Development of pavement roughness models using Artificial Neural Network (ANN)

Alatoom, Yazan Ibrahim; Al‐Suleiman, Turki I.

doi:10.1080/10298436.2021.1968396

Cited by 37 publications

(9 citation statements)

References 51 publications

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“…In this step, ANN 31 is first trained to detect network intrusion attack. ANN is a tightly interconnected collection of basic processors called neurons.…”

Section: Proposed Methodologymentioning

confidence: 99%

Intrusion detection system based on the beetle swarm optimization and K‐RMS clustering algorithm

Pran,

Raja,

Jeyasudha

2024

Adaptive Control & Signal

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SummaryIntrusion detection is a cyber‐security method that is significant for network security. It is utilized to detect behaviors that compromise security and privacy within a network or in the context of a computer system. To enhance the identification, an Intrusion Detection System Based on the Beetle Swarm Optimization and K‐RMS Clustering Algorithm cluster‐based hybrid classifiers is proposed in this manuscript. Here, the data is amassed from CICIDS2017 dataset. Then the data is preprocessed to eradicate the unwanted noise. After completing the preprocessed data, it can be clustered by using K‐RMS clustering algorithm. This algorithm cluster the entire data to the associated cluster set depending on the data behavior. The classification algorithm is considered to predict the data as normal or attacking behaviors. The hybrid classification is used to predict the data. The solitary predictor aims to achieve high detection rates and accuracy. The hybrid classifiers, such as support vector machines, artificial neural networks are applied to recognize the normal or intruder. The performance of the SVM‐ANN‐IDS method attains 22.05%, 15.87%, 27.25% higher accuracy, 23.90% and 28.53% higher precision, 29.29%, 19.19% and 23.27% higher specificity and 18.28%, 24.36% and 27.49% greater recall when compared to the existing models, like developing novel deep‐learning model to improve network intrusion categorization (DNN‐IDS), Intrusion identification scheme on real‐time data traffic under machine learning techniques along feature selection method (RNN‐SVM‐IDS) and recurrent deep learning basis feature fusion ensemble meta‐classifier for intellectual network intrusion identification scheme (RNN‐IDS) respectively.

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“…In this step, ANN 31 is first trained to detect network intrusion attack. ANN is a tightly interconnected collection of basic processors called neurons.…”

Section: Proposed Methodologymentioning

confidence: 99%

Intrusion detection system based on the beetle swarm optimization and K‐RMS clustering algorithm

Pran,

Raja,

Jeyasudha

2024

Adaptive Control & Signal

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“…The first group is defined as regression analysis techniques, (empirical performance models and mechanistic-empirical models). The second is defined as probabilistic performance modeling (Bayesian and Markov probabilistic modeling [6,7]) and the third group is defined as artificial neural networks modeling (back-propagation networks [8]). Nevertheless, despite the wide range of available deterioration models, the deterministic and probabilistic groups are referred to as the basic groups because they attract the greatest attention [9].…”

Section: State Of the Artmentioning

confidence: 99%

“…In both cases, performing maintenance repair action was simulated for the year 2027, i.e., the 6th year of pavement operation. In each year cumulative cash flow of the project was calculated following Equation (8). The cumulative cash flow is influenced by pavement degradation given by individual deterioration equations.…”

Section: Parameter-cumulative Cash Flowmentioning

confidence: 99%

Economic Impact Analysis of the Application of Different Pavement Performance Models on First-Class Roads with Selected Repair Technology

et al. 2021

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Mathematical expression of the deterioration of individual pavement parameters is, from the point of optimal repair and maintenance strategy decision-making process, an important part of the application of any pavement management system (PMS). The reliability of individual PMS depends on the quality of the inputs and the reliability of its internal sub-systems; thus, deterioration equations derived from high-quality input data play pivotal roles in a system for the prediction of the pavement life cycle. This paper describes the application of pavement performance models within pavement life cycle analysis (LCA) with the use of the integrated system of economic evaluation (ISEH), which is a calculation tool used for first-class roads with a standardized pavement composition of asphalt binders, where changes in operational capability parameters are modeled using individual model simulations. The simulations presented in this paper demonstrate changes in main economic indicators (net present value and internal rate of return) on two different pavement performance models. Both simulations share the same input parameters (traffic intensity, construction intervention, maintenance costs, discount rate) but differ in deterioration evaluation, all of which were applied to each model (a total of five models).

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“…Gu et al [17] developed an artificial neural network model to predict the response of geogrid reinforced pavement, and then used a neural network model to predict the modified material performance caused by geogrid reinforcement to predict the performance of geogrid reinforced pavement. Alatom&Al Sleiman (Obaidat) [18] studied the effects of pavement service life, traffic load, and traffic volume on pavement IRI and a pavement IRI model was developed based on artificial neural networks and compared with regression models which found that artificial neural networks had a great performance in prediction. Focusing on pavement surface maintenance, Wang et al [19] established a predictive model for preventive maintenance of highways using neural networks.…”

Section: Introductionmentioning

confidence: 99%

Pavement performance deterioration model based on deep neural networks

2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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The development of transportation has shifted from construction to the state of maintenance, and reasonable and efficient pavement performance prediction models play an important role in developing reasonable pavement maintenance strategies, ensuring the efficient operation of pavement and extending the service life. At present, the research on pavement performance prediction mostly focuses on a non-comprehensive pavement performance evaluation indicator, and when the influence factors increase, the predictive ability will also be affected. Therefore, based on deep neural networks, OPI is selected as the pavement performance evaluation index, and the pavement service time, traffic load, pavement asphalt rutting condition and pavement asphalt cracking condition are selected as the influence factors. Next, the pavement performance deterioration model is established, and the established model is compared with the traditional regression model. After comparison, it is found that the prediction accuracy of the deterioration model reaches 83.25%, which is 17.7% higher than the traditional regression model. Besides, the prediction accuracy of the vast majority of test sets and train sets exceeds 80%. According to the research in this paper, the pavement performance deterioration model based on deep neural networks can better simulate the pavement performance deterioration process, and can still predict pavement performance well under multiple influence factors.

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Development of pavement roughness models using Artificial Neural Network (ANN)

Cited by 37 publications

References 51 publications

Intrusion detection system based on the beetle swarm optimization and K‐RMS clustering algorithm

Intrusion detection system based on the beetle swarm optimization and K‐RMS clustering algorithm

Economic Impact Analysis of the Application of Different Pavement Performance Models on First-Class Roads with Selected Repair Technology

Pavement performance deterioration model based on deep neural networks

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