Using deterioration modelling to simulate sewer rehabilitation strategy with low data availability

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.

Developing a Data Quality Evaluation Framework for Sewer Inspection Data

“…Several different deterioration models have been developed to assess pipe conditions. These models can be divided into two groups: (1) function-based models, and (2) data-based models [4,5]. Function-based models use statistical methods such as regressions and Markov chains, while data-based models use artificial intelligence (AI) and machine learning techniques such as artificial neural networks (ANN) or random forests [6][7][8][9][10][11].…”