Sensitivity analysis and optimization of pier shape scour prediction using HEC-RAS

Muhanad AL-Jubouri; Richard P. Ray; Ethar H. Abbas

doi:10.1556/606.2024.01211

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Pollack Periodica

Volume/Issue:: Accepted Manuscript / Online First

Sensitivity analysis and optimization of pier shape scour prediction using HEC-RAS

Authors:

Muhanad AL-Jubouri

Muhanad AL-Jubouri Department of Structural and Geotechnical Engineering, Faculty of Civil Engineering, Széchenyi István University, Győr, Hungary

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al.jubouri.muhanad@hallgato.sze.hu https://orcid.org/0000-0003-0865-9312

Richard P. Ray

Richard P. Ray Department of Structural and Geotechnical Engineering, Faculty of Civil Engineering, Széchenyi István University, Győr, Hungary

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, and

Ethar H. Abbas

Ethar H. Abbas General Commission for Irrigation and Reclamation Projects, Ministry of Water Resources, Baghdad 10001, Iraq

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Online Publication Date:: 07 Nov 2024

Publication Date:: 07 Nov 2024

Article Category:: Research Article

DOI:: https://doi.org/10.1556/606.2024.01211

Keywords:: scour depth; bridge piers; HEC-RAS; Colorado State University equation; Froehlich method; sensitivity analysis

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Abstract

Scour around bridge piers threatens bridge stability. This study uses the Hydrologic Engineering Center River Analysis System to improve depth estimates for various pier shapes. The Colorado State University and Froehlich equations were tested with a one-dimensional model calibrated for circular, square, rectangular, oblong, oval, and cylindrical piers. Sensitivity analysis identified coefficients K2, K3, flow velocity, and depth as key factors, with K2 being most significant. The Colorado State University equation overestimated scour depths, especially for square piers. The Froehlich method provided more accurate predictions, confirming the system's value in hydraulic modeling for bridge stability analysis.

Abstract

1 Introduction

A sediment scour occurs when water passes around an obstruction within a body [1]. Thus, this phenomenon has damaged the foundations of structures like spillways, bridges, and weirs, making them more prone to erosion that could lead to structural collapse. Three types of scours typically affect rivers and hydraulic structures. The phrase “long-term aggradation and degradation” could mean either deterioration or slow but perceptible changes in bank height and configuration due to geomorphic processes occurring over a long time. Additionally, contraction scour can occur because of natural or artificial features like river embankments or bridge openings that restrict flow rates. Moreover, inside a river's main channel and valley, some piers and abutments cause local scour around bridge foundations [2, 3]. In addition, according to K. Wardhana and F. C. Hadipriono [4], many bridges have been washed away recently by erosion around their piers. The main goals were to identify the causes of pier scour and to develop new methods to protect bridges from its effects. According to P. F. Lagasse and E. V Richardson [5], 1,000 bridges in the US have collapsed in the last 30 years, with 60% of those collapses caused by hydraulic failure. Namely, foundation scours. River flooding is the primary cause of local scouring around bridge piers and abutments, according to the research of G. J. C. M. Hoffmans and H. J. Verheij [6], leading to bridge collapses. Since the 1950s, there have been many studies on scouring bridge piers. Nevertheless, understanding the complex flow patterns, scouring processes, bridge geometry, and the presence of different erodible bed materials like gravel, sand, and clay remains a challenging problem [7]. Promoting a better understanding of scour's origins and developing modern technologies for identifying scour are crucial steps toward mitigating its detrimental impacts. Overestimating construction costs results in high costs, whereas underestimating the equilibrium of scour depth may cause bridge collapse at any time [8]. Scientists conducted significant testing and computational analysis to determine the extent of erosion in various soil components. More studies have refined the equations [9–16] to determine better scour depth. Academics have long used simulation modeling software to predict the scouring depth around bridge piers [17]. This study examines local scour around bridge piers using dry silver paint, wet paint, and Computer Fluid Dynamic (CFD) techniques, comparing results with past research. The findings provide insights into side-by-side pier arrangements' scour mechanisms and drag forces. These software alternatives are adapted to realize a range of modeling needs. However, limitations in time and data availability make it difficult to choose the best application. Scour depths and possible dangers have been assessed in several studies using the HEC-RAS software [18–22]. HEC-RAS is software developed by the US Army Corps of Engineers for hydraulic modeling. It simulates water flowing through rivers and channels, providing tools for steady and unsteady flow calculations, sediment transport, and water quality modeling [23]. Recent research has demonstrated the effectiveness of HEC-RAS in predicting scour depth for parallel bridges, showing significant variations in scour profiles based on bridge configurations and flow conditions [21]. The Galala Bridge Scour Model [24] research investigates the impact of scouring on the bridge's structural integrity over Ambon's Way Ruhu River. The study uses the HEC-RAS 6.4.1 application to evaluate scouring depth using the Colorado State University CSU and Froehlich methods. Based on various flow conditions and recurrence periods, the simulation results indicate that the scouring depth could reach the base of the bridge, potentially compromising its stability and leading to collapse during significant flood events [24]. The HEC-RAS software [25] was used to compute the local scours near bridge piers during large discharge events with extended return durations. This study shows a combination of factors, like high river discharge and velocity and increased vortex flow around the piers, which cause scouring in the lateral piers of the bridge. According to [26], by using the HEC-RAS model, highly accurate predictions can be made on pier scour depth. In supporting this conclusion, both the results of the Babylon Bridge test and those produced by the HEC-RAS program were in concurrence. These studies have shown how important it is to consider pier dimensions, flow parameters, and sediment quality to estimate scouring depth accurately. Utilizing empirical correlations coupled with the HEC-RAS tool, it is feasible to approximate scouring depths and give the best strategies for making bridges more resistant to scouring. The 1D Model simplifies data entry and results display through its user-friendly interface; hence, it is useful in predicting scour depths around bridge piers. In addition, it accurately forecasts local scour near the pier bases. It would be possible to save a considerable amount of money on the construction of bridge foundations if the depth of scour could be estimated during the design stage. Predicting scour depth with high accuracy enables pre-emptive actions to be implemented cheaply [19, 27]. However, the system can only consider what happens if the pier structure blocks hydraulic flow. Consequently, local differences in the orientation angle considerably influence the measurement of the scour depth [28]. Notable among this program's features is its capacity to model one-dimensional channel flow dynamics. This study focuses on improving the accuracy of scour depth predictions close to bridge piers of varying forms using the widely used hydraulic modeling program HEC-RAS. Experimental data in reference [29] form the basis of the study. A one-dimensional model shows how well the software-integrated CSU and Froehlich equations can determine the scour depth for round, square, rectangular, diamond, oval, or cylinder-shaped piers. Using the Monte Carlo technique, a sensitivity analysis was conducted to determine which factors in the CSU equation impact the pier scour depth findings.

2 Experimental data

Data collection uses the experimental model of [29] as the primary data source, and an accurately sized flume was used for laboratory testing with precise measurements of 12.50 m in length, 0.3 m in breadth, and 0.5 m in depth. Accurate flow measurements were obtained using a volumetric flow meter with 1% precision and an integrated electronic gauge within the flume discharge system. This provided 0.05% accuracy with an average flow velocity of 0.29 m s⁻¹ and flow depth of 0.12 m. The time spent doing each experiment was six hours. Figure 1 shows the channel dimensions and a plan view of the flume. For this investigation, a sand bed that was 15 cm thick was used, and it was smoothed before every observation. According to the sieve analysis, the sediments' median diameter (D50) was 0.93 mm, and the gradation factor (σ) was 1.27. Six pier configurations, namely cylindrical (C), square (S), rectangle (Re), diamond (D), oval (OV), and ogival (OG), were examined. Standardized dimensions of 2.5 cm in width were maintained for all shapes except cylindrical, square, and diamond configurations. The oval, rectangle, and ogival shapes had a consistent length of 10 cm, as it is shown in Fig. 2.

Fig. 1.

The experimental flume shows a visualization of testing conditions with a cylindrical pier

Citation: Pollack Periodica 2024; 10.1556/606.2024.01211

Fig. 2.

Testing pier configurations including cylindrical (C), square (S), diamond (D), rectangle (Re), oval (OV), and ogival (OG)

Citation: Pollack Periodica 2024; 10.1556/606.2024.01211

3 HEC-RAS 1-D model

HEC-RAS's computational framework relies on solving the one-dimensional energy equation as per Manning's equation, friction, and contraction/expansion, which multiply the coefficient by velocity head, control energy losses in this manner. The momentum equation is helpful for quickly changing water surface profiles. Hydraulic leaps, bridge hydraulic dynamics, and stream junction profiles are examples of mixed flow regime computations. This program became much more helpful in 2006 when it could control and simulate sediment motion. From intricate grids to large river networks, this multifunctional program models aquatic properties well. It is versatile and can analyze turbulent, subcritical, and supercritical flow. Beyond these properties, culvert design and sediment transport modeling need it [30]. One of its more convincing applications is calculating scour around riverside bridge piers. This algorithm is complicated due to hydraulic flow data, bridge pier shape, and riverbed characteristics. The integrated CSU equation is the software's default simulation for assessing local scour depth at piers [14],

Y_{s} = 2 K_{1} K_{2} K_{3} K_{4} D^{0.65} Y^{0.35} {F r}^{0.43} .

(1)

The pier width is denoted by

D

, the flow depth is represented by

Y

, and the Froud number of upstream flows is denoted by

F r

. Here,

Y_{s}

is the scour depth,

K_{1,} K_{2}, K_{3}

and

K_{4}

. Accordingly, the correction factors for pier shape, direction of flow, bed situation, and bed conditions. The Frohlich equation, a different model included in the HEC-RAS program, also helps calculate the depth of bridge base scour. Here is how this equation is defined [31],

Y_{s} = 0.32 {(D)}^{0.62} Y^{0.47} {F r}^{0.22} D_{50}^{- 0.09} φ + D,

(2)

where

D

is the projected width of the pier,

φ

is the pier nose correction factor, and

D_{50}

is the median sand particle diameter. HEC-RAS is an integrated software system meticulously crafted by the Hydrologic Engineering Centre, a US Army Corps of Engineers division. Its primary function is modeling river networks, and it can handle one-dimensional (1D) and two-dimensional (2D) scenarios for unsteady flow.

4 Hydraulic modeling process

This study utilized HEC-RAS to predict the water level profile and scour depth around bridge piers. For this purpose, the required hydraulic and hydrological data were used. The experimental configuration had a flume of 12.5 m long, 0.3 m wide, and 0.6 m deep. The pier was positioned eight meters away from the upstream end, as it is shown in Fig. 3. The data necessary for modeling was gathered at precise time intervals and included topographic maps generated in AutoCAD. The maps enabled the creation of precise canal cross-sections before and after pier placement.

Fig. 3.

Visual representation of 1-D HEC-RAS model of the rectangular flume with cylindrical pier

Citation: Pollack Periodica 2024; 10.1556/606.2024.01211

Figure 4 shows the procedure of the approach methodology used for the 1D HEC-RAS hydraulic model. The input data in HEC-RAS included cross-sectional profiles, bed slope, Manning's roughness coefficient, and discharge values. Additional inputs included coefficients for flow expansion and contraction and the geometric characteristics of the pier and flume configuration. HEC-RAS operates on the assumption that flow contraction occurs when the downstream velocity exceeds the upstream velocity. For slight river cross-section changes the expansion coefficient was set at 0.3 and the contraction coefficient at 0.1. The values used were 0.3 and 0.5 to represent extreme shifts, particularly near bridges. By determining the hydraulic functioning of the bridge, the model derives appropriate equations. Experimental conditions are accurately presented through this system's Graphical User Interface (GUI) to remove extra hydraulic data input and alteration. The HEC-RAS model included pier scouring and sediment transport equations, validated using empirical measurements. Manning's roughness coefficient and slope geometry were adjusted to calibrate this model correctly. These changes ensured that water levels and scour depths at different sections corresponded precisely. HEC-RAS water level estimations and scour depths were compared with experimental flume data for verification purposes. This comparison confirmed that the model accurately reproduced the flowing sand/flow/pier design complex, thus demonstrating its trustworthiness in terms of accuracy. The model parameters were modified whenever inconsistencies occurred during this validation process to align the findings with the experimentally acquired data. By measuring cross sections at regular intervals, it was possible to estimate the carrying capacity for laboratory flumes along a channel due to its smaller size. The model needed a standard depth, bed slope, flow resistance factors, and precise channel geometry to replicate uniform flow conditions accurately. The bridge's geometric data, including its location on the canal, were inputted into HEC-RAS to depict the experimental model accurately. The HEC-RAS bridge model assigned a certain proportion to each of the six pier types: circular, square, diamond, rectangular, oblong, and ogival. The energy equation has variables like river height, water depth, mean flow velocity, acceleration due to gravity, and energy head loss that were employed to find water surface profiles across consecutive cross-sections. Expansion, friction, and contraction losses have been accounted for, allowing the calculation of head losses between cross-sections. Losses of this kind were calculated using Manning's equation, which is based on the hydraulic radius, flow area, discharge rate, and roughness coefficient. Manning's roughness coefficient is the resistance of a channel bed to water flow, typically ranges from 0.010 to 0.016 for very smooth surfaces like glass to rougher surfaces like alluvial channels with giant dunes. Various academic sources support this range as [32, 33]. Additionally, uniform and steady flow conditions, where velocity is constant and the channel is consistent, might also warrant a lower Manning's n because of reduced turbulence and resistance. A lower coefficient might be appropriate in scenarios with higher velocity flow over smooth surfaces as the water interacts less with surface roughness. Several Manning n values were studied concerning a bridgeless open-channel model, emphasizing average velocities at the mid-channel where the bridge existed. Notably, there was a significant difference between observed average velocities and those estimated.

Fig. 4.

Flow chart clarifying the approach methodology used for the 1D-HEC-RAS model establishing

Citation: Pollack Periodica 2024; 10.1556/606.2024.01211

5 Results and discussion

5.1 The pier scouring depth

Predicting the depth of erosion that occurs at diverse pier designs, for example the cylindrical, square, rectangular, diamond, oval, and ogival shapes, is an essential technique in understanding the hydraulic behavior of bridge construction. It applied Froehlich equations and integrated CSU with HEC-RAS software to better understand scouring depth under uniform flow conditions. As it is shown in Fig. 5, the results demonstrate significant diversity among pier shapes. The CSU method accurately predicted the measurement of square piers (5.94 cm), nearly matching the experimental value (6.00 cm). However, the Froehlich approach underestimated the scour depth, measuring 4.64 cm. The experimental value for cylindrical piers was estimated at 4.60 cm; however, the CSU technique expected it to be 5.40 cm, suggesting an overestimation. The Froehlich approach consistently underestimated the erosion depths for different pier designs, like oval and ogival piers. Though the Froehlich approach yielded lower estimations, experimental and CSU methodologies demonstrate considerable agreement regarding rectangular piers. The diamond-shaped pier indicated a scour depth of 5.50 cm from the observed method, while it showed a more excellent value of 6.70 cm by the CSU scheme. The results show significant variation in computational methods for estimating scour depth among different computational methods and designs of piers. However, this procedure superimposes the outcomes of tests despite its limitations, as the Root Mean Squared Error (RMSE) = 1.1 and Nash-Sutcliffe Efficiency (NSE) = −0.3 for the CSU technique. On average, it overestimates the one, which is indicated by Mean Absolute Percentage Error (MAPE) = 24%. This is especially obvious in design cases, where safety should be prioritized. However, with an RMSE of 0.7, a Nash-Sutcliffe efficiency of 0.49, and a mean absolute percentage error of 12%, the Froehlich equation (which is famous for being conservative) was able to show remarkably accurate results. In summary, the CSU method has lower accuracy on NSE and RMSE than the Froehlich method, implying that it provides accurate estimates and aligns well with data. Conversely, using its higher MAPE, the CSU approach tends to exaggerate scouring depths for safety reasons. These findings indicate that while each approach has its strengths and weaknesses when computing scour depth in this case, the best technique would be Froehlich's method instead, which is consistent with other research findings [19, 20, 28, 34, 35]. Therefore, sensitivity analysis is essential to determine pier design and flow characteristics presently not considered by existing models and accurately predict scouring depths around bridge structures.

Fig. 5.

Comparison of the scour depth results of various pier shapes from the experimental and the 1-D HEC-RAS models

Citation: Pollack Periodica 2024; 10.1556/606.2024.01211

5.2 Sensitivity analysis

The Monte Carlo simulation technique can assess the uncertainty and sensitivity of variables constituting a one-dimensional HEC-RAS CSU equation. This method entails producing several random samples for each parameter, accounting for their anticipated distributions, and then computing the respective scour depths. Analyzing the obtained data can provide information about the variables' sensitivity, the model's resilience, and the estimate's margin of error. This study primarily concentrates on various pier forms, with a particular emphasis on cylindrical piers. The study starts by selecting correlated variables, namely. ${K_{2}, K}_{3}$ , flow velocity ( $V$ ), and flow depth ( $Y$ ) as the components that significantly impact scour depth. Parameters, including $K$ (1, and 4), $F r$ , and $D$ , were not included in the sensitivity analysis. This was because their values remained constant or had been considered in the mathematical approach. Default values for ${K_{2}, K}_{3}$ , $V$ , and $Y$ are carefully set to ensure they align with the study's aims. For example, the values of ${K_{2}, K}_{3}$ , $V$ , and $Y$ are determined to be 1, 1.1, 0.12 cm, and 0.29 m s⁻¹. Sensitivity and correlation analyses were used to evaluate the impact of parameter variations on scour depth, focusing on the selected parameters. The sensitivity analysis and correlation results yield valuable information regarding the influence of various parameters on scour depth. The spider plot (Fig. 6) depicts the scour depth fluctuation based on the parameter $K_{2}$ percentile values. The values of ${K_{2}, K}_{3}$ , $V$ , and $Y$ vary. Among these characteristics, $K_{2}$ has the most considerable influence on scour depth, as evidenced by its most significant plot curve. This indicates that even minor adjustments in the value of $K_{2}$ may lead to significant variations in the depth of scouring. The graphs representing the parameter $K_{2}$ and flow velocity ( $V$ ) show a notable level of sensitivity, with $K_{2}$ having a somewhat more pronounced effect than flow velocity. Conversely, the flow depth ( $Y$ ) has a minimal influence on the scour depth, as shown by the horizontal curve. The association coefficients between these factors and scour depth are shown in the bar chart (Fig. 7). The high correlation coefficient demonstrates the most dependable linear link between scour depth and the measure $K_{2}$ . This data points to a strong correlation between variations in scour depth and shifts in $K_{2}$ . Because of its strong association, $K_{3}$ is considered a critical variable for scour depth prediction. Flow velocity ( $V$ ), although having a weak correlation coefficient, is crucial due to its high sensitivity, which is consistent with the observations from the spider plot. The correlation coefficient for flow depth ( $Y$ ) is the lowest, suggesting that it has a limited impact on the scour depth.

Fig. 6.

The sensitivity of changing in the testing variables value on the scour equation (CSU model)

Citation: Pollack Periodica 2024; 10.1556/606.2024.01211

Fig. 7.

The correlation value of the testing variables in the CSU model

Citation: Pollack Periodica 2024; 10.1556/606.2024.01211

The histogram (Fig. 8) displays the frequency distribution of the scour depth measurements. The distribution exhibits approximate normality, peaking ate 5.5 cm. This peak represents the prevailing scour depth values recorded in the data. The distribution's spread indicates that although values are clustered around the average, there is also a substantial range of scour depths, emphasizing the presence of variability. The variability is crucial for comprehending the spectrum of potential results and the corresponding hazards in projections of scour depth.

Fig. 8.

Histogram showing the distribution of scour depth values indicating the most common values and notable variability

Citation: Pollack Periodica 2024; 10.1556/606.2024.01211

The research identifies the best parameter combination that minimizes error percentages via an iterative process directed by the optimization objective function. To determine the global ideal combination, it is necessary to conduct 1,000,000 simulations, each representing a distinct combination of parameters, with the error threshold set at ±2%. This combination produces the optimum model performance with a minimal error of −0.013 percent. Consequently, the reliability and precision of the computational model for estimating the scour depth around cylindrical piers were enhanced. Subsequently, Fig. 9 (modified from Fig. 3) was generated after employing the optimal values of the variables. ${K_{2}, K}_{3}$ , $V$ , and $Y$ to the CSU equation for different pier shape cases to validate the sensitivity analysis results. The estimation error was decreased to 20% because of this modification; the RMSE is now 0.6, the NSE is 0.62, and the MAPE is 11%. The effectiveness of the conducted sensitivity analysis is confirmed by the validation method, which also underscores the importance of optimizing parameter combinations to improve the accuracy of the scour depth estimate. Predicting erosion depth around various kinds of piers is now easier using the numerical model because of its increased performance and decreased estimate error.

Fig. 9.

Validation of the modified variables of the CSU equations for different pier shapes

Citation: Pollack Periodica 2024; 10.1556/606.2024.01211

6 Conclusion

This study demonstrates that the HEC-RAS software effectively estimates erosion around bridge piers with specific design features. Based on the CSU equation, a comprehensive sensitivity analysis using the Monte Carlo method identified that the parameters K2, K3, flow depth, and velocity significantly impact scour depth predictions, with K2 showing the highest sensitivity. The results revealed notable differences between the CSU and Froehlich methods, with the CSU method generally overestimating scour depths, while the Froehlich method proved more conservative and realistic. The iterative optimization process, involving 1,000,000 simulations, substantially improved model accuracy, reducing the estimation error to 20%, RMSE to 0.6, NSE to 0.62, and MAPE to 11%. This optimization highlighted the effectiveness of the Froehlich method and the enhanced reliability of the HEC-RAS model. The study emphasizes the critical need to thoroughly analyze pier designs and hydraulic flow parameters to predict scouring depths accurately. The improved HEC-RAS modeling tool provides valuable insights for refining scour prediction techniques and enhancing bridge design and safety. However, the study has limitations. Calibration assumptions may compromise the model's accuracy and perform poorly under extreme flow conditions or highly variable sediment transport, which were not thoroughly investigated. Future research should address these limitations by expanding the model's application to various pier configurations and complex hydraulic conditions. Incorporating real-time data could further enhance the model's predictive accuracy and reliability. Addressing these areas will improve HEC-RAS's utility in bridge design and hydraulic engineering.

Funding

This research was funded by Széchenyi István University.

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Pollack Periodica

Print ISSN:: 1788-1994

Online ISSN:: 1788-3911

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

2023
Scopus
CiteScore	1.5
CiteScore rank	Q3 (Civil and Structural Engineering)
SNIP	0.849
Scimago
SJR index	0.288
SJR Q rank	Q3

Pollack Periodica
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