Systematic Statistical Approach to Populate Missing Performance Data in Pavement Management Systems

Al-Zou'bi, Mazin Moh'd; Chang, Carlos M.; Nazarian, Soheil; Kreinovich, Vladik

doi:10.1061/(asce)is.1943-555x.0000247

Cited by 13 publications

(7 citation statements)

References 18 publications

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“…In 1993, the Texas Department of Transportation (TxDOT) developed the Pavement Management Information Systems (PMIS) to manage their pavement assets and to improve the overall conditions of Texas pavements [13,14]. This database is one of the largest pavement databases in the U.S., containing relevant pavement information for more than 300,000 road sections, each roughly 0.5 m in length [15].…”

Section: Pavement Management Information System (Pmis)mentioning

confidence: 99%

Selecting Network-Level Project Sections for Sustainable Pavement Management in Texas

Kim

Chi

Kim³

2018

Sustainability

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Abstract:In recent years, the increasing gap between available funding and preservation needs has influenced district pavement engineers to select and prioritize projects to effectively use funding. However, currently, projects are often selected after an informal assessment, based on local conditions and local district engineers' experience, in the absence of a statewide systematic process. The primary objective of this study is to determine network-level project sections for effective sustainable pavement management using logistic regression analysis. A large volume of inventory data, documented using pavement-management information systems (PMIS), was used to develop the logistic regression (LR) model for selecting candidate sections. The LR model was subsequently validated using a single 50/50 split sample method. The findings of this study will assist the Austin, Texas, USA district to select and evaluate candidate projects. Furthermore, the study will eventually contribute to improved efficiency in project selection and prioritization by reducing not only the amount of time necessary to review the district PMIS data to identify project candidates, but also the potential for human error.

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Section: Pavement Management Information System (Pmis)mentioning

confidence: 99%

Selecting Network-Level Project Sections for Sustainable Pavement Management in Texas

Kim

Chi

Kim³

2018

Sustainability

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“…This technique is useful when dealing with rail and road data and is based on the idea that because road/rail segments are longitudinally connected if the segments are sufficiently close, the related values of condition indicators will be spatially correlated. For example, to find the value of a condition indicator for a road segment, a weighted mean of the values of the two nearest road segments can be used, where the weights are negatively correlated with the distance of the segments to the target segment (Al-Zou'bi et al 2015). Research on these approaches is not abundant, however.…”

Section: Introductionmentioning

confidence: 99%

“…However, the results by spline interpolation and regression models were not as satisfactory as the other models. Al-Zou'bi et al (2015) categorized the techniques to estimate the missing pavement condition indicators into two groups, namely model-based and model-free techniques. The modelbased techniques, as the name suggests, require a mathematical model to estimate the missing data.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Values of Missing Data Related to Infrastructure Condition States Using Their Spatial Correlation

Moghtadernejad

Jin

Adey

2023

J. Infrastruct. Syst.

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Infrastructure managers consistently monitor the condition of their assets to predict their deterioration speed and determine the optimal time to execute preventive interventions. However, despite the recent progress in more frequent and accurate monitoring of assets and storage of the related results, in practice, real-world data often contains errors and discrepancies such as missing data or faulty entries. This problem can happen owing to collection errors during routine inspections or inconsistency of data storage formats in different years. Because the quality of data plays a significant role in the accuracy of deterioration prediction and the resulting intervention programs, it is important to improve condition state predictions by imputing the values of missing information. This paper examines the efficiency of different models that use the spatial correlation of infrastructure assets in predicting the value of missing data. The models include univariate and multivariate Kriging, a hybrid artificial neural network (ANN)-Kriging model, and the bidirectional long short term memory (bi-LSTM) neural network, which can model the data with spatial correlation or a sequential relationship. The results confirm that the condition indicator values can be estimated with reasonably low levels of error.

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“…One of the foundations of pavement management systems (PMS) is to predict the condition of the pavement network based on historical condition data ( 2 ). Although pavement condition is inspected regularly, historical condition data sets are often incomplete for various reasons such as sensor failure or non-periodic inspection ( 3 ). Missing data are common yet can be problematic in the implementation of predictive modeling.…”

mentioning

confidence: 99%

Missing Pavement Performance Data Imputation Using Graph Neural Networks

Gao

2022

Transportation Research Record: Journal of the Transportation R

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Pavement condition data is important for providing information on the current state of the network and determining the needs of preventive maintenance or rehabilitation treatments. However, the condition data set is often incomplete for various reasons such as measurement errors and non-periodic inspection intervals. Missing data, especially when missing systematically, presents loss of information, reduces statistical power, and introduces biased assessment. Existing practices in pavement management systems (PMS) usually discard entire cases with missing data or impute it through data correlation. This paper proposes a graph-based deep learning framework, convolutional graph neural networks, to tackle the missing data problem in PMS. Unlike other variants of neural networks, the proposed approach is able to capture the spatio-temporal relationship in data and to learn and reconstruct the missing data by combining information among neighboring sections. In the case study, pavement condition data from 4,446 sections managed by Texas Department of Transportation were used. Experiments show that the proposed model was able to outperform standard machine learning models when imputing the missing data.

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Systematic Statistical Approach to Populate Missing Performance Data in Pavement Management Systems

Cited by 13 publications

References 18 publications

Selecting Network-Level Project Sections for Sustainable Pavement Management in Texas

Selecting Network-Level Project Sections for Sustainable Pavement Management in Texas

Estimating the Values of Missing Data Related to Infrastructure Condition States Using Their Spatial Correlation

Missing Pavement Performance Data Imputation Using Graph Neural Networks

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