Abstract
Objective: this paper focused on: (i) developing a deterioration model based on support vector machines (SVM) from its regression approach to separate the prediction of the structural condition of sewer pipes from a classification by grades and predict the scores obtained by failures found in CCTV inspections; and (ii) comparing the prediction results of the proposed model with the ones obtained by a deterioration model based on SVM classification tasks to explore the advantages and disadvantages of their predictions from different perspectives. Materials and methods: The sewer network of Bogota was the case study for this work in which a dataset consisting of the characteristics of 5031 pipes inspected by CCTV (obtained by GIS) was considered, as well as information on external variables (e.g., age, sewerage, and road type). Probability density functions (PDF) were used to convert the scores given by failures found in CCTV into structural grades. In addition, three techniques were used to evaluate the predictions from different perspectives: positive likelihood rate (PLR), performance curve and deviation analysis. Results: it was found that: (i) SVM-based deterioration model used from its regression approach is suitable to predict critical structural conditions of uninspected sewer pipes because this model showed a PLR value around 6.8 (the highest value among the predictions of all structural conditions for both models) and 74 % of successful predictions for the first 100 pipes with the highest probability of being in critical conditions; and (ii) SVM-based deterioration model used from its classification approach is suitable to predict other structural conditions because this model showed homogeneous PLR values for the prediction of all structural conditions (PLR values between 1.67 and 3.88) and deviation analysis results for all structural conditions are lower than the ones for the SVM-based model from its regression approach.
Highlights
Urban drainage systems present alarming rates of aging and deterioration in both developed and developing countries [1], [2]
Deterioration models based on support vector machines (SVM) have had successful predictions for case studies with low frequency of inspections [12], [15] using its classification task
SVM uses its classification approach in predicting the structural condition of sewer assets because most of the assessment standards classify the structural condition in a categorical variable [16]
Summary
Urban drainage systems present alarming rates of aging and deterioration in both developed and developing countries [1], [2]. The deterioration models developed and applied in other studies usually are based on classification methods because of the classification in grades of structural condition [7], [4], [8]–[12], [15]. As the case of Bogota has a second rate, Batch assignment of parallel machines in an automotive safety glass manufacturing facility as a numerical variable (scores), it sparks our interest to develop a deterioration model based on a regression approach using the scores, separating the aggrupation of structural condition from grades that could increase uncertainty in the prediction. Since deterioration models based on SVM have two prediction tasks and have had successful predictions with its classification task for case studies with low inspection rates, the authors propose a methodology based on the SVM’s regression task to predict the structural condition of the sewer assets. This work suggests (i) a methodology, applying SVM from its regression approach (SVM_R) to predict the structural condition of the uninspected pipes of the sewer system of Bogota and (ii) a comparison between the prediction results of the SVM_R methodology and the prediction results using classification with SVM (SVM_CL) for the same case study
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