Authors:
Ali Saleh Department of Transport Infrastructure and Water Resources Engineering, Faculty of Architecture, Civil Engineering and Transport Sciences, Széchenyi István University, Győr, Hungary

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László Gáspár Department of Transport Infrastructure and Water Resources Engineering, Faculty of Architecture, Civil Engineering and Transport Sciences, Széchenyi István University, Győr, Hungary
Institute for Hungarian Transport Sciences and Logistics Non-Profit Ltd. Budapest, Hungary

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https://orcid.org/0000-0002-0574-4100
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Abstract

This study uses a three-layer backpropagation neural network combined with particle swarm optimization to control the foamed bitumen in cold recycling technology. The foaming process of bitumen is non-linear and depends on dynamic temperature. By developing a neural network model, this study effectively captures the complex relationships between temperature, water content, air pressure, and the expansion ratio and half-life of foamed bitumen. The integration of particle swarm optimization enhances the accuracy and convergence of the neural network model by optimizing the initial weights. This optimization process improves the model's ability to predict and control the quality of foamed bitumen accurately. It serves as a valuable tool for the rapid development of high-quality cold asphalt design.

Abstract

This study uses a three-layer backpropagation neural network combined with particle swarm optimization to control the foamed bitumen in cold recycling technology. The foaming process of bitumen is non-linear and depends on dynamic temperature. By developing a neural network model, this study effectively captures the complex relationships between temperature, water content, air pressure, and the expansion ratio and half-life of foamed bitumen. The integration of particle swarm optimization enhances the accuracy and convergence of the neural network model by optimizing the initial weights. This optimization process improves the model's ability to predict and control the quality of foamed bitumen accurately. It serves as a valuable tool for the rapid development of high-quality cold asphalt design.

1 Introduction

Cold recycling technology is considered one of the most important renewable construction methods, which is used for saving energy in addition that its environmentally and economic advantages [1]. Foamed bitumen, a unique material produced through a specialized process, involves the injection of water and air into hot bitumen at temperatures ranging from 150 to 180 °C [2]. This distinctive technique induces a reaction between the water and hot bitumen, resulting in a heat exchange phenomenon. Therefore, the water transforms into steam and is forcefully introduced into the bitumen structure under pressure. This interaction gives rise to numerous bitumen bubbles that encapsulate vapor within their composition [3]. The resulting product, characterized by its foamed texture and enhanced properties, offers a range of unique applications and advantages in various fields.

The difficulty in studying the foamed asphalt changes is that the bitumen foam formation process is a non-linear process, as it depends on the dynamic temperature [4, 5]. The foaming quality measurement parameters are ER, which is defined as the ratio of the maximum volume to the original volume before the foaming process, HL, which is the time when the maximum volume needs half of the expansion volume [6, 7]. As many factors affect the performance of asphalt foam, the three most influential factors are temperature (T), water content (WC) and air pressure (AP). In engineering applications, the optimal amount of water needed to foam asphalt is between 2% and 4% of the asphalt mass [8], the minimum value of ER is 8 times and HL is 6 s [9]. Due to the importance of foamed asphalt in these applications, it is necessary to create a foamed asphalt control model with its parameters to determine the best value for each variable and to improve the foam quality. All of them are mean to achieve the rapid development of high-quality cold asphalt design [10].

The neural network model stands out as a distinctive approach in the realm of data-driven modeling, as it effectively establishes connections between input and output data, without necessitating an in-depth understanding of the underlying internal processes [11, 12]. This unique characteristic has rendered neural network models highly sought-after and widely employed in addressing complex modeling challenges across diverse fields. By leveraging the power of neural networks, researchers and practitioners have been able to tackle intricate problems that would otherwise prove arduous or impractical using traditional methodologies. The versatility and efficacy of neural network models have solidified their position as a go-to solution in numerous domains, revolutionizing the way complex modeling problems are approached and solved. There is a parametric model, which is created by Wang [5], its function is control the foamed bitumen quality based on the experimental results, but it turned outfaced at difficult to be applied in the engineering because of the large and complicated calculation [7].

This incorporation of Particle Swarm Optimization (PSO) not only improved the prediction accuracy of the neural network model but also contributed to a more robust and precise characterization of the asphalt foaming behavior. The combined utilization of neural networks and PSO showcases a novel methodology that can be applied to similar modeling problems, providing valuable insights, and paving the way for improved understanding and control of asphalt foaming processes.

2 Machine learning models

Machine learning is a branch of Artificial Intelligence (AI) [13], encompasses three primary approaches for solving problems.

Supervised learning: In this approach, a computer is trained using a dataset that consists of input data along with corresponding output datum. All that, to learn a general rule or function that can map given inputs to their respective outputs. This sort is commonly used for tasks as classification and regression [14].

Unsupervised learning: Unsupervised learning algorithms operate without specific guidance. They aim to discover patterns or structures within input data without the presence of labeled outputs. Unsupervised learning techniques include methods for data visualization, dimensionality reduction, and clustering, enabling the identification of inherent patterns within the data [15].

Reinforcement learning: Reinforcement learning involves a computer or agent operating within a dynamic environment [16]. The algorithm learns to perform a specific goal-oriented task and receives feedback, typically in the form of rewards or penalties, to reinforce its learning process. By optimizing its actions based on received feedback, the algorithm aims to maximize its cumulative reward.

Furthermore, hybrid approaches as semi-supervised learning, offer a combination of supervised and unsupervised techniques, and can be tailored to specific problem domains [17–22]. In the context of predicting foamed bitumen content by analyzing aggregate gradation, bitumen type, and mixture properties, the problem can be considered a regression task. As the study falls within the realm of supervised learning, which involves predicting continuous values, various supervised machine learning techniques are available for regression problems, including Random Forest, Support Vector Regression, Artificial Neural Networks, and others. These algorithms can be employed to develop accurate models for predicting foamed bitumen content, enhancing our understanding and control of the foaming process.

3 Bitumen foaming model

3.1 BP neural network model

To address the task of bitumen foaming control, a three-layer BackPropagation (BP) neural network model was employed, recognized as one of the most widely used Artificial Neural Network (ANN) architectures [18, 19]. The BP neural network exhibits favorable properties that make it suitable for various applications, including its ability to comprehend nonlinear mappings. In this research, a three-layer configuration was adopted, consisting of an input layer, a hidden layer, and an output layer [20].

The model was designed to focus on three key factors: temperature, water content, and air pressure, which served as the input parameters for the neural network. The experimental results provided the target values, namely the Expansion Ratio and Half-life, which were the outputs of the network [21].

The neural network architecture consisted of three input neurons and two output neurons, reflecting the number of input and output factors being considered. This configuration necessitated the inclusion of a single hidden layer to facilitate the complex mapping process [22–25]. The overall structure and connectivity of the neural network model are illustrated in Fig. 1.

Fig. 1.
Fig. 1.

Architecture of the neural network model

Citation: Pollack Periodica 19, 1; 10.1556/606.2023.00896

By leveraging this unique three-layer BP neural network, the study aimed to construct an asphalt foaming control model, allowing for effective regulation and monitoring of the foaming process. The model's capability to capture nonlinear relationships and its incorporation of the specific input factors and target outputs make it a valuable tool for achieving accurate predictions and optimizing bitumen foaming outcomes.

In the context of optimizing the initial weights of a BP neural network, the hidden layer plays a crucial role in influencing the prediction accuracy. The number of neurons within this layer significantly impacts the network's performance. Based on experimental findings, a formula is commonly used to determine the appropriate number of hidden neurons, which is equal to the square root of the product of the input layer nodes and the output layer nodes. In the case of our network configuration, with 3 input nodes and 2 output nodes, the optimal number of neurons in the hidden layer is determined to be 16.

To further enhance the performance of the BP neural network, PSO can be employed. PSO aims to find the optimal set of initial weights for the network by mimicking the behavior of a swarm of particles moving through a search space. By iteratively adjusting the weights, the PSO algorithm seeks to improve the network's convergence and prediction accuracy.

To implement PSO for weight optimization in the presented neural network model it is needed to define the specific problem, including the desired network architecture and the relevant equations. In this case, the network consists of 3 input nodes, 2 output nodes, and a hidden layer with 16 neurons. The PSO algorithm will work to optimize the initial weights, thereby enhancing the network's performance and its ability to accurately predict the desired outputs. This combination of BP neural network and PSO optimization provides a unique and effective approach to improve the accuracy and effectiveness of the bitumen foaming prediction model.

3.1.1 Problem definition

The problem is to find optimal initial weights for the neural network model. These weights will affect the performance of the network during the training phase, and by optimizing them, it is aimed to improve the network's overall accuracy and convergence speed.

3.1.2 Neural network architecture

The neural network architecture consists of an input layer, a hidden layer, and an output layer. The input layer has 3 nodes; the hidden layer has 16 nodes, and the output layer has 2 nodes.

3.1.3 Equations involved

  1. a)Forward propagation

During the forward propagation phase, the output of each neuron based on the input values and the current weights of the network are calculated. The following equation is used to compute the output of a neuron in the hidden layer:
hi=σj=1nwij(1)·xj+bi(1),
where hi is the output of the ith neuron in the hidden layer; wij(1) is the weight connecting the ith neuron in the hidden layer to the jth input node; xj is the jth input value; bi(1) is the bias term associated with the ith neuron in the hidden layer; σ is the activation function (e.g., sigmoid, tanh, ReLU, etc.).
Similarly, the output of a neuron in the output layer is computed using the following equation:
Ok=σi=1mwki(2)·hi+bk(2),
where Ok is the output of the kth neuron in the output layer; wki(2) is the weight connecting the kth neuron in the output layer to the ith neuron in the hidden layer; hi is the output of the ith neuron in the hidden layer; bk(2) is the bias term associated with the kth neuron in the output layer.
  1. b)Error calculation
During the training phase, the error between the predicted outputs and the desired outputs is also calculated. The error can be computed using various metrics, as Mean Squared Error (MSE) or cross-entropy loss. Let's consider the MSE for simplicity:
E=12nk=1n(dkOk)2,
where E is the mean squared error; n is the number of training samples; dk is the desired output for the kth training sample; Ok is the predicted output for the kth training sample.

3.2 Particle swarm optimization

PSO is a population-based optimization algorithm inspired by the collective behavior of birds and fish, proves useful in optimizing the initial weights of BP neural networks. In a BP neural network, multiple layers of interconnected nodes represent neurons, and the network adjusts the weights associated with neuron connections to minimize output error. Conventionally, initial weights in BP networks are randomly set or determined using heuristics, but finding optimal weights is crucial for enhancing network performance and convergence speed. PSO can address this challenge by treating the initial weights as particles in a search space. The algorithm initializes a population of particles, with each particle representing a potential solution (a set of initial weights). These particles are then iteratively updated based on personal best solutions and the best solution discovered by any particle in the population. During each iteration particles adjust their positions (weights) by considering their velocity, influenced by personal and global best solutions. PSO leverages social interactions and information sharing among particles to guide the search towards promising weight regions that yield improved performance. The fitness function evaluates the neural network's performance with the current set of initial weights, and the process continues until a stopping criterion is met, as reaching a maximum number of iterations or achieving satisfactory performance. By integrating PSO into the optimization of initial weights, BP neural networks benefit from a more efficient and effective search process. PSO intelligently explores the weight space, promoting convergence and potentially enhancing overall network performance.

By applying the PSO algorithm to optimize the initial weights of the BP neural network, the weights that minimize the error function can be found, thus improving the network's performance. The PSO algorithm iteratively updates the positions and velocities of particles, allowing them to explore the weight space and converge towards an optimal solution.

4 Verification of asphalt foaming control model

After verifying the linear regression model on foamed bitumen data, the following results and summary were obtained (Fig. 2).

Fig. 2.
Fig. 2.

R-squared for designed model

Citation: Pollack Periodica 19, 1; 10.1556/606.2023.00896

4.1 Model performance

The R-squared value indicates that approximately 86.3% of the variation in the target variables (Expansion Ratio and Half-life) can be explained by the input variables (Temperature, Water content, and Air pressure). Additionally, the mean squared error provides an estimate of the average squared difference between the actual and predicted values, which in this case the amount is 0.032.

4.2 Scatter plots

Expansion Ratio: The scatter plot comparing the actual and predicted values of the Expansion Ratio shows a reasonably positive linear relationship, indicating that the model can effectively predict this variable (Fig. 3).

Fig. 3.
Fig. 3.

a) Prediction values of ER and HL, b) Experimental results of ER and HL

Citation: Pollack Periodica 19, 1; 10.1556/606.2023.00896

Half-life: The scatter plot for the Half-life variable demonstrates a good agreement between the actual and predicted values, suggesting that the model captures the underlying patterns in the data (Fig. 3).

4.3 Correlation analysis

The correlation heatmap reveals the relationship between the input variables (Temperature, Water content, and Air pressure) and the output variables (Expansion Ratio and Half-life). It helps in identifying the variables that have a strong impact on the target variables and their interrelationships as it is shown in Fig. 4.

Fig. 4.
Fig. 4.

Correlation values

Citation: Pollack Periodica 19, 1; 10.1556/606.2023.00896

4.4 Model equation

The linear regression model equations can be expressed as follows:
ExpansionRatio=0.213·Temperature+0.056·Watercontent+0.017·Airpressure0.003,
Halflife=0.038·Temperature+0.045·Wotercontent+0.014·Airpressure+0.002.

These equations represent the relationship between the input variables (Temperature, Water content, and Air pressure) and the corresponding output variables (Expansion Ratio and Half-life) are based on the linear regression model. The coefficients in the equations indicate the impact of each input variable on the respective output variable. The intercept terms −0.003 and 0.002 represent the base value of the output variables when all input variables are zero.

In addition to that, by plugging in specific values for the input variables, the predicted values for the Expansion Ratio and Half-life can be calculated using these equations (Fig. 5).

Fig. 5.
Fig. 5.

Demonstration of Eqs (4) and (5) using the designed model

Citation: Pollack Periodica 19, 1; 10.1556/606.2023.00896

4.5 Optimal values

This model can predicate the optimal values after training the network; the results are shown in Fig. 6:

  • The optimal water content that maximizes the Expansion Ratio is found to be 2.65%;

  • The optimal temperature that maximizes the Expansion Ratio amounts to 178 °C;

  • The optimal air pressure that maximizes the Half-life is 2.5 bar.

Fig. 6.
Fig. 6.

The optimal values using the model

Citation: Pollack Periodica 19, 1; 10.1556/606.2023.00896

These results provide valuable insights into the relationship between the input variables and the target variables in the foamed bitumen application. The linear regression model demonstrates a good fit to the data, allowing for accurate predictions, and understanding of the influential factors.

5 Conclusions

Using the linear regression model on foamed bitumen data, valuable insights were gained and promising results achieved. The model demonstrated good performance with a high R-squared value of 0.863, indicating that approximately 86.3% of the variation in the target variables (Expansion Ratio and Half-life) can be explained by the input variables (Temperature, Water content, and Air pressure). Additionally, the MSE of 0.032 suggests that the model's predictions are, on average, quite close to the actual values. The scatter plots comparing the actual and predicted values for both the Expansion Ratio and Half-life show a reasonably positive linear relationship, indicating that the model effectively captures the underlying patterns in the data. This suggests that the model can be utilized to make accurate predictions for these variables.

Furthermore, the correlation analysis using a heatmap has provided insights into the relationships between the input variables (Temperature, Water content, and Air pressure) and the output variables (Expansion Ratio and Half-life). This analysis helps identify the variables that have a strong impact on the target variables and reveals their interrelationships.

The equations derived from the linear regression model allow us to quantify the relationships between the input and output variables. The coefficients in the equations represent the impact of each input variable on the corresponding output variable, while the intercept terms provide the base values of the output variables when all input variables are zero. These equations can be used to make predictions and understand the influential factors in the foamed bitumen application.

Overall, the results from this operation highlight the effectiveness of the linear regression model in predicting the Expansion Ratio and Half-life of foamed bitumen. This model can be utilized to optimize the mixture design and enhance the understanding of the influential factors in foamed bitumen applications, leading to improved performance and efficiency in this field.

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    • Search Google Scholar
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    • Search Google Scholar
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  • [1]

    A. Wang, H. Cheng, and J. Wang, “Research on foamed asphalt technology and equipment,” in Proceedings of 2009 International Conference on Energy and Environment Technology, Guilin, China, October 16–18, 2009, pp. 287291.

    • Search Google Scholar
    • Export Citation
  • [2]

    B. K. Bairgi and R. A. Tarefder, “A synthesis of asphalt foaming parameters and their association in foamed binder and mixture characteristics,” in International Conference on Highway Pavements and Airfield Technology, Philadelphia, Pennsylvania, August 27–30, 2017, pp. 256267.

    • Search Google Scholar
    • Export Citation
  • [3]

    S. Kumrawat and V. Deulkar, “A research of foamed bitumen,” Int. Res. J. Eng. Technol., vol. 06, no. 5, pp. 69696974, 2019.

  • [4]

    B. W. Hailesilassie, M. Hugener, A. Bieder, and M. N. Partl, “New experimental methods for characterizing formation and decay of foam bitumen,” Mater. Struct., vol. 49, pp. 24392454, 2016.

    • Search Google Scholar
    • Export Citation
  • [5]

    A. Wang, Q. Chen, and R. Qiu, “Comparative evaluation for analysis and experiment of multiphase flow field in asphalt foaming chamber,” J. Build. Mater., vol. 16, no. 4, pp. 621625, 2013.

    • Search Google Scholar
    • Export Citation
  • [6]

    S. Khosravifar, D. G. Goulias, and C. W. Schwartz, “Laboratory evaluation of foamed asphalt stabilized base materials,” in GeoCongress 2012, Oakland, California, US, March 25–29, 2012, pp. 15921601.

    • Search Google Scholar
    • Export Citation
  • [7]

    A. Woszuk, A. Zofka, L. Bandura, and W. Franus, “Effect of zeolite properties on asphalt foaming,” Construct. Build Mater., vol. 139, pp. 247255, 2017.

    • Search Google Scholar
    • Export Citation
  • [8]

    R. Hanna, B. Sultan, and A. Saleh, “The possibility of design the asphalt mixtures using foamed asphalt,” Tishreen Univ. J. Res. Sci. Stud. – Eng. Sci. Ser., vol. 41, no. 3, pp. 179198, 2019.

    • Search Google Scholar
    • Export Citation
  • [9]

    Cold Recycling Technology. Wirtgen GmbH, Windhagen, Germany, 2012.

  • [10]

    J. G. Liu, J. H. Liu, Y. He, and K. Li, “Status and development trend of asphalt foaming process,” J. Meas. Sci. Instrum., vol. 5, no. 2, pp. 98102, 2014.

    • Search Google Scholar
    • Export Citation
  • [11]

    A. R. Ghanizadeh and M. Fakhri, “Prediction of frequency for simulation of asphalt mix fatigue tests using MARS and ANN,” Sci. World J., vol. 2014, 2014, Art no. 515467.

    • Search Google Scholar
    • Export Citation
  • [12]

    F. Leiva-Villacorta, A. Vargas-Nordcbeck, and D. H. Timm, “Non-destructive evaluation of sustainable pavement technologies using artificial neural networks,” Int. J. Pavement Res. Technol., vol. 10, no. 2, pp. 139147, 2017.

    • Search Google Scholar
    • Export Citation
  • [13]

    E. Horvitz and D. Mulligan, “Data, privacy, and the greater good,” Science, vol. 349, no. 6245, pp. 253255, 2015.

  • [14]

    M. Alkaissy, M. Arashpour, E. M. Golafshani, M. R. Hosseini, S. Khanmohammadi, Y. Bai, and H. Feng, “Enhancing construction safety: Machine learning-based classification of injury types,” Saf. Sci., vol. 162, 2023, Art no. 106102.

    • Search Google Scholar
    • Export Citation
  • [15]

    A. L. Wang, Z. S. Fu, and F. M. Liu, “Asphalt foaming quality control model using neural network and parameters optimization,” Int. J. Pavement Res. Technol., vol. 11, no. 4, pp. 401407, 2018.

    • Search Google Scholar
    • Export Citation
  • [16]

    N. Baldo, F. Rondinella, F. Daneluz, and M. Pasetto, “Foamed bitumen mixtures for road construction made with 100% waste materials: A laboratory study,” Sustainability, vol. 14, no. 10, 2022, Art no. 6056.

    • Search Google Scholar
    • Export Citation
  • [17]

    F. Y. Liu, W. Q. Ding, Y. F. Qiao, L. B. Wang, and Q. Y. Chen, “Compressive behavior of hybrid steel-polyvinyl alcohol fiber-reinforced concrete containing fly ash and slag powder: experiments and an artificial neural network model,” J. Zhejiang Univ.-Sci. A, vol. 22, no. 9, pp. 721735, 2021.

    • Search Google Scholar
    • Export Citation
  • [18]

    K. L. Priddy and P. E. Keller, Artificial Neural Networks: An Introduction, SPIE Press, 2005.

  • [19]

    R. Yang, Z. Duan, Y. Lu, L. Wang, and G. Xu, “Load reduction test method of similarity theory and BP neural networks of large cranes,” Chin. J. Mech. Eng., vol. 29, no. 1, pp. 145151, 2016.

    • Search Google Scholar
    • Export Citation
  • [20]

    K. Zhong, Q. Meng, M. Sun, and G. Luo, “Artificial Neural Network (ANN) modeling for predicting performance of SBS modified asphalt,” Materials, vol. 15, no. 23, 2022, Paper no. 8695.

    • Search Google Scholar
    • Export Citation
  • [21]

    R. Rahman, S. Chowdhury, M. Abdullah, A. Sarkar, S. R. Sayeed, and M. I. Real, “A comparative study on properties of different grade bitumen used in the transportation projects in and around Dhaka City,” Trends Civil Eng. its Architecture, vol. 3, no. 2, pp. 17, 2019.

    • Search Google Scholar
    • Export Citation
  • [22]

    K. J. Jenkins, “Mix design considerations for cold and half-warm bituminous mixes with emphasis of foamed bitumen,” PhD Thesis, University of Stellenbosch, South Africa, 2000.

    • Search Google Scholar
    • Export Citation
  • [23]

    Á. Pintér, B. Schmuck, and S. Szénási, “Short text evaluation with neural network,” Pollack Period., vol. 13, no. 3, pp. 107118, 2018.

    • Search Google Scholar
    • Export Citation
  • [24]

    I. Bolkeny and V. Fuvesi, “AI based predictive detection system,” Pollack Period., vol. 13, no. 2, pp. 137146, 2018.

  • [25]

    K. Almássy, A. Geiger, and P. Gergo, “Using possibilities of rubber bitumen in road building,” Pollack Period., vol. 5, no. 1, pp. 5363, 2010.

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Senior editors

Editor(s)-in-Chief: Iványi, Amália

Editor(s)-in-Chief: Iványi, Péter

 

Scientific Secretary

Miklós M. Iványi

Editorial Board

  • Bálint Bachmann (Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Jeno Balogh (Department of Civil Engineering Technology, Metropolitan State University of Denver, Denver, Colorado, USA)
  • Radu Bancila (Department of Geotechnical Engineering and Terrestrial Communications Ways, Faculty of Civil Engineering and Architecture, “Politehnica” University Timisoara, Romania)
  • Charalambos C. Baniotopolous (Department of Civil Engineering, Chair of Sustainable Energy Systems, Director of Resilience Centre, School of Engineering, University of Birmingham, U.K.)
  • Oszkar Biro (Graz University of Technology, Institute of Fundamentals and Theory in Electrical Engineering, Austria)
  • Ágnes Borsos (Institute of Architecture, Department of Interior, Applied and Creative Design, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Matteo Bruggi (Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Italy)
  • Petra Bujňáková (Department of Structures and Bridges, Faculty of Civil Engineering, University of Žilina, Slovakia)
  • Anikó Borbála Csébfalvi (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Mirjana S. Devetaković (Faculty of Architecture, University of Belgrade, Serbia)
  • Szabolcs Fischer (Department of Transport Infrastructure and Water Resources Engineering, Faculty of Architerture, Civil Engineering and Transport Sciences Széchenyi István University, Győr, Hungary)
  • Radomir Folic (Department of Civil Engineering, Faculty of Technical Sciences, University of Novi Sad Serbia)
  • Jana Frankovská (Department of Geotechnics, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
  • János Gyergyák (Department of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Kay Hameyer (Chair in Electromagnetic Energy Conversion, Institute of Electrical Machines, Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Germany)
  • Elena Helerea (Dept. of Electrical Engineering and Applied Physics, Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov, Romania)
  • Ákos Hutter (Department of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technolgy, University of Pécs, Hungary)
  • Károly Jármai (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
  • Teuta Jashari-Kajtazi (Department of Architecture, Faculty of Civil Engineering and Architecture, University of Prishtina, Kosovo)
  • Róbert Kersner (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Rita Kiss  (Biomechanical Cooperation Center, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, Hungary)
  • István Kistelegdi  (Department of Building Structures and Energy Design, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Stanislav Kmeť (President of University Science Park TECHNICOM, Technical University of Kosice, Slovakia)
  • Imre Kocsis  (Department of Basic Engineering Research, Faculty of Engineering, University of Debrecen, Hungary)
  • László T. Kóczy (Department of Information Sciences, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, University of Győr, Hungary)
  • Dražan Kozak (Faculty of Mechanical Engineering, Josip Juraj Strossmayer University of Osijek, Croatia)
  • György L. Kovács (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Balázs Géza Kövesdi (Department of Structural Engineering, Faculty of Civil Engineering, Budapest University of Engineering and Economics, Budapest, Hungary)
  • Tomáš Krejčí (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
  • Jaroslav Kruis (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
  • Miklós Kuczmann (Department of Automations, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Széchenyi István University, Győr, Hungary)
  • Tibor Kukai (Department of Engineering Studies, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Maria Jesus Lamela-Rey (Departamento de Construcción e Ingeniería de Fabricación, University of Oviedo, Spain)
  • János Lógó  (Department of Structural Mechanics, Faculty of Civil Engineering, Budapest University of Technology and Economics, Hungary)
  • Carmen Mihaela Lungoci (Faculty of Electrical Engineering and Computer Science, Universitatea Transilvania Brasov, Romania)
  • Frédéric Magoulés (Department of Mathematics and Informatics for Complex Systems, Centrale Supélec, Université Paris Saclay, France)
  • Gabriella Medvegy (Department of Interior, Applied and Creative Design, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Tamás Molnár (Department of Visual Studies, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Ferenc Orbán (Department of Mechanical Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Zoltán Orbán (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Dmitrii Rachinskii (Department of Mathematical Sciences, The University of Texas at Dallas, Texas, USA)
  • Chro Radha (Chro Ali Hamaradha) (Sulaimani Polytechnic University, Technical College of Engineering, Department of City Planning, Kurdistan Region, Iraq)
  • Maurizio Repetto (Department of Energy “Galileo Ferraris”, Politecnico di Torino, Italy)
  • Zoltán Sári (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Grzegorz Sierpiński (Department of Transport Systems and Traffic Engineering, Faculty of Transport, Silesian University of Technology, Katowice, Poland)
  • Zoltán Siménfalvi (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
  • Andrej Šoltész (Department of Hydrology, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
  • Zsolt Szabó (Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Hungary)
  • Mykola Sysyn (Chair of Planning and Design of Railway Infrastructure, Institute of Railway Systems and Public Transport, Technical University of Dresden, Germany)
  • András Timár (Faculty of Engineering and Information Technology, University of Pécs, Hungary)
  • Barry H. V. Topping (Heriot-Watt University, UK, Faculty of Engineering and Information Technology, University of Pécs, Hungary)

POLLACK PERIODICA
Pollack Mihály Faculty of Engineering
Institute: University of Pécs
Address: Boszorkány utca 2. H–7624 Pécs, Hungary
Phone/Fax: (36 72) 503 650

E-mail: peter.ivanyi@mik.pte.hu 

or amalia.ivanyi@mik.pte.hu

Indexing and Abstracting Services:

  • SCOPUS
  • CABELLS Journalytics

 

2022  
Web of Science  
Total Cites
WoS
not indexed
Journal Impact Factor not indexed
Rank by Impact Factor

not indexed

Impact Factor
without
Journal Self Cites
not indexed
5 Year
Impact Factor
not indexed
Journal Citation Indicator not indexed
Rank by Journal Citation Indicator

not indexed

Scimago  
Scimago
H-index
14
Scimago
Journal Rank
0.298
Scimago Quartile Score

Civil and Structural Engineering (Q3)
Computer Science Applications (Q3)
Materials Science (miscellaneous) (Q3)
Modeling and Simulation (Q3)
Software (Q3)

Scopus  
Scopus
Cite Score
1.4
Scopus
CIte Score Rank
Civil and Structural Engineering 256/350 (27th PCTL)
Modeling and Simulation 244/316 (22nd PCTL)
General Materials Science 351/453 (22nd PCTL)
Computer Science Applications 616/792 (22nd PCTL)
Software 344/404 (14th PCTL)
Scopus
SNIP
0.861

2021  
Web of Science  
Total Cites
WoS
not indexed
Journal Impact Factor not indexed
Rank by Impact Factor

not indexed

Impact Factor
without
Journal Self Cites
not indexed
5 Year
Impact Factor
not indexed
Journal Citation Indicator not indexed
Rank by Journal Citation Indicator

not indexed

Scimago  
Scimago
H-index
12
Scimago
Journal Rank
0,26
Scimago Quartile Score Civil and Structural Engineering (Q3)
Materials Science (miscellaneous) (Q3)
Computer Science Applications (Q4)
Modeling and Simulation (Q4)
Software (Q4)
Scopus  
Scopus
Cite Score
1,5
Scopus
CIte Score Rank
Civil and Structural Engineering 232/326 (Q3)
Computer Science Applications 536/747 (Q3)
General Materials Science 329/455 (Q3)
Modeling and Simulation 228/303 (Q4)
Software 326/398 (Q4)
Scopus
SNIP
0,613

2020  
Scimago
H-index
11
Scimago
Journal Rank
0,257
Scimago
Quartile Score
Civil and Structural Engineering Q3
Computer Science Applications Q3
Materials Science (miscellaneous) Q3
Modeling and Simulation Q3
Software Q3
Scopus
Cite Score
340/243=1,4
Scopus
Cite Score Rank
Civil and Structural Engineering 219/318 (Q3)
Computer Science Applications 487/693 (Q3)
General Materials Science 316/455 (Q3)
Modeling and Simulation 217/290 (Q4)
Software 307/389 (Q4)
Scopus
SNIP
1,09
Scopus
Cites
321
Scopus
Documents
67
Days from submission to acceptance 136
Days from acceptance to publication 239
Acceptance
Rate
48%

 

2019  
Scimago
H-index
10
Scimago
Journal Rank
0,262
Scimago
Quartile Score
Civil and Structural Engineering Q3
Computer Science Applications Q3
Materials Science (miscellaneous) Q3
Modeling and Simulation Q3
Software Q3
Scopus
Cite Score
269/220=1,2
Scopus
Cite Score Rank
Civil and Structural Engineering 206/310 (Q3)
Computer Science Applications 445/636 (Q3)
General Materials Science 295/460 (Q3)
Modeling and Simulation 212/274 (Q4)
Software 304/373 (Q4)
Scopus
SNIP
0,933
Scopus
Cites
290
Scopus
Documents
68
Acceptance
Rate
67%

 

Pollack Periodica
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Pollack Periodica
Language English
Size A4
Year of
Foundation
2006
Volumes
per Year
1
Issues
per Year
3
Founder Akadémiai Kiadó
Founder's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 1788-1994 (Print)
ISSN 1788-3911 (Online)

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