Condition and criticality-based predictive maintenance prioritisation for networks of bridges

Hadjidemetriou, Georgios M.; Herrera, Manuel; Parlikad, Ajith Kumar

doi:10.1080/15732479.2021.1897146

Cited by 22 publications

(7 citation statements)

References 62 publications

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“…Contemporary assessments of bridge defects are usually carried out by manual inspection methods, which are highly subjective [41]. To solve this problem, sensor-based detection methods and computer-vision-based detection methods can also be used to obtain bridge assessment data [42].…”

Section: Discussionmentioning

confidence: 99%

Ensemble-Learning-Based Prediction of Steel Bridge Deck Defect Condition

Song

2022

Applied Sciences

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This study developed an ensemble-learning-based bridge deck defect condition prediction model to help bridge managers make more rational and informed steel bridge deck maintenance decisions. Using the latest data from the NBI database for 2021, this study first used ADASYN to solve imbalance problems in the data, then built six ensemble learning models (RandomForest, ExtraTree, AdaBoost, GBDT, XGBoost, and LightGBM) and used a grid search method to determine the hyperparameters of the models. The optimal model was finally analyzed using the interpretable machine learning framework, SHAP. The results show that the optimal model is XGBoost, with an accuracy of 0.9495, an AUC of 0.9026, and an F1-Score of 0.9740. The most important factor affecting the condition of steel bridge deck defects is the condition of the bridge’s superstructure. In contrast, the condition of the bridge substructure and the year of bridge construction are relatively minor factors.

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Section: Discussionmentioning

confidence: 99%

Ensemble-Learning-Based Prediction of Steel Bridge Deck Defect Condition

Song

2022

Applied Sciences

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“…A similar approach was undertaken by Zhang et al ( 21 ), combining metrics from the node-place-design model (i.e., public transportation connectivity, pedestrian accessibility and surrounding land usage) using principal component analysis. Hadjidemetriou et al ( 22 , 23 ) combined closeness vitality measures and observed daily traffic flows to develop a hybrid criticality index for maintenance prioritization of road bridges over their lifecycle. Liu et al ( 24 ) simulated a variety of random failures and targeted attacks, such as sequentially attacking stations of the highest passenger loads or centrality metrics, on railway networks in the Chengdu-Chongqing region, China.…”

Section: Current Methods In Assessing Criticality and Failure Propaga...mentioning

confidence: 99%

Simulation and Criticality Assessment of Urban Rail and Interdependent Infrastructure Networks

Wee

Herrera

Hadjidemetriou

et al. 2022

Transportation Research Record: Journal of the Transportation R

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The role of urban infrastructure is becoming increasingly interdependent, resulting in new sources of vulnerability. Infrastructural asset failure can propagate between rail transportation and other infrastructure networks. There remains a lack of academic research focusing on the dynamic simulation of city-wide infrastructure using real-life data to quantify and cross-compare the criticality of assets. This paper aims to bridge this gap by developing a modeling methodology for interdependent urban infrastructure using complex network theory, which serves as a basis for investigating asset criticality and failure propagation. This modeling framework comprises the distribution of resource supply and demand, the topological representation and skeletonization of the infrastructure network, as well as modeling the propagation of asset failures. The framework is thereafter applied to a case study of the exposure of Greater London’s rail transportation network to failures from electricity infrastructure, selected as a representative example of interdependent infrastructures within a large-scale urban metropolitan area. Two time-based criticality metrics are also proposed to measure the topological extent of infrastructural failures and economic impacts resulting from the failure propagation of given initial failure scenarios. The results of the case study demonstrate that these proposed criticality metrics are effective in capturing the dynamics of failure propagation, and that topological metrics in criticality assessment do not always reflect the resulting economic damages of infrastructural failures.

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“…One of the keys to successful asset management is the use of predictive models that allow foreseeing, for different periods, the performance of the asset, taking into account the demand values of exposure. Thus, the subsequent modules related to the time and extent of the necessary maintenance actions depend entirely on the established deterioration model, the consequences triggered in case of failure, and the costs of each type of intervention [5].…”

Section: Introductionmentioning

confidence: 99%

Improvement of the Inspection Interval of Highway Bridges through Predictive Models of Deterioration

et al. 2022

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Bridges have substantial significance within the transport system, considering that their functionality is essential for countries’ social and economic development. Accordingly, a superior level of safety and serviceability must be reached to ensure the operating status of the bridge network. On that account, the recent collapses of road bridges have led the technical–scientific community and society to reflect on the effectiveness of their management. Bridges in a network are likely to share coinciding environmental conditions but may be subjected to distinct structural deterioration processes over time depending on their age, location, structural type, and other aspects. This variation is usually not considered in the bridge management predictions. For instance, the Brazilian standards consider a constant inspection periodicity, regardless of the bridges’ singularities. Consequently, it is helpful to pinpoint and split the bridge network into classes sharing equivalent deterioration trends to obtain a more precise prediction and improve the frequency of inspections. This work presents a representative database of the Brazilian bridge network, including the most relevant data obtained from inspections. The database was used to calibrate two independent predictive models (Markov and artificial neural network). The calibrated model was employed to simulate different scenarios, resulting in significant insights to improve the inspection periodicity. As a result, the bridge’s location accounting for the differentiation of exposure was a critical point when analyzing the bridge deterioration process. Finally, the degradation models developed following the proposed procedure deliver a more reliable forecast when compared to a single degradation model without parameter analysis. These more reliable models may assist the decision process of the bridge management system (BMS).

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Condition and criticality-based predictive maintenance prioritisation for networks of bridges

Cited by 22 publications

References 62 publications

Ensemble-Learning-Based Prediction of Steel Bridge Deck Defect Condition

Ensemble-Learning-Based Prediction of Steel Bridge Deck Defect Condition

Simulation and Criticality Assessment of Urban Rail and Interdependent Infrastructure Networks

Improvement of the Inspection Interval of Highway Bridges through Predictive Models of Deterioration

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