Physical characteristics of pipes as indicators of structural state for decision-making considerations in sewer asset management

López‐Kleine, Liliana; Hernández, Nathalie; Torres, Andrés

doi:10.15446/ing.investig.v36n3.56616

Cited by 9 publications

(9 citation statements)

References 13 publications

(15 reference statements)

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“…For this case study, the variables included in the matrix of the object X in both SVM approaches were some sewer characteristics such as material (concrete, clay, and others), depth, length, slope, diameter, age, sewerage type (combined, wastewater, and stormwater), and road type (primary, intermediate, local, rural, and extension of a road). These variables were chosen according to previous studies where potential factors that could influence the structural condition of sewer pipes in the city of Bogota were identified [27]- [29].…”

Section: Svm Regression Approach Methodologymentioning

confidence: 99%

Support Vector Machines Used for the Prediction of the Structural Conditions of Pipes in Bogota’s Sewer System

Hernández

Caradot

Sonnenberg

et al. 2021

IyU

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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.

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Section: Svm Regression Approach Methodologymentioning

confidence: 99%

Support Vector Machines Used for the Prediction of the Structural Conditions of Pipes in Bogota’s Sewer System

Hernández

Caradot

Sonnenberg

et al. 2021

IyU

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“…For example, López‐Kleine et al. (2016) used principal components analysis (PCA) coupled with

k

‐means clustering to determine the relationship between structural characteristics of sewer pipes and their deterioration states. The relationships that were detected in this study helped the authors to identify the variables with a strong influence on the state of pipelines.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For example, in studies by Chughtai and Zayed (2008) and López‐Kleine et al. (2016), atypical observations were marked based on normal probability plots and box plots, respectively, and then were removed from the final dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Second, time and budget constraints limit the inspection capabilities of water utilities as they can only investigate a small section of the infrastructure at a time, rather than inspecting the entire sewer system serving a wide region, city, county, or state. Previous studies have recommended an overall turnover time for a full sewer system inspection, ideally, to be between 2 and 25 years depending on the size, condition/state, and age of the system (McDonald & Zhao, 2001); nevertheless, municipalities often report these times to be doubled or even tripled under real circumstances (Allouche & Freure, 2002; López‐Kleine, Hernández, & Torres, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Although data‐driven techniques, such as statistical learning and machine learning , have gained significant traction to predict sewer system failures, another critical factor that may hinder the prediction quality of these methodologies is the lack of an adequate spatiotemporal analysis in the current state‐of‐practice. Despite the fact that failures in a sewer system infrastructure occur in a spatiotemporal context (i.e., a pipe is more prone to fail if it has failed in the past, has not been replaced, or if its neighboring pipes have also failed), most previous works have considered explanatory variables from an either temporal or spatial perspective, ignoring the high interdependence of these two key dimensions (Duchesne et al., 2013; López‐Kleine et al., 2016; Soriano‐Pulido, Valencia‐Arboleda, & Rodríguez Sánchez, 2019; Younis & Knight, 2010). One of the main reasons why both the spatial and temporal dimensions are typically considered in an isolated way is the absence of comprehensive datasets that provide descriptive information directly regarding these two aspects.…”

Section: Introductionmentioning

confidence: 99%

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A Two‐Stage Data‐Driven Spatiotemporal Analysis to Predict Failure Risk of Urban Sewer Systems Leveraging Machine Learning Algorithms

et al. 2021

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Risk-informed asset management is key to maintaining optimal performance and efficiency of urban sewer systems. Although sewer system failures are spatiotemporal in nature, previous studies analyzed failure risk from a unidimensional aspect (either spatial or temporal), not accounting for bidimensional spatiotemporal complexities. This is owing to the insufficiency of good-quality data, which ultimately leads to under-/overestimation of failure risk. Here, we propose a generalized methodology/framework to facilitate a robust spatiotemporal analysis of urban sewer system failure risk, overcoming the intrinsic challenges of data imperfections-e.g., missing data, outliers, and imbalanced information. The framework includes a two-stage data-driven modeling technique that efficiently models the highly rightskewed sewer system failure data to predict the failure risk, leveraging a bidimensional spacetime approach. We implemented our analysis for Bogotá, the capital city of Colombia. We train, test, and validate a battery of machine learning algorithms-logistic regression, decision trees, random forests, and XGBoost-and select the best model in terms of goodnessof-fit and predictive accuracy. Finally, we illustrate the applicability of the framework in planning/scheduling sewer system maintenance operations using state-of-the-art optimization techniques. Our proposed framework can help stakeholders to analyze the failure-risk models' performance under different discrimination thresholds, and provide managerial insights on the model's adequate spatial resolution and appropriateness of decentralized management for sewer system maintenance.

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Linking sewer condition assessment methods to asset managers’ data-needs

Noshahri

Scholtenhuis

Dorée

et al. 2021

Automation in Construction

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Physical characteristics of pipes as indicators of structural state for decision-making considerations in sewer asset management

Cited by 9 publications

References 13 publications

Support Vector Machines Used for the Prediction of the Structural Conditions of Pipes in Bogota’s Sewer System

Support Vector Machines Used for the Prediction of the Structural Conditions of Pipes in Bogota’s Sewer System

A Two‐Stage Data‐Driven Spatiotemporal Analysis to Predict Failure Risk of Urban Sewer Systems Leveraging Machine Learning Algorithms

Linking sewer condition assessment methods to asset managers’ data-needs

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