On the modeling of the annual corrosion rate in main cables of suspension bridges using combined soft computing model and a novel nature-inspired algorithm

Seghier, Mohamed El Amine Ben; Corriea, José A. F. O.; Jafari‐Asl, Jafar; Malekjafarian, Abdollah; Plevris, Vagelis; Trung, Nguyen Thoi

doi:10.1007/s00521-021-06199-w

Cited by 40 publications

(18 citation statements)

References 43 publications

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“…hybrid genetic algorithm-based artificial neural network was used for the study. Seghier et al [26] used a integrated soft computing technique with nature inspired algorithm for determining corrosion rate in main cables of suspension bridge. The model was developed using multilayer perceptron technique optimized with marine predators algorithm.…”

Section: Literaturementioning

confidence: 99%

Application of Soft Computing Techniques in Bridge Health Assessment: A Review

Khan¹,

Topdar²,

Datta³

2023

Data Science and Intelligent Computing Techniques

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Bridges are complex structures of highway networks. Regular inspection and maintenance are required to keep these structures safe and functional. Decision-makers often face challenges in selecting maintenance and repair plans for the massive backlog of deteriorated bridge structures within limited budget. Several approaches are available for the health evaluation of bridges, such as visual inspection, non-destructive tests, and sensors. However, the information is somewhat uncertain no matter what the inspection procedure used to gather the health information of bridges. Further, uncertainty in the collected data hampers the reliability of the evaluation process. Therefore, there is a lack of reliable procedures for prioritizing bridges using present health conditions. So, computational techniques are required to tackle the imprecision and uncertainty of bridge inspection data and provide reliable bridge maintenance and management information. In this context, soft computing is a useful technique for extracting meaningful information from a dataset inherent uncertainty. Hence, this study attempted to review the current state of the application of soft computing techniques in bridge health assessment. The articles between 2019 to 2022 were reviewed. Different soft computing techniques were found to be compatible with different inspection data types (visual inspection data, NDT data, or sensor data). The current review found that applying a specific soft computing technique to a particular data type is helpful for reliable health assessment.

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Section: Literaturementioning

confidence: 99%

Application of Soft Computing Techniques in Bridge Health Assessment: A Review

Khan¹,

Topdar²,

Datta³

2023

Data Science and Intelligent Computing Techniques

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“…In past studies, this diagram has been used to compare estimating methods. 108,109 This type of diagram combines several indices in order to present how the predicted values are matched against the real measurements. Using three error criteria, standard deviation, correlation coefficient, and RMSE, Taylor diagram was plotted for the total specimens as displayed in Figure 6.…”

Section: Taylor Diagrammentioning

confidence: 99%

Estimating the compressive strength of rectangular fiber reinforced polymer–confined columns using multilayer perceptron, radial basis function, and support vector regression methods

Moodi

Ghasemi

Mousavi

2021

Journal of Reinforced Plastics and Composites

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Recently, there has been a tendency to use machine learning (ML)–based methods, such as artificial neural networks (ANNs), for more accurate estimates. This paper investigates the effectiveness of three different machine learning methods including radial basis function neural network (RBNN), multi-layer perceptron (MLP), and support vector regression (SVR), for predicting the ultimate strength of square and rectangular columns confined by various FRP sheets. So far, in the previous study, several experiments have been conducted on concrete columns confined by fiber reinforced polymer (FRP) sheets with the results suggesting that the use of FRP sheets enhances the compressive strength of concrete columns effectively. Also, a wide range of experimental data (including 463 specimens) has been collected in this study for square and rectangular columns, confined by various FRP sheets. The comparison of ML-derived results with the experimental findings, which were in a very good agreement, demonstrated the ability of ML to estimate the compressive strength of concrete confined by FRP; the correlation coefficient (R2) for MLP, RBFNN, and SVR methods was equal to 0.97, 0.97, and 0.90, respectively. Similar accuracy was obtained by MLP and RBFNN, and they provided better estimates for determining the compressive strength of concrete confined by FRP. Also, the results showed that the difference between statistical indicators for training and testing specimens in the RBFNN method was greater than the MLP method, and this difference indicated the poor performance of RBFNN.

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“…With the development of artificial intelligence (AI) technology, the use of deep learning methods to obtain better corrosion predictions for oil and gas pipelines has also become a focus of current research [10][11][12]. For example, Jain et al [13] proposed a quantitative evaluation model for the external corrosion rate of oil and gas pipelines based on Bayesian networks.…”

Section: Introductionmentioning

confidence: 99%

Metal Corrosion Rate Prediction of Small Samples Using an Ensemble Technique

Yang¹,

Zheng²,

Zeng³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Accurate prediction of the internal corrosion rates of oil and gas pipelines could be an effective way to prevent pipeline leaks. In this study, a proposed framework for predicting corrosion rates under a small sample of metal corrosion data in the laboratory was developed to provide a new perspective on how to solve the problem of pipeline corrosion under the condition of insufficient real samples. This approach employed the bagging algorithm to construct a strong learner by integrating several KNN learners. A total of 99 data were collected and split into training and test set with a 9:1 ratio. The training set was used to obtain the best hyperparameters by 10-fold cross-validation and grid search, and the test set was used to determine the performance of the model. The results showed that the Mean Absolute Error (MAE) of this framework is 28.06% of the traditional model and outperforms other ensemble methods. Therefore, the proposed framework is suitable for metal corrosion prediction under small sample conditions.

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On the modeling of the annual corrosion rate in main cables of suspension bridges using combined soft computing model and a novel nature-inspired algorithm

Cited by 40 publications

References 43 publications

Application of Soft Computing Techniques in Bridge Health Assessment: A Review

Application of Soft Computing Techniques in Bridge Health Assessment: A Review

Estimating the compressive strength of rectangular fiber reinforced polymer–confined columns using multilayer perceptron, radial basis function, and support vector regression methods

Metal Corrosion Rate Prediction of Small Samples Using an Ensemble Technique

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