Predicting Bridge Conditions in Ontario: A Case Study

Taghaddos, Maedeh; Mohamed, Yasser A.

doi:10.22260/isarc2019/0023

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…Firstly, they have been successful in data-driven structural health-monitoring [63][64][65][66][67] and investigating infrastructure problems in previous studies [28,33,68,69]. Secondly, the existing studies have demonstrated the effectiveness of Random Forest [70,71], XGboost [22,28], and ANN [25,29,33,72,73] in capturing the complex and nonlinear relationships among the variables involved in predicting bridge condition rating. The following is a general overview of the models trained and evaluated in this study.…”

Section: Results Of Training and Evaluating Modelsmentioning

confidence: 99%

Development and Utilization of Bridge Data of the United States for Predicting Deck Condition Rating Using Random Forest, XGBoost, and Artificial Neural Network

Fard,

Sadeghi Naieni Fard

2024

Remote Sensing

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Accurately predicting the condition rating of a bridge deck is crucial for effective maintenance and repair planning. Despite significant research efforts to develop deterioration models, the efficacy of Random Forest, eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) in predicting the condition rating of the nation’s bridge decks has remained unexplored. This study aims to assess the effectiveness of these algorithms for deck condition rating prediction at the national level. To achieve this, the study collected bridge data, which includes National Bridge Inventory (NBI), traffic, and climate regions gathered using Geospatial Information Science (GIS) and remote sensing techniques. Two datasets were collected: bridge data for a single year of 2020 and historical bridge data covering a five-year period from 2016 to 2020. Three models were trained using 319,404 and 1,246,261 bridge decks in the single-year bridge data and the five-year historical bridge data, respectively. Results show that the use of historical bridge data significantly improves the performance of the models compared to the single-year bridge data. Specifically, the Random Forest model achieved an overall accuracy of 83.4% and an average F1 score of 79.7%. In contrast, the XGBoost model achieved an overall accuracy of 79.4% and an average F1 score of 77.5%, while the ANN model obtained an overall accuracy of 79.7% and an average F1 score of 78.4%. Permutation-based variable importance reveals that NBI, traffic, and climate regions significantly contribute to model development. In conclusion, the Random Forest, XGBoost, and ANN models, trained using updated historical bridge data, provide useful tools for accurately predicting the condition rating of bridge decks in the United States, allowing infrastructure managers to efficiently schedule inspections and allocate maintenance resources.

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Section: Results Of Training and Evaluating Modelsmentioning

confidence: 99%

Development and Utilization of Bridge Data of the United States for Predicting Deck Condition Rating Using Random Forest, XGBoost, and Artificial Neural Network

Fard,

Sadeghi Naieni Fard

2024

Remote Sensing

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“…In particular, the existing method underestimates the occurrence probabilities of DC 2 and DC 3 in the latter part of the bridge life (after approximately 20 years of bridge age), which can be attributed to the fact that the deterioration process estimated by the existing method is not time dependent. In Taghaddos and Mohamed (2019), a condition with a BCI value of 70 or less is defined as a condition requiring maintenance. In this study, although it is a slightly pessimistic assessment, it is supposed that DC 3, which has a BCI of 75 or less as shown in Table 3, is a condition that requires maintenance.…”

Section: Comparison With Homogeneous Ctmcmentioning

confidence: 99%

“…In such processes, it cannot be denied that predicting deterioration for each bridge element may result in an overly detailed analysis. In addition, it is suggested that prediction of entire bridge deterioration is still meaningful because (1) BCI is still being recorded in the Ontario data set used in this study and (ii) analyses that predicted deterioration by BCI for entire bridges have been actively conducted in recent years (Taghaddos & Mohamed, 2019). For these reasons, the authors decided to use the BCI for the entire bridge as a deterioration index in this real‐world example, while also recognizing the importance of predicting deterioration for each bridge element.…”

Section: Real‐world Examplementioning

confidence: 99%

Infrastructure deterioration modeling with an inhomogeneous continuous time Markov chain: A latent state approach with analytic transition probabilities

Mizutani

Yuan

2023

Computer aided Civil Eng

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Markov chains have been widely used to characterize performance deterioration of infrastructure assets, to model maintenance effectiveness, and to find the optimal intervention strategies. For long‐lived assets such as bridges, the time‐homogeneity assumptions of Markov chains should be carefully checked. For this purpose, this research proposes a regime‐switching continuous‐time Markov chain of which the state transition probabilities depend on another, latent, Markov chain that characterizes the overall aging regime of an asset. With the aid of a state‐augmentation technique, closed‐form solutions for the transition probabilities are analytically derived, making the statistical analysis simple. A case study is presented using the open Ontario Bridge Condition data for provincial highway bridges. The case study demonstrates that the proposed method allows to (1) estimate a statistically superior model to the homogeneous Markov chain and (2) obtain results with comparable accuracy in approximately 48% of the computation time of the state‐of‐the‐art inhomogeneous Markov chain.

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Evaluating Deterioration of Tunnels Using Computational Machine Learning Algorithms

Ahmed

Khalef

Ali

et al. 2021

J. Constr. Eng. Manage.

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Predicting Bridge Conditions in Ontario: A Case Study

Cited by 6 publications

References 5 publications

Development and Utilization of Bridge Data of the United States for Predicting Deck Condition Rating Using Random Forest, XGBoost, and Artificial Neural Network

Development and Utilization of Bridge Data of the United States for Predicting Deck Condition Rating Using Random Forest, XGBoost, and Artificial Neural Network

Infrastructure deterioration modeling with an inhomogeneous continuous time Markov chain: A latent state approach with analytic transition probabilities

Evaluating Deterioration of Tunnels Using Computational Machine Learning Algorithms

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