Bridge Maintenance Planning Framework Using Machine Learning, Multi-Criteria Decision Analysis and Evolutionary Optimization Models

Jaafaru, Hussaini; Agbelie, Bismark

doi:10.1016/j.autcon.2022.104585

Cited by 8 publications

(6 citation statements)

References 31 publications

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“…For example, Fang et al reported the prediction of web-residual strength of cold-formed stainless steel channel sections under end double-flange loading conditions using a deep confidence network (DBN) [32]; Philip et al used convolutional neural networks (CNN) to accurately identify and classify structural cracks and security [33]; Cardellicchio et al used different machine-learning methods to automatically identify defects in existing reinforced concrete (RC) bridges, opening up new scenarios for road management companies and public organizations to assess the health of road networks [34]. The main advantages of these applications include reduced labor needs, fast data collection, and accurate knowledge of the bridges' conditions without interference to the daily operation of structures [21,35]. However, there are still few studies using more recent machine-learning techniques like boosted algorithms to create more accurate and reliable bridge structural state prediction models [17,36,37].…”

Section: Machine-learning Applications In Bridge Condition Predictionmentioning

confidence: 99%

An Improved Inspection Process and Machine-Learning-Assisted Bridge Condition Prediction Model

Fang,

Hu,

Elzarka

et al. 2023

Buildings

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Bridges have a special place in transportation infrastructures and road networks due to their direct relationship with other places. These structures have the purpose of maintaining the traffic loads of the highway, crossing any obstacle, and performing effective communication between two destinations. Costs associated with bridge maintenance continue to be expensive due to their widespread use and stringent inspection requirements. Many researchers have been working on methods to use machine-learning (ML) techniques to forecast specific situations rather than physically checking bridges as part of the maintenance process in recent years. The practical value of the models has, however, been severely constrained by issues such relatively poor model evaluation results, unstable model performances, and the ambiguous application of established models in real-world scenarios. This work showed a thorough method of bridge condition prediction model building from feature engineering to model evaluation, along with a clear procedure of applying the produced model to actual usage, using data from the United States National Bridge Inventory (NBI) and the Adaboost algorithm. Multiple ML model assessment metrics’ findings revealed that the given model outperformed the majority of earlier studies in terms of values and stability. The case study demonstrated that there is a 30% reduction in the number of bridges that need to be inspected. This study serves as a crucial resource for the practical application of ML approaches in the forecast of the status of civil infrastructure. Additionally, it shows that boosted ML models may be a superior option as modeling algorithms advance. To explore the main influencing aspects of bridge conditions, a predictor importance analysis is also offered.

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Section: Machine-learning Applications In Bridge Condition Predictionmentioning

confidence: 99%

An Improved Inspection Process and Machine-Learning-Assisted Bridge Condition Prediction Model

Fang,

Hu,

Elzarka

et al. 2023

Buildings

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“…The following research paper by Jaafaru and Agbelie [11] combines machine learning, multi-criteria analysis and evolutionary optimization models for bridge maintenance planning. This paper provides a bridge maintenance planning framework considering financial and performance parameters.…”

Section: Relevant Research On Multi-criteria Analysis Applications In...mentioning

confidence: 99%

Definition of Compliance Criterion Weights for Bridge Construction Method Selection and Their Application in Real Projects

Tegos,

Papadopoulos,

Aretoulis

2023

Buildings

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The main research purpose of the present paper is the establishment of certain compliance criteria, applied for the selection of the most appropriate, per case, bridge construction method, as well as the definition of the weights of these criteria. The five basic concrete bridge construction methods considered in this study are: Cast-in-place, Precast I-Girder, Incremental Launching, Advanced Shoring, and Balanced Cantilever. In this context, the choice of construction method in a concrete road bridge project is proposed based on seven compliance criteria which are: safety, economy, durability, construction speed, serviceability, aesthetics, and environmental harmonization. The inclusion of all these criteria is achieved via the decision-making tool of multi-criteria analysis. A notable innovation of the current study is that road bridges are divided into three categories (bridges for highways, national roads, and provincial roads), in accordance with the importance of the road that contains them. Thus, three different sets of weights of criteria are calculated, corresponding to each bridge category. The research method used for this purpose was a structured questionnaire that was distributed to a large number of selected experts in the field of bridges, who come either from academia or the construction industry. The research results showed that the criteria of safety and economy are the most significant according to the experts, while aspects such as the correlations between experts’ profile and their weights were also considered. Finally, the derived criterion weights were applied to two case studies of real bridge projects in Greece.

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“…Bridges aid in the delivery of products and services to various locations around the country. They increase the effectiveness of the transportation system and foster national economic development [1,2]. There are more than 617,000 bridges in the United States.…”

Section: Introductionmentioning

confidence: 99%

“…Since the inspection techniques are expensive, sophisticated, and need precise technologies, they are only used for specific occurrences [5]. Therefore, it is crucial to create trustworthy systems to track the performance condition of bridge infrastructure and to implement appropriate maintenance planning and strategies to extend the bridge lifespan and reduce the lifecycle cost of bridge construction [1,9]. ing train the network using samples with known labels and unlabeled samples, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Using the testing set, the model's accuracy was 90%, 90%, and 89% for the deck, superstructure, and substructure components, respectively. Jaafaru and Agbelie (2022) [1] developed a thorough bridge maintenance planning framework within budgetary and performance limitations. Assessment of the bridge performance, machine learning models, multi-criteria decisionmaking, and a genetic algorithm optimization model were all included in the framework.…”

Section: Introductionmentioning

confidence: 99%

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Scrutinizing the Performances of Hybrid ANN Models for Forecasting Condition of Bridges

Elshaboury,

Ben Seghier,

Abdelkader

et al. 2023

ce papers

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Bridges all over the world are vulnerable to severe deterioration agents meanwhile their maintenance budgets are being tightened. This state of affairs necessitates the establishment of an autonomous deterioration model to predict the performance condition of bridges. This research paper explores the implementation of a set of intelligent data driven models to analyze the future condition ratings of bridge decks. These models comprise support vector machines, Gaussian process regression, regression tree, back propagation artificial neural network, Elman recurrent neural network, cascade forward neural network, long short‐term memory network and deep convolutional neural network. The performance comparison is carried out using five evaluation metrics of root mean squared percentage error, mean absolute percentage error, root mean squared error mean absolute error and relative absolute error. The models herein are developed and validated using the structural deterioration information retrieved from the National Bridge Inventory (NBI). It can be argued that the developed deterioration model could be implemented by departments of transportation to analyze and monitor the performance condition behavior of bridge components over their useful lifetime.

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Bridge Maintenance Planning Framework Using Machine Learning, Multi-Criteria Decision Analysis and Evolutionary Optimization Models

Cited by 8 publications

References 31 publications

An Improved Inspection Process and Machine-Learning-Assisted Bridge Condition Prediction Model

An Improved Inspection Process and Machine-Learning-Assisted Bridge Condition Prediction Model

Definition of Compliance Criterion Weights for Bridge Construction Method Selection and Their Application in Real Projects

Scrutinizing the Performances of Hybrid ANN Models for Forecasting Condition of Bridges

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