Evaluating various machine learning algorithms for automated inspection of culverts

Mohammadi, Pouria; Rashidi, Abbas; Malekzadeh, Masoud; Tiwari, Sushant

doi:10.1016/j.enganabound.2023.01.007

Cited by 22 publications

(1 citation statement)

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“…Three ML algorithms, including artificial neural network (ANN), support vector machine (SVM), and DT, were frequently employed. These models were developed for many applications, such as culverts' remaining service life estimation [9,10,17], predicting specific culvert failure or deterioration types [8,11,12,[18][19][20], coupling with digital image correlation (DIC) techniques to identify and analyze structural defects [21,22], and predicting the condition of other types of transportation assets [23][24][25][26]. For model evaluation, commonly used metrics are accuracy, recall, precision, F-score, and receiver operating characteristic (ROC) curve for classification.…”

Section: Models For Culvert Condition Predictionmentioning

confidence: 99%

Machine Learning and Statistical Test–Based Culvert Condition Impact Factor Analysis

Gao,

Li,

Elzarka

et al. 2024

Advances in Civil Engineering

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For managers of road infrastructure, culvert deterioration is a major concern since culvert failures can cause serious risks to the traveling public. The efficiency of the cost- and labor-intensive culvert inspection and maintenance process can be improved by properly identifying the key impact factors on culvert condition deterioration. Although the use of machine learning (ML) techniques to predict culvert conditions has been proven to be a promising tool for enhancing culvert management and enabling proactive scheduling of maintenance tasks, the information provided by the developed ML models has been given little attention for further use and analysis. By utilizing the predictor importance results of an evaluated decision tree (DT) culvert condition prediction model and the Mann–Whitney U test, this study provided insights to the identification of the key variables influencing culvert deterioration. According to the findings, five impact factors, including culvert span, pH, age, rise, and cover height, often have significant impact on the condition ratings of culverts made of various materials. In addition, such a statistical test-assisted factor identification process offered a way of identifying and enhancing the input variable selection for predictive ML model development.

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