Sewer deterioration modeling with condition data lacking historical records

WATER RESEARCH

卷:47

Article

Egger, C.^1,2  Scheidegger, A.¹  Reichert, P.^1,3  Maurer, M.^1,2 

[1] Eawag, Swiss Fed Inst Aquat Sci & Technol, CH-8600 Dubendorf, Switzerland

[2] Swiss Fed Inst Technol, Dept Civil Environm & Geomat Engn, Inst Environm Engn, CH-8093 Zurich, Switzerland

[3] Swiss Fed Inst Technol, Dept Environm Syst Sci, Inst Biogeochem & Pollutant Dynam, CH-8092 Zurich, Switzerland

关键词: Deterioration model;    Rehabilitation model;    Data management;    Survival selection bias;    Likelihood;    Bayesian inference;   

DOI  :  10.1016/j.watres.2013.09.010

来源: Elsevier

【 摘 要 】

Accurate predictions of future conditions of sewer systems are needed for efficient rehabilitation planning. For this purpose, a range of sewer deterioration models has been proposed which can be improved by calibration with observed sewer condition data. However, if datasets lack historical records, calibration requires a combination of deterioration and sewer rehabilitation models, as the current state of the sewer network reflects the combined effect of both processes. Otherwise, physical sewer lifespans are overestimated as pipes in poor condition that were rehabilitated are no longer represented in the dataset. We therefore propose the combination of a sewer deterioration model with a simple rehabilitation model which can be calibrated, with datasets lacking historical information. We use Bayesian inference for parameter estimation due to the limited information content of the data and limited identiflability of the model parameters. A sensitivity analysis gives an insight into the model's robustness against the uncertainty of the prior. The analysis reveals that the model results are principally sensitive to the means of the priors of specific model parameters, which should therefore be elicited with care. The importance sampling technique applied for the sensitivity analysis permitted efficient implementation for regional sensitivity analysis with reasonable computational outlay. Application of the combined model with both simulated and real data shows that it effectively compensates for the bias induced by a lack of historical data. Thus, the novel approach makes it possible to calibrate sewer pipe deterioration models even when historical condition records are lacking. Since at least some prior knowledge of the model parameters is available, the strength of Bayesian inference is particularly evident in the case of small datasets. (C) 2013 Elsevier Ltd. All rights reserved.

【 授权许可】

Free