Integration of site condition information using geographic information system for seismic risk reduction for bridge network

Farzam, Azarm; Nollet, Marie‐José; Khaled, Amar

doi:10.1080/17499518.2021.1952609

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…According to statistics in Canada, one-third of bridges have various deficiencies that consume 57% of their designed lifetime [7,8]. For example, Quebec bridges are the most aged, with a nearly 15% higher maintenance consumption rate than national Canadian bridges, which can be attributed to the fact that these bridges were built between the 1960s and 1980s [9,10]. Experts estimate that the total backlog of bridge maintenance, rehabilitation, and replacement will be worth $10 billion [11].…”

Section: Introductionmentioning

confidence: 99%

A Novel Chaotic Optimization‐Oriented Model for Bridge Maintenance and Rehabilitation Planning

Abdelkader,

Ben Seghier,

Elshaboury

et al. 2023

ce papers

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The rise in percentage of deteriorated bridges coupled with the escalation of maintenance costs call for systematic bridge management systems. Therefore, this research paper introduces an exponential chaotic differential evolution (ECDE) model for optimizing bridge maintenance plans at both project and network levels. The developed optimization model is envisioned on addressing the performance, economic, social, and environmental aspects of bridge maintenance management. In addition, it exploits the use of chaotic sequences to circumvent the critical shortcomings of local minima entrapment and premature convergence encountered by classical optimization metaheuristics. The validity of the developed model is experimented using a comparative analysis of a wide set bridge elements and over a varying multi‐year maintenance plan. Comparison results reveal that ECDE‐based Sinusoidal algorithm improve the performance diagnostics of classical meta‐heuristics by values ranging from 49.2% to 73.1% over the multi‐year maintenance plans. It can be argued that the developed model could benefit transportation agencies in formulating flexible, sustainable, and cost‐effective maintenance strategies in addition to preserving the structural condition of designated bridges in the network.

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Section: Introductionmentioning

confidence: 99%

A Novel Chaotic Optimization‐Oriented Model for Bridge Maintenance and Rehabilitation Planning

Abdelkader,

Ben Seghier,

Elshaboury

et al. 2023

ce papers

View full text Add to dashboard Cite

show abstract

“…On the other hand, bridges in Prince Edward Island have the smallest average age, which is 15.6 years. This can be attributed to that about 70% of the bridges in Quebec were built between the 1960s and 1980s (Farzam et al, 2016; Viami International Inc. and the Technology Strategies Group, 2013). The backlog of bridge maintenance, rehabilitation and replacement is estimated to be equal to $10 billion.…”

Section: Introductionmentioning

confidence: 99%

Integrative Evolutionary-Based Method for Modeling and Optimizing Budget Assignment of Bridge Maintenance Priorities

Abdelkader

Moselhi

Marzouk

et al. 2021

J. Constr. Eng. Manage.

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Recently, the number of deteriorating bridges has drastically increased. As such, enormous amount of resources are invested yearly to maintain the performance of bridges within acceptable levels. This entails the development of bridge management systems to manage the imbalance between the extensive needs for maintenance, repair and rehabilitation actions, and the limited available funds. In this regard, the present study introduces three-tier platform to model and allocate limited resources in bridge deck replacement projects. The first model involves building a discrete event simulation model to mimic the bridge deck replacement process. The second encompasses structuring an efficient and straightforward surrogate machine learning model for mimicking the computationally expensive discrete event simulation model. In the second phase, a novel hybrid Elman neural network-Invasive weed optimization model is developed for predicting time, cost, greenhouse gases and utilization rates of resource allocation plans using database generated from the previous model. The third constitutes formulation of a multi-objective differential evolution optimization model subject to the utilization rates of the involved resources and their dispersion. Results manifest superiority in cost prediction accuracies; achieving mean absolute percentage error, mean absolute error and root-mean squared error of 4.873%, 78.466 and 39.515, respectively. Additionally, the developed multiobjective optimization model significantly outperformed a set well-performing meta-heuristics;

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“…This can be explained by the fact that approximately 70% of Quebec's bridges were constructed between the 1960s and 1980s. 1,2,3,4 Visual inspection is considered as the common practice adopted by transportation agencies to monitor condition of the bridge decks. These inspections are carried out at equal time intervals by engineers to evaluate the severities of the bridge defects.…”

Section: Introductionmentioning

confidence: 99%

A Grey Wolf Optimization-Based Method for Segmentation and Evaluation of Scaling in Reinforced Concrete Bridges

Abdelkader

Moselhi

Marzouk

et al. 2021

Int. J. Info. Tech. Dec. Mak.

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Bridges are prone to severe deterioration agents which promote their degradation over the course of their lifetime. Furthermore, maintenance budgets are being trimmed. This state of circumstances entails the development of a computer vision-based method for the condition assessment of bridge elements in an attempt to circumvent the drawbacks of visual inspection-based models. Scaling is progressive local flaking or loss in the surface portion of concrete that affects the functional and structural integrity of reinforced concrete bridges. As such, this research study proposes a self-adaptive three-tier method for the automated detection and assessment of scaling severity levels in reinforced concrete bridges. The first tier relies on the integration of cross entropy function and grey wolf optimization (GWO) algorithm for the segmentation of scaling pixels. The second tier is designated for the autonomous interpretation of scaling area. In this model, a hybrid feature extraction algorithm is proposed based on the fusion of singular value decomposition and discrete wavelet transform for the efficient and robust extraction of the most dominant features in scaling images. Then an integration of Elman neural network and GWO algorithm is proposed for the sake of improving the prediction accuracies of scaling area though optimization of both structure and parameters of Elman neural network. The third tier aims at establishing a unified scaling severity index to assess the extent of severities of scaling according to its area and depth. The developed method is validated through multi-layered comparative analysis that involved performance evaluation comparisons, statistical comparisons and box plots. Results demonstrated that the developed scaling detection model significantly outperformed a set of widely-utilized classical segmentation models achieving mean squared error, mean absolute error, peak signal to noise ratio and cross entropy of 0.175, 0.407, 55.754 and 26011.019, respectively. With regards to the developed scaling evaluation model, it accomplished remarkable better and more robust performance that other meta-heuristic-based Elman neural network models and conventional prediction models. In this context, it obtained mean absolute percentage error, root-mean squared error and mean absolute error 1.513%, 29.836 and 12.066, respectively, as per split validation. It is anticipated that the developed integrated computer vision-based method could serve as the basis of automated, reliable and cost-effective inspection platform of reinforced concrete bridges which can assist departments of transportation in taking effective preventive maintenance and rehabilitation actions.

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Integration of site condition information using geographic information system for seismic risk reduction for bridge network

Cited by 5 publications

References 25 publications

A Novel Chaotic Optimization‐Oriented Model for Bridge Maintenance and Rehabilitation Planning

A Novel Chaotic Optimization‐Oriented Model for Bridge Maintenance and Rehabilitation Planning

Integrative Evolutionary-Based Method for Modeling and Optimizing Budget Assignment of Bridge Maintenance Priorities

A Grey Wolf Optimization-Based Method for Segmentation and Evaluation of Scaling in Reinforced Concrete Bridges

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