets with the municipality. Since age is one key variable for deterioration modelling, a new method is proposed to estimate missing construction years in the utility database with a prediction error of less than 7 years.
Subject(s)
Models, Theoretical , Sewage , CitiesABSTRACT
The present study aims to explore the relationship between rainfall variables and water quality/quantity characteristics of combined sewer overflows (CSOs), by the use of multivariate statistical methods and online measurements at a principal CSO outlet in Berlin (Germany). Canonical correlation results showed that the maximum and average rainfall intensities are the most influential variables to describe CSO water quantity and pollutant loads whereas the duration of the rainfall event and the rain depth seem to be the most influential variables to describe CSO pollutant concentrations. The analysis of partial least squares (PLS) regression models confirms the findings of the canonical correlation and highlights three main influences of rainfall on CSO characteristics: (i) CSO water quantity characteristics are mainly influenced by the maximal rainfall intensities, (ii) CSO pollutant concentrations were found to be mostly associated with duration of the rainfall and (iii) pollutant loads seemed to be principally influenced by dry weather duration before the rainfall event. The prediction quality of PLS models is rather low (R² < 0.6) but results can be useful to explore qualitatively the influence of rainfall on CSO characteristics.
