Development of artificial neural network for condition assessment of bridges based on hybrid decision making method – Feasibility study

Fabianowski, Dariusz; Jakiel, Przemysław; Stemplewski, Sławomir

doi:10.1016/j.eswa.2020.114271

Cited by 19 publications

(16 citation statements)

References 67 publications

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“…The feasibility of NPEM-MEA is verified using Monte Carlo method. Comparative analyses confirmed the accuracy and efficiency of the method.Wei Guo 9 used a time-varying corrosion bridge finite element model developed in OpenSees to assess the operational safety of the train.Dariusz Fabianowski 10 proposed a neural network model for bridge inspection and applied the model to a Polish railway that has been in operation for more than 100 years, and the results were compared with those obtained using EA FAHP. The accuracy of the model was verified by comparing the results with those obtained using the EA FAHP + DAHP model.N.…”

Section: Introductionmentioning

confidence: 99%

Investigation into the Damage State and Safety Assessment of Concrete Bridge Deck Slabs

Ding,

Li,

Yang

et al. 2024

Preprint

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The present study introduces an analytical approach grounded in fuzzy hierarchical analysis, aimed at detecting and analyzing bridge deck deterioration, with the overarching objective of enhancing the efficiency and safety of bridge maintenance operations.This methodology assesses the structural integrity of bridge deck slabs by scrutinizing variables including traffic volume, service life, traffic duration, temperature, and other pertinent factors, followed by meticulous weight calculation.Employing finite element modeling and computational techniques, this study delves into the analysis of principal stress and displacement distribution across the bridge deck, both pre- and post-reinforcement measures.This analysis serves as a pivotal reference point for maintenance endeavors, ensuring the structural integrity and safety of the bridge infrastructure.Moreover, this approach enables prompt implementation of repair and reinforcement measures, mitigating the risk of accidents and concurrently augmenting the efficacy and safety standards of bridge maintenance protocols.Given the pivotal role of transportation infrastructure in societal development, the efficacy of such measures bears significant ramifications for overall transport advancement.

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Section: Introductionmentioning

confidence: 99%

Investigation into the Damage State and Safety Assessment of Concrete Bridge Deck Slabs

Ding,

Li,

Yang

et al. 2024

Preprint

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“…Therefore, it is crucial to ensure that the parameters of safety level, suitability for operation, and durability correspond to this indicator. In the study [5], a critical overview of models for calculating and predicting the technical condition of bridges based on the reliability parameter is provided. It is emphasized that most models are based on determining reliability for elements with a standard set of constructions and materials.…”

Section: Introductionmentioning

confidence: 99%

“…It is emphasized that most models are based on determining reliability for elements with a standard set of constructions and materials. The authors of [5] propose a hybrid calculation model that combines a modified process of fuzzy analytical hierarchy analysis (EA FAHP) and dominant analytical hierarchy process (DAHP). However, the authors note the complexity of calculations using their developed model for practicing engineers.…”

Section: Introductionmentioning

confidence: 99%

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Methodology for Assessing the Technical Condition and Durability of Bridge Structures

Medvediev,

Kharchenko,

Stakhova

et al. 2024

Infrastructures

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The proposed methodology aims to determine and forecast the technical condition of bridge elements, which could serve as an advanced engineering tool for assessing reliability and durability. It is developed based on fundamental studies that synthesize the experience of studying the physical–mechanical and physical–chemical properties of materials in bridge structures operating under real conditions. The theoretical foundation of the methodology is a reliability model and residual lifetime prediction of bridge elements based on Markov’s theory. The developed methodology is designed for assessing the technical condition of individual bridge elements, followed by a comprehensive evaluation of the entire structure. Reliability during operation is adopted as the indicator of technical condition. This quantitative reliability indicator in the model serves as a criterion for evaluating the safety level of bridge elements; ranking of bridge elements as necessary for specific types of repair, reconstruction, or replacement; strategic planning of expenditures for repair or reconstruction under limited funding; and forecasting the remaining resource of elements. An evaluation and prediction algorithm for the technical condition of bridges is proposed for the application of the developed methodology. A mathematical experiment of the developed methodology was conducted, which confirmed the adequacy of the proposed hypothesis, i.e., the use of the reliability model and the prediction of residual lifetime of bridge elements. First, a three-step mechanism for refining the technical condition of the bridge is proposed, significantly enhancing the accuracy of the calculations. Therefore, the developed methodology holds practical value and can serve as a basis for information-analytical systems for managing the condition of bridges.

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“…Since the neural network has the advantages of high computational efficiency and strong nonlinear fitting ability (Schmidhuber, 2015;Dong et al, 2021;Shi et al, 2022), it can well reflect the nonlinear characteristic relationship in the process of scheme decision making, so as to be closer to the real scheme selection actual scene. Fabianowski et al (2021) developed a neural network model for the decision-making of bridge management, and ranked the alternatives. Compared with the hybrid algorithm of Extent Analysis Fuzzy Analytic Hierarchy Process (EAFAHP) and Dominant Analytic Hierarchy Process (DAHP), the high accuracy of the neural network was verified.…”

Section: Introductionmentioning

confidence: 99%

A decision-making method for reservoir operation schemes based on deep learning and whale optimization algorithm

Lin

et al. 2023

Front. Plant Sci.

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Reservoir operation is an important part of basin water resources management. The rational use of reservoir operation scheme can not only enhance the capacity of flood control and disaster reduction in the basin, but also improve the efficiency of water use and give full play to the comprehensive role the reservoir. The conventional decision-making method of reservoir operation scheme is computationally large, subjectivity and difficult to capture the nonlinear relationship. To solve these problems, this paper proposes a reservoir operation scheme decision-making model IWGAN-IWOA-CNN based on artificial intelligence and deep learning technology. In view of the lack of data in the original reservoir operation scheme and the limited improvement of data characteristics by the traditional data augmentation algorithm, an improved generative adversarial network algorithm (IWGAN) is proposed. IWGAN uses the loss function which integrates Wasserstein distance, gradient penalty and difference item, and dynamically adds random noise in the process of model training. The whale optimization algorithm is improved by introducing Logistic chaotic mapping to initialize population, non-linear convergence factor and adaptive weights, and Levy flight perturbation strategy. The improved whale optimization algorithm (IWOA) is used to optimize hyperparameters of convolutional neural networks (CNN), so as to obtain the best parameters for model prediction. The experimental results show that the data generated by IWGAN has certain representation ability and high quality; IWOA has faster convergence speed, higher convergence accuracy and better stability; IWGAN-IWOA-CNN model has higher prediction accuracy and reliability of scheme selection.

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Development of artificial neural network for condition assessment of bridges based on hybrid decision making method – Feasibility study

Cited by 19 publications

References 67 publications

Investigation into the Damage State and Safety Assessment of Concrete Bridge Deck Slabs

Investigation into the Damage State and Safety Assessment of Concrete Bridge Deck Slabs

Methodology for Assessing the Technical Condition and Durability of Bridge Structures

A decision-making method for reservoir operation schemes based on deep learning and whale optimization algorithm

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