Bridge maintenance planning framework using machine learning, multi-attribute utility theory and evolutionary optimization models

Jaafaru, Hussaini; Agbelie, Bismark

doi:10.1016/j.autcon.2022.104460

Cited by 16 publications

(7 citation statements)

References 31 publications

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“…In this regard, we propose a conceptual model for OSC-DfMA optimization and automation, which incorporates the proposed OSC-DfMA assessment model, a deep learning model for optimizing the manufacturing-delivering-assembling schedule based on production efficiency and capacity, and a BIM-based data management automation model as follows (refer to Figure 5). According to the literature [35][36][37][38][39][40], AI technologies such as machine learning, deep learning, and other similar technologies can be successfully used in optimization problems. Moreover, previous studies [41][42][43][44] confirm that BIM-enabled applications help automate the DfMA process.…”

Section: Expansion Of the Osc-dfma Assessment Model With Ai And Bimmentioning

confidence: 99%

“…Moreover, previous studies [41][42][43][44] confirm that BIM-enabled applications help automate the DfMA process. Thus, the implementability and applicability of the above-pro- According to the literature [35][36][37][38][39][40], AI technologies such as machine learning, deep learning, and other similar technologies can be successfully used in optimization problems. Moreover, previous studies [41][42][43][44] confirm that BIM-enabled applications help automate the DfMA process.…”

Section: Expansion Of the Osc-dfma Assessment Model With Ai And Bimmentioning

confidence: 99%

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DfMA Integrated Assessment Model for Selecting Optimal Design Alternatives in OSC Projects

Jung,

Lee,

2024

Buildings

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To select the optimal design alternative in off-site construction (OSC) projects, the building industry has turned to design for manufacturing and assembly (DfMA). However, most DfMA developments in the OSC field until now have been on improving the production process in OSC projects and guideline strategies on how to apply them. The application of DfMA guidelines only provides background knowledge to designers on how to design. However, it cannot inspect whether the DfMA concept is fully reflected in a design draft to examine the suitability to the OSC production environment, and it cannot determine the optimal alternative from among multiple design alternatives. Thus, this study developed an integrated assessment model of OSC-DfMA consisting of the OSC-DfMA production suitability assessment model and the OSC-DfMA production efficiency assessment model to support decision-making for selecting the optimal design alternative of an OSC project. In this study, the scope of the main research was limited to precast concrete (PC)-based OSC projects. Firstly, we developed an OSC-DfMA production suitability assessment model to review whether design drafts are suitable in the OSC production environment by applying checklist and matrix techniques. Secondly, we developed an OSC-DfMA production efficiency assessment model to select an optimal alternative in terms of production efficiency among multiple design drafts. Thirdly, we conducted a case study to validate the usefulness of the OSC-DfMA assessment model developed in this study. Finally, we discuss the possibility of using AI technology to consider the facility capacity and resource constraints during the production of OSC building components. The study results are of practical value in providing the basis for expanding the applicability of DfMA by proposing a DfMA assessment model suitable for OSC contexts.

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Section: Expansion Of the Osc-dfma Assessment Model With Ai And Bimmentioning

confidence: 99%

Section: Expansion Of the Osc-dfma Assessment Model With Ai And Bimmentioning

confidence: 99%

DfMA Integrated Assessment Model for Selecting Optimal Design Alternatives in OSC Projects

Jung,

Lee,

2024

Buildings

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“…In the engineering field, several aspects regarding the traffic load model are of great concern, including the accuracy of the load effect analysis, the complexity of the calculation process, and the rationality of the calculation scheme. The application of optimization algorithms in the field of bridge engineering can solve design problems with room for improvement, thus significantly increasing the accuracy of the analysis, helping engineers to better understand and predict the behavior of bridges, and improving the efficiency and safety of bridge design and maintenance [9][10][11]. Commonly used optimization algorithms include the genetic algorithm, the particle swarm optimization algorithm, the simulated annealing algorithm, and so on [12,13].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Traffic Load Model for Suspenders of a Super-Long-Span Suspension Bridge Considering Influence Line Geometry and Extreme Load Effect Scenarios

Wei,

Ruan,

et al. 2024

Applied Sciences

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The reasonable expression of live load and its accuracy are important to the safety and design rationality of highway bridge structures. In this study, the optimization issue of the traffic load model for the suspenders of large-scale suspension bridges is studied. Taking a 2300-m main span suspension bridge as an example, a method for suspender classification based on the geometric feature of the influence lines is proposed, and the extreme traffic load effect scenarios are analyzed and used as an optimization reference. Multi-objective optimization based on a genetic algorithm is used to explore the improvement of the traffic load model of the suspender. The traffic load model of the suspender is optimized with three objectives, i.e., accuracy, convenience, and improvement, and the optimization results regarding the load value and loading length are obtained. The value of the uniformly distributed load of the optimized model ranges from 6.4 kN/m to 8.9 kN/m, and the maximum value of the concentrated force could reach 1433 kN. By comparing the obtained optimized model with the current specification model and the extreme load effect scenario model, the improved applicability of the optimized model in the analysis of the load effect of the suspender can be verified. The optimized method and relevant conclusions can provide useful references for the improved design and operation management of similar bridge structures.

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“…Machine learning (ML)based models, in particular, have the capacity to learn from historical data and forecast future structural performance with high accuracy. For example, Jaafaru and Agbelie [12] introduced a bridge maintenance planning framework (BMPF) that combines random forest (RF) algorithm, multi-attribute utility theory, and genetic algorithms to help engineers evaluate and maintain bridges effectively. The study analyzed 95 bridges, achieving an 84% accuracy in ML predictions.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Driven Ontological Knowledge Base for Bridge Corrosion Evaluation

Jiang,

Li,

Yang

et al. 2023

IEEE Access

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In bridge maintenance, assessing structural performance requires adherence to rules outlined in safety and regulatory standards which can be effectively and formally represented in both human and machine-readable formats using ontologies. However, ontology-based semantic inference alone falls short when faced with the complicated mathematical operations required for structural analysis. The increasing digitization of bridge engineering has opened doors to data-driven prediction methods. Machine learning (ML)-based models, in particular, have the capacity to learn from historical data and forecast future structural performance with remarkable accuracy. This paper introduces an innovative approach that integrates ML models with an ontological knowledge base for evaluating bridge corrosion. Web Ontology Language and Semantic Web Rule Language are combined to develop the knowledge base. Random forest algorithm is used to train the ML model with a good agreement (coefficient of determination of 0.989 and root mean square error of 1.200). A Python-based module is designed to seamlessly integrate ML predictions with ontologybased semantic inference. The proposed approach not only infers the corrosion ratings based on the rules defined in the Network Rail standard, but also infers the structural safety performance based on predicted structural response under the action of corrosion. To demonstrate the effectiveness of the developed method in enabling accurate and rational evaluations, a real bridge in the UK is showcased as a practical application.

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Bridge maintenance planning framework using machine learning, multi-attribute utility theory and evolutionary optimization models

Cited by 16 publications

References 31 publications

DfMA Integrated Assessment Model for Selecting Optimal Design Alternatives in OSC Projects

DfMA Integrated Assessment Model for Selecting Optimal Design Alternatives in OSC Projects

Optimization of the Traffic Load Model for Suspenders of a Super-Long-Span Suspension Bridge Considering Influence Line Geometry and Extreme Load Effect Scenarios

Machine Learning-Driven Ontological Knowledge Base for Bridge Corrosion Evaluation

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