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Tahar Bachiri Department Mechanical and Civil Engineering, Faculty of Sciences ant Technology, Abdelmalek Essaadi University, Tangier 91001, Morocco

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Abdellatif Khamlichi Department STIC, National School of Applied Sciences, Abdelmalek Essaadi University, Tetouan 933030, Morocco

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Mohammed Hamdaoui Department of Physics, Faculty Polydisciplinary Nador, Mohammed First University, Morocco

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Mohammed Bezzazi Department Mechanical and Civil Engineering, Faculty of Sciences ant Technology, Abdelmalek Essaadi University, Tangier 91001, Morocco

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Ahmed Faize Department of Physics, Faculty Polydisciplinary Nador, Mohammed First University, Morocco

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Abstract

This study explores the impact of corrosion on Ground Penetrating Radar (GPR) responses through practical experiments and numerical modelling, focusing on rebar diameter reduction, corrosion product layer thickness, crack formation and corrosion product filling in vertical and transverse crack. Practical experiments involved GPR testing of reinforced concrete slab. By analyzing B-scans we identify areas with moderate and severe corrosion. Numerical modelling using the Finite Difference Time Domain (FDTD) Method to model GPR signal propagation in a concrete bridge deck with corrosion is applied. Key finding includes a significant 26.70% increase in reflected wave amplitude when corrosion product filling in vertical crack increased by 400%, highlighting its extensive effect on signal GPR propagation. Reduced rebar diameter led to a 9.79% amplitude decrease and a 0.06 ns arrival time delay. Increased corrosion product layer thickness primarily affected arrival time with a 0.06 ns extension but significantly amplified GPR signal amplitude. These findings offer insights for improving GPR based corrosion detection and assessment methods, leading to more robust systems for concrete bridge deck inspection and maintenance. This paper contributes to understanding how corrosion affects the signal that is detected by GPR. This information can be used to improve the way that we manage and assess corrosion in concrete bridge deck.

Abstract

This study explores the impact of corrosion on Ground Penetrating Radar (GPR) responses through practical experiments and numerical modelling, focusing on rebar diameter reduction, corrosion product layer thickness, crack formation and corrosion product filling in vertical and transverse crack. Practical experiments involved GPR testing of reinforced concrete slab. By analyzing B-scans we identify areas with moderate and severe corrosion. Numerical modelling using the Finite Difference Time Domain (FDTD) Method to model GPR signal propagation in a concrete bridge deck with corrosion is applied. Key finding includes a significant 26.70% increase in reflected wave amplitude when corrosion product filling in vertical crack increased by 400%, highlighting its extensive effect on signal GPR propagation. Reduced rebar diameter led to a 9.79% amplitude decrease and a 0.06 ns arrival time delay. Increased corrosion product layer thickness primarily affected arrival time with a 0.06 ns extension but significantly amplified GPR signal amplitude. These findings offer insights for improving GPR based corrosion detection and assessment methods, leading to more robust systems for concrete bridge deck inspection and maintenance. This paper contributes to understanding how corrosion affects the signal that is detected by GPR. This information can be used to improve the way that we manage and assess corrosion in concrete bridge deck.

1 Introduction

The assessment of reinforcement corrosion in concrete structures is crucial for ensuring their long term safety and durability. GPR has emerged as a promising non-destructive evaluation (NDE) technology for detecting and analyzing subsurface features in concrete bridge decks. Numerous studies have explored different approaches for analyzing GPR data, including visual interpretation and numerical analysis methods [1]. The use of numerical modeling tools, particularly the FDTD approach, has gained popularity for advanced GPR evaluation in complex near-surface environments, providing realistic simulations with accurate antenna models and inhomogeneous materials [2].

While some studies have reviewed the general application of GPR in civil engineering [34] or focused on on-site inspections for specific constructions [5], there is still a notable lack of comprehensive critical evaluation of GPR's effectiveness in corrosion assessment for reinforced concrete. GPR has demonstrated its utility in detecting buried networks and examining reinforced concrete structures, thanks to its improved vertical resolution and strong signal reflection from high conductivity bars [6–8]. The authors' objective is to gain deeper insights into the capabilities and limitations of GPR in detecting and quantifying reinforcement corrosion, aiming to enhance corrosion assessment practices in civil engineering structures.

This study aims to investigate the factors influencing the effectiveness of GPR for detecting corrosion in bridge deck reinforcements. The objective is to use numerical simulations with the FDTD method to understand the effects of corrosion-induced deterioration, such as rebar diameter reduction, corrosion layer formation, crack opening, and corrosion product filling, on the GPR signal response. The study will identify promising indicators of corrosion detection through numerical modeling and validate them using GPR data. The results will be compared and analyzed in comparison with existing literature, particularly with the findings of [9]. However, the section related to previous studies seeks to identify relevant publications on corrosion assessment using GPR and explore the relationship between numerical modeling of bridge deck reinforcement corrosion testing and the practical on-site application of GPR.

2 Previous studies

GPR has emerged as a valuable non-destructive testing technique for assessing the health and condition of reinforced concrete structures. It enables the detection and evaluation of various defects, such as rebar corrosion, concrete corrosion product, and cracks, which are critical in ensuring the structural integrity of these elements [10]. This literature review aims to comprehensively analyze the current state of research on GPR-based corrosion detection and its impact on the reflected wave characteristics in concrete structures.

Experiments conducted by [11] with rebars embedded in concrete observed a decrease in the amplitude of the GPR reflected wave, indicative of corrosion-induced changes in the electromagnetic properties of concrete. Similarly, pre-corroded rebars and embedded by [12] in a concrete-like oil emulsion, reporting a decrease in the reflected wave amplitude and an increase in travel time. These findings underscore the potential of GPR as a reliable technique for detecting rebar corrosion.

The study by [13] revealed that GPR is capable of visualizing concrete corrosion product, but its detectability is influenced by various factors. Thickness, material (air or water) within the corrosion product, peak frequency of the emitted signal, and the depth of the corrosion product relative to neighboring steel bars can impact its detectability in GPR images. Synthetic and real data were used to arrive at these conclusions, emphasizing the importance of these parameters for accurate corrosion product assessment.

Tests conducted by [14] with advanced corrosion levels on rebars embedded in concrete, showed a decrease in the amplitude of the GPR reflected wave and an increase in travel time. Rebar is corroded artificially by [15] for 10 days, resulting in a decrease in the reflected wave amplitude and an increase in travel time. These studies further support GPR's potential for corrosion detection in concrete structures.

All Ref. [16] and [17, 18] are observed an increase in the amplitude of the GPR reflected wave and a decrease in travel time after corroding rebars in concrete. Also, noticed by Ref. [19] similar trends in their study. These findings suggest that corrosion-induced changes in electromagnetic properties significantly impact the GPR reflected signal, highlighting GPR's sensitivity to rebar corrosion.

The authors of [20] significantly contributed to the knowledge in the field of reinforcement corrosion detection using GPR by focusing on electromagnetic wave propagation in composites and analyzing wave reflection from corrosion product defects. Their work allowed for the correlation of reflection pattern image features to corrosion product defect characteristics, enabling quantification of corrosion product damage in composites used in aerospace and aircraft industries. This research enhanced the understanding of GPR's potential for nondestructive evaluation in detecting corrosion-related defects. Similarly, the authors of [21] provided valuable insights into the estimation of rebar radius buried in concrete using GPR. Their derived mathematical formula and Hough transform procedure enabled accurate rebar radius retrieval, enhancing the accuracy of corrosion detection. Although they acknowledged the sensitivity to noise levels, their contributions expanded the application of GPR for reinforcement corrosion assessment.

The study in [22] reviewed existing literature on GPR methods for investigating reinforced concrete structures. The case study presented demonstrated improved detectability of rebars using a high-frequency dual-polarized antenna system, offering potential advancements in GPR data processing and analysis.

Researchers have emphasized GPR's effectiveness in mapping and monitoring bridge deck health, detecting deterioration factors, such as corrosion product, rebar corrosion, vertical cracking, and concrete degradation [10]. The effect of work frequency and corrosion layer thickness on the input reflection coefficient has been analyzed to improve the accuracy of GPR-based corrosion detection [23].

Numerical simulations using GprMax software have been utilized to analyze the detectability of defects in reinforced concrete structures [24]. Moreover, various studies have explored the effects of corrosion, attenuation, scattering, and quantitative estimation of corrosive effects on rebars using GPR data [12, 15–19, 25, 26]. Image-based and non-image analyses of GPR radargram images have been recommended for corrosion identification and monitoring [1, 27]. Additionally, hybrid approaches involving multiple NDE techniques, such as Infrared Thermography and Ultrasonic Surface Waves, have been explored to determine corrosion decay [26].

Numerical modeling of bridge deck reinforcement corrosion based on analysis of GPR data is a promising area of research with significant contributions. Recent strides in GPR simulation algorithms have honed the precision, accuracy, and efficiency of modeling. Exploration of diverse forward modeling algorithms, medium models, mesh generation, and boundary conditions has marked significant progress [2, 3, 25]. The integration of innovative algorithms like the Discontinuous Galerkin finite element method, spectral element method, and symplectic Euler method into GPR simulations is noteworthy.

The integrity of GPR data is critical for precise data interpretation, as electromagnetic waves can disperse and diffract, complicating the extraction of useful information from recorded images. GPR's application in assessing bridge deck reinforcement corrosion is an evolving research field, offering insights into structural integrity and corrosion levels, crucial for maintenance and safety evaluations. The numerical simulation of GPR for this purpose holds substantial promise for advancing corrosion understanding and predictive modeling for infrastructure management. Future research directions include refining simulation algorithms, enhancing data processing, and broadening GPR's application scope in civil engineering and beyond [6, 7, 9, 20, 23].

The impact of corrosion on GPR waveforms, the development of numerical models, and the integration of GPR with other NDE techniques have shown great potential in enhancing the accuracy of corrosion detection and quantification. However, further research is needed to fully understand the complex interaction between GPR signals and corrosion in concrete structures, paving the way for improved bridge deck condition assessment and maintenance strategies.

3 Materials and methods

3.1 Modelling hyperbolic signature of corrosion product in concrete deck bridge

GPR employs emitted electromagnetic pulses to penetrate and interact with materials of differing dielectric properties, yielding reflections that aid target localization. Radar waves can penetrate layers based on frequency and depth [28]. Electromagnetic (EM) wave pulses scatter at interfaces with abrupt dielectric changes, detecting embedded targets. The receiver captures scattered and reflected pulses for subsurface mapping as showed in Fig. 1.

Fig. 1.
Fig. 1.

A buried corroded rebar in a B-scan configuration

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00740

The dielectric properties of EM waves are described by the permittivity, ε, which is a complex number consisting of the dielectric constant, ε′, and the loss factor, ε″. The permittivity can be written as [29]:
ε=εjε
where j is the imaginary unit.
The wave velocity, v, is related to the dielectric constant by the following equation:
v=cε
where c is the speed of light in vacuum.
The attenuation coefficient, α, is a measure of how much the amplitude of an EM wave decreases as it travels through a material. It is related to the electrical conductivity, σ, by the following equation:
α=σ2με
where μ is the magnetic permeability of the material.
The frequency at which EM waves propagate through a material depends on the electrical conductivity, σ, and the permittivity, ε of the material. The critical frequency, fc, is defined as the frequency below which the waves propagate as evanescent waves and above which they propagate as oscillatory waves. The critical frequency is given by the following equation:
fc=σ2πε0εr
where ε0 is the permittivity of free space.
The wavelength, λ, of EM waves in a material is related to the speed of light, c, and the permittivity, ε, of the material by the following equation:
λ=cεr
where εr is the relative permittivity of the material.

Equation (5) implies that lower εr results in longer wavelengths, while higher εr leads to shorter wavelengths.

Corrosion can alter the dielectric constants of materials, impacting the reflection of electromagnetic waves at interfaces. Changes in dielectric constants lead to variations in the amplitude of reflected waves, allowing the detection of different subsurface layers.

In GPR data, the hyperbolic patterns originate from reflections that transpire at the interface of the target, with variations in the separation between the antenna and the target. For the purpose of our analysis, we will consider a simplified two-dimensional scenario, akin to a B-scan, where the target is envisioned as a circular object with a radius denoted as ‘r,'. the formula linking GPR signal wavelength (λ) to corrosion product thickness (d) is expressed as by [13]:
d>c50f

For a frequency of 1.6 GHz, the minimum corrosion product thickness (d) must exceed approximately 1.875 mm to maintain a ratio between the GPR signal wavelength and the corrosion product thickness below 50, ensuring clear visibility in the B-scan image, as illustrated in Fig. 2.

Fig. 2.
Fig. 2.

Radargram corresponding to a B-scan

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00740

The vertical resolution (Δv) is determined through the time interval between adjacent reflections τ1 and τ2 at layer boundaries. This time difference relates to depth as Δτ = (τ1 - τ2). The formula for vertical resolution is [7]:
Δv=v*Δτ/2=λ/2=c/(2f)

This calculation assumes a simple time-depth relationship based on medium properties and pulse duration (τp). It represents the minimum resolvable spacing for GPR (Fig. 3).

Fig. 3.
Fig. 3.

The GPR footprint in the medium

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00740

The horizontal resolution (Δh) is determined by the time delay (Δt₀) and the velocity (v) of the radar signal. In references [7, 30], Δh is gauged as the radius of the antenna's footprint (2A) that all part outside this area will have no significant reflection, as encapsulated by the ensuing equation:
Δh=λ4+h0εr+1=2λh02=2vΔt0c

This formulation signifies the minimal distinguishable interval between adjoining targets that GPR system can discern.

Power Reflectivity (Pr) quantifies the reflection of electromagnetic waves at subsurface interfaces. Changes in dielectric constants due to corrosion lead to variations in Pr values, indicating differences between layers with varying material properties. Under the premise that distinctions in electrical conductivity (σ) and magnetic permeability (μ) can be deemed insignificant, the reflection coefficient is formulated as follows:
Pr=[εr1εr2εr1+εr2]2
By synthesizing the aforementioned equations, the resultant formulation can be established in accordance with the presentation by [31]:
r=(2πLλ)(4πfvλ)(2πc2t0v2)
where r is the radius of the rebar and L is the length of the contrast zone on the radargram
x=r(2πLλ)+(4πfvλ)+(2πc2t0v2)

This equation shows that the radius of loss due to corrosion product thickness x, can be calculated by subtracting the contribution of the measured length of the contrast zone, the influence of the velocity of electromagnetic waves and the frequency of the GPR signal, and the role of time delay and velocity in characterizing the spatial resolution of the GPR system from the radius of the rebar. Each term in the equation corresponds to a specific physical characteristic (Table 1).

Table 1.

Physical attributes of Equation (10) component terms

TermsSignifications
2πL/λRepresents the contribution of the measured length of the contrast zone on the radargram. It accounts for the spatial extent of the subsurface region being analyzed.
-4πfv/λReflects the influence of the velocity of electromagnetic waves and the frequency of the GPR signal. This term contributes to the accuracy in estimating the distance between subsurface layers
-2πc2t₀/v2This term accounts for the role of time delay (Δt₀) and velocity (v) in characterizing the spatial resolution of the GPR system. It helps in distinguishing adjacent features

Equation (11) embodies a composite metric encompassing spatial attributes and attributes that influence radar signal behavior. Variations in dielectric constants arising from corrosion influence Power Reflectivity, thereby causing fluctuations in the amplitude of reflected electromagnetic waves. The continuous monitoring of this equation across time facilitates the early detection of potential corrosion concerns in reinforcement rebars. The synergy between this equation and GPR technology empowers timely intervention through the identification of corrosion at an incipient stage. Experts analyze equation (11) and its terms to detect corrosion by identifying shifts in material properties and subsurface interfaces.

In practical applications, the correlation proposed by reference [32] between relative permittivity (εr) and density is used to derive the formula for calculating εr for the accumulated corrosion product:
εr=[((εf)131)(ρaρf)+1]3
In this equation, εf represents the relative permittivity of the free-expanded corrosion product (14.2, as detailed in Table 2), while ρa and ρf denote the densities of the accumulated and free-expanded corrosion products, respectively. The ratio ρa/ρf corresponds to Vf/Va, where Vf represents the volume of the free-expanded corrosion product, and Va approximates the volume of the accumulated corrosion product (approximated as the consumed steel volume). Given that Vf/Va equals the volume expansion ratio (Vf/Va = 1.7, as cited in [33]), Equation (13) can be expressed as:
εr=[((εf)131)1.7+1]3

Consequently, the calculated relative permittivity of the accumulated corrosion product, εa, is determined to be 39.9.

The formula used to compute the critical corrosion amount (CCA) as a percentage of the initial mass of the rebar is given by:
CCA=(MasslossduetocorrosionInitialmassofrebar)100

Here, the mass loss due to corrosion represents the amount of mass lost from the rebar due to corrosion, and the initial mass of the rebar is its mass before corrosion.

The GPR paradox, as described in reference [3], arises from complex corrosion stages, including free expansion, stress buildup, and concrete cracking [33]. During the free expansion phase, oxidized rebar corrosion products accumulate in the interfacial transition zone (ITZ) between concrete and steel. This accumulation leads to increased stress during the stress buildup phase, ultimately resulting in concrete cracking and the expansion of crack width.

Based on existing data, it is determined that CCA triggers concrete cover cracking in c/d = 2.0 reinforced concrete when it reaches 4.0% [34], which is the ratio of the initial rebar mass loss due to corrosion. Reference [9] establishes the value of c/d as 1.8. Using these values, an approximation of CCA yields a radius loss of 0.5 mm.

3.2 Conducting a practical experiment

In order to conduct GPR testing, an investigation was carried out using a reinforced concrete slab, depicted in Fig. 4. The GPR system employed was the MALÅ CX model by MALA Geosciences, featuring a high antenna frequency of 1.6 GHz and a penetration depth of approximately 36 cm. The data collection process involved longitudinal radar lines (labeled as P1 to P4) and transverse radar lines (designated as P5 to P9). Subsequent data analysis was performed using ReflexW V.7.5 software. The specific GPR data acquisition parameters are outlined in Table 2.

Fig. 4.
Fig. 4.

Scheme of GPR data acquisition

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00740

Table 2.

GPR MALÅ CX data parameters

Antenna:Centre frequency 1.6 GHz
GPR line Sampling Time:1 scans/cm
Bandwidth:1,600 MHz

Bandwidth from 800 to 2,400 MHz
Time windows:9 ns

To depict GPR data from concrete structures with embedded rebars or in scenarios where features are expected to vary significantly along the survey line, B-scans are commonly used. A B-scan is created by stacking individual A-scans along the survey line and converting their amplitudes into pixel intensities, resulting in a two-dimensional image. Figures 5, 7 and 8 illustrate typical B-scans obtained from ground-coupled GPR. In these figures, the vertical axis represents the two-way travel time of the signals, while the horizontal axis shows the antenna's traveling distance (location of each A-scan). It is important to note that the hyperbolic patterns seen in the B-scans are indicative of steel reinforcement. By employing B-scans and A-scans, we gained valuable insights into the condition and integrity of the reinforced concrete structure, especially in areas with corrosion and potential risks of deterioration. These findings are crucial for assessing the long-term performance and safety of such concrete structures.

Furthermore, the degradation of steel rebars due to oxidation was investigated. This process was observed to lead to the deterioration of steel rebars in areas with moderate corrosion, as illustrated in Figs 6 and 7. The result of this oxidation is the creation of a non-adhering porous layer around the metal. Such a phenomenon has the potential to culminate in the complete deterioration of concrete structures. Notably, damage to the surrounding concrete and the appearance of cracks around oxidized elements were also observed. B-scan radargrams obtained from specific radar lines are displayed as follows.

Fig. 5.
Fig. 5.

B-scan acquired along line P2

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00740

Fig. 6.
Fig. 6.

B-scan radargram obtained from the longitudinal radar line P4

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00740

In Figures 5, 7 and 8, areas displaying blurriness were identified, indicating severe corrosion where the reinforcement had undergone nearly complete oxidation. This occurrence resulted in the formation of a substantial porous layer around the remaining parts of the reinforcement. Moreover, the concrete not bonded to the reinforced surface exhibited cracks. A significant contribution to the understanding of reinforcement corrosion detection through GPR. Their findings, particularly illustrated in Figs 58, showcase the profound impact of corrosion, leading to the creation of substantial porous layers and surface cracks. Previous studies [1, 27] offer valuable supporting tools and advocate GPR radargram analysis for corrosion assessment, underlining its pertinence. By analyzing signal attenuation patterns over time, insights into the presence of corrosive conditions and the extent of reinforcement deterioration are extracted, enriching the comprehension of corrosion variables.

Fig. 7.
Fig. 7.

B-scan radargram acquired along transversal radar line P6

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00740

Fig. 8.
Fig. 8.

B-scan radargram acquired along transversal radar line P8

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00740

A drill core, shown in Fig. 9, was extracted from the concrete structures for the purpose of assessing its mechanical strength. The core's composition was meticulously measured and comprised a 0.8 cm tile, 5 cm screed, 24 cm reinforced concrete, and 11 cm coarse concrete. Unfortunately, the compressive strength was found to be unsatisfactory at 15.5 MPa.

Fig. 9.
Fig. 9.

Drill core extracted from the concrete structures

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00740

In this study, GPR testing using the MALÅ CX model revealed significant corrosion in a reinforced concrete slab. B-scans identified corrosion-induced porous layers, cracks, and potential structural deterioration. This research contributes to understanding corrosion detection and its impact on concrete structures. Subsequently, numerical investigation was undertaken to explore the potential influence of corrosion in this practical experiment.

3.3 Simulation of a case of study

In this study, we utilized the FDTD method to analyze GPR responses from a concrete deck bridge (Fig. 1). Introduced by [35], this well-established method is widely employed to simulate GPR signal responses from intricate targets, owing to its explicit computational efficiency and conditional stability. We discretized both spatial and temporal domains. Smaller steps improved model fidelity at the expense of increased computational resources. Despite the limitations of a 2D model, especially for non-truly two-dimensional targets and a line source, it provided valuable insights into general 2D GPR responses [36].

Our simulation focused on a concrete deck bridge with corrosion, utilizing the FDTD method to model electromagnetic wave propagation. The simulation domain measured 0.6 m by 0.3 m, with a consistent grid spacing of 0.0005 m in both the x and y directions. The temporal window was set at 8.0 ns. The simulation comprised four distinct media types: concrete, corrosion, asphalt, and free space, each characterized by specific dielectric constants and conductivities. Table 3 details the dielectric properties of substances within our simulation model. Geometrically, two boxes represented the concrete deck bridge and asphalt overlay, while two cylinders depicted the corrosion product layer and rebar. Electromagnetic waves were emitted from a line source located at 0.075 and 0.22 m, generating a Ricker wavelet at 1.6 GHz.

Table 3.

Dielectric properties of materials in the simulation model

MediaRelative Dielectric Constant (εr)Electrical Conductivity (σ) (S m−1)Relative Magnetic Permeability (μr)
Concrete6.20.0051
Rebar1.459.93 × 106200
Corrosion14.20.0071
Air101

The FDTD method facilitated the creation of a numerical model for the concrete deck bridge within GprMax software. This model aimed to investigate the impact of rebar diameter reduction, corrosion product layer thickness, and crack width on wave propagation. Concrete was represented as a dielectric material with variable properties dependent on its composition and moisture content. Rebar was modeled as a metallic conductor, while corrosion product layers and cracks were simulated as structural imperfections.

The simulation involved transmitting a wave pulse into the concrete structure and recording the resulting wave propagation. We systematically varied rebar diameter, corrosion product layer thickness, and crack width to analyze their effects on wave propagation, with the simulation results presented in yellow:

  • Model 1: Rebar diameter reduction

To simulate the loss of steel sections in the corrosion process, four different rebar diameter levels were set from 25 to 19 mm. Only the diameter of the rebar was defined as a variable, with the depth of the center fixed. The results are illustrated in Fig. 10, which shows the contrast of the signal curves for different sizes of rebar.

Fig. 10.
Fig. 10.

GPR reflected waves for different sized rebar

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00740

The results show that the reduction in rebar diameter affects the reflected wave, with the amplitude decreasing and the arrival time delaying as the rebar diameter decreases. This is because GPR waves have to travel a longer distance in concrete as the steel becomes thinner. In the model, reducing rebar diameter by 24% (from 25 to 19 mm) resulted in a 9.79% decrease in reflected wave amplitude and an arrival time prolonged by 0.06 ns. The change is due to the fact that a smaller rebar diameter means the GPR waves have a longer path to travel in the concrete, as there is less metallic material to reflect the signal.

  • Model 2: Corrosion product layer thickness

To study the effects of corrosion product layer thickness, simulations were conducted with three different thicknesses: 1.7 mm, 3.2 mm, and 4.7 mm. The diameter of the rebar was fixed at 25 mm for all thicknesses, and the other parameters of the model were also fixed. The results are shown in Fig. 11, which shows the wave travel time of GPR increasing with the increase in rebar corrosion. Despite this substantial increase in thickness, the arrival time only extended by 0.06 ns, similar to the delay caused by rebar diameter reduction. This suggests that the corrosion product layer has a less pronounced effect on the travel time of the GPR signal compared to the rebar diameter.

Fig. 11.
Fig. 11.

GPR signal variation for corrosion product layer thickness

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00740

The results show that increasing the thickness of the corrosion product layer by 176% (from 1.7 to 4.7 mm) only extended the arrival time by 0.06 ns. However, the effect of the thickness of the corrosion product layer on the amplitude of the GPR signal is obvious. The change in the thickness of the corrosion product layer in the concrete influenced the intensity of the GPR responses. The corrosion product layer could affect the signal's intensity in a way that is not easily discernible from the arrival time alone. It is possible that other factors, such as the material properties of the corrosion product or its interaction with the electromagnetic waves, play a role in altering the signal amplitude.

  • Model 3: Combined rebar diameter reduction and corrosion product expansion

To investigate the influence of concurrent rebar diameter reduction and corrosion product layer thickness increase on GPR responses, we conducted numerical simulations employing three distinct combinations of rebar diameters and corrosion product layer thicknesses. Additionally, we assessed GPR signals corresponding to varying corrosion levels on the rebar. Figure 12 illustrates the corrosion product layer thicknesses associated with rebar radius reductions of 1 mm, 2 mm, and 3 mm, resulting in corrosion product thicknesses of 1.7 mm, 3.2 mm, and 4.7 mm, respectively.

Fig. 12.
Fig. 12.

GPR responses related to different rebar diameters and corrosion levels

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00740

Our modeling approach employed three sets of rebar parameters: 23 mm diameter with 1.7 mm thickness, 21 mm diameter with 3.2 mm thickness, and 19 mm diameter with 4.7 mm thickness. This allowed us to simulate rebar specimens with diverse corrosion levels and assess the effects of varying rebar diameters and corrosion product thicknesses.

Across different corrosion levels, we observed fluctuations in both the amplitude and arrival time of the reflected wave in the GPR responses generated by this model. Specifically, when applying a 17.4% reduction in rebar diameter and a 176% increase in corrosion product thickness, the reflected wave amplitude decreased by 8.23%, while the travel time increased by 0.05 ns. This reduction in rebar diameter and simultaneous increase in corrosion product thickness were consistent with the effects of corrosion on the rebar's physical dimensions.

  • Model 4: Corrosion development before concrete cracking

Concrete structures experience steel bar corrosion-induced volume expansion, potentially exerting pressure on the surrounding concrete and initiating surface cracks. Identifying the Critical Corrosion Amount (CCA) is essential for predicting concrete structure service life in corrosive environments. We simulated the CCA to assess its impact on GPR signals, comparing them with pre-corrosion responses. Figure 13 shows two waveforms: one from the simulated reinforcing bar before corrosion, and the other representing the critical corrosion amount. Initially, the reinforcing bar had a diameter of 25 mm and was covered by 45 mm of concrete.

Fig. 13.
Fig. 13.

GPR signal variation for rebar corrosion (CCA)

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00740

As corrosion progressed, the rebar diameter decreased, and corrosion products accumulated in the zone between the concrete and corroded rebar, including the initial interfacial transition zone (ITZ) and consumed rebar volume. We approximated the zone thickness as the radius loss, resulting in a cover thickness-to-reinforcing bar diameter ratio (c/d) of 1.8. Using literature data, a c/d ratio of 2.0 corresponded to a 4.0% CCA, representing mass loss due to corrosion. We assumed this as our model's approximate CCA, yielding a calculated radius loss of 0.5 mm. In our model, varying the degree of corrosion before cover cracking led to minor signal changes. Corrosion progression from the initial state to the CCA resulted in a 4.04% decrease in amplitude and a 0.02 ns increase in travel time for the reflected wave.

  • Model 5: Effects of concrete crack width due to corrosion

This numerical simulation study aims to investigate the influence of crack width variations, induced by corrosion development, on GPR responses. In the models illustrated in Fig. 14, we explore two prevalent types of cracks: transverse cracks (Fig. 14a) and vertical (Fig. 14b). We base our crack width parameters on observations from laboratory experiments, where a longitudinal crack of 2 mm width emerged after days of accelerated corrosion [21]. We simulate four levels of crack width, ranging from 0.5 to 2.0 mm with 0.5 mm increments, to replicate the progressive crack widening caused by corrosion.

Fig. 14.
Fig. 14.

Crack width effects on corrosion transverse (a) and vertical (b) crack

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00740

For consistency with the conditions set in section before, we maintain a fixed diameter of 24 mm for the corroded rebar and a 0.5 mm thickness for the corrosion product layer. Figure 15 presents the outcomes derived from our numerical models, showcasing the effects of varying crack widths for both transverse cracks (Fig. 15a) and vertical (Fig. 15b). Notably, there are no significant alterations in the travel time of the radar signal's reflected waves. However, we observe pronounced changes in the amplitude of the reflected wave concerning the width of both vertical and transverse cracks. Specifically, vertical cracks exhibit a 9.92% decrease in reflected wave amplitude with a 300% increase in crack width (from 0.5 to 2.0 mm), while transverse cracks experience a substantial 23.76% amplitude reduction under the same conditions.

Fig. 15.
Fig. 15.

Radar waveform variation for corrosion crack width transverse (a) and vertical (b) crack

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00740

  • Model 6: Corrosion product filling in concrete vertical cracks

The effects of the amount of corrosion product filling in a concrete vertical crack on the GPR response were studied by simulating three different heights of corrosion product: 10 mm, 20 mm, and 30 mm. The corroded rebar diameter and corrosion product layer thickness were fixed. Figure 16 presents on the corrosion product effects on concrete crack.

Fig. 16.
Fig. 16.

Corrosion product effects on concrete crack

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00740

The results, shown in Fig. 17, indicate that the amount of corrosion product filling has a significant impact on the GPR response. Both the amplitude and arrival time of the reflected wave increase with increasing height of the corrosion product filling in the vertical crack. Increasing the corrosion product filling in vertical cracks by 200% (from 10 to 30 mm) resulted in a 25.51% increase in reflected wave amplitude and a 0.01 ns delay in arrival time.

Fig. 17.
Fig. 17.

GPR signal variation for corrosion product in concrete vertical cracks

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00740

  • Model 7: Corrosion product filling in both concrete and asphalt vertical Cracks

In Fig. 18, we implemented three distinct levels of corrosion product height 10 mm, 30 mm, and 50 mm to represent varying quantities of corrosion product filling. This setup aimed to simulate the accumulation of corrosion product within vertical cracks in both concrete and asphalt, induced by corrosion development. To assess the isolated effects of corrosion product filling, we kept the corroded rebar diameter and corrosion product layer thickness constant. Importantly, the corrosion product filling within the vertical crack was allowed to expand freely.

Fig. 18.
Fig. 18.

Corrosion product effects on both asphalt and concrete crack

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00740

The simulated GPR responses, presented in Fig. 19, emphasize the critical role of the amount of corrosion product filling in the GPR response. It is evident that increasing corrosion product filling within vertical cracks by 400% (from 10 to 50 mm) results in a substantial 26.70% increase in reflected wave amplitude.

Fig. 19.
Fig. 19.

GPR signal variation for corrosion product in both asphalt and concrete vertical cracks

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00740

4 Discussion

For a comparison between the main findings in our work and those presented in [09], it is evident that our research investigates similar parameters related to rebar diameter reduction, corrosion product layer thickness, and their combined effects on reflected wave characteristics in concrete structures.

In this work, as compared to the findings in reference [09], reveals notable insights into the behavior of waveforms under different corrosion-related conditions. Notably, reducing rebar diameter by 24% led to a 9.79% decrease in reflected wave amplitude in our work, aligning with the observation in [09] of decreased amplitudes with decreased rebar size. However, our study additionally noted a 0.06 ns prolongation in arrival time, which was not reported in [09]. Moreover, our investigation into corrosion product layer thickness indicated that increasing it by 176% primarily affected the arrival time with a 0.06 ns extension, while [09] emphasized the increase in travel time alone. The combined effect of rebar diameter reduction and corrosion product expansion showed a consistent decrease in amplitude, but our study noted a 0.05 ns increase in travel time, which complements the findings in [09].

Additionally, we explored corrosion development before concrete cracking, demonstrating a 4.04% decrease in amplitude and a 0.02 ns increase in travel time at critical corrosion levels, aligning with the observations in [09]. However, our work extended these findings by studying the effects of concrete crack width due to corrosion. We found that a 300% increase in crack width led to decreased reflected wave amplitudes of 9.92% (vertical cracks) and 23.76% (transverse cracks), which were not discussed in [09].

Our investigation into corrosion product filling in vertical cracks revealed that a 200% increase in filling resulted in a 25.51% increase in reflected wave amplitude and a 0.01 ns delay in arrival time. In contrast, [09] mainly highlighted the amplitude increase associated with corrosion product spread in the crack. Furthermore, our study extended the investigation to corrosion product filling in both concrete and asphalt vertical cracks, showing a 26.70% increase in reflected wave amplitude for a 400% increase in filling.

In previous studies, the failure modes of the reinforced concrete samples were different, and the variations of the radar signal were thus distinct, due to the different setups of the corrosion experiments. Table 3 presents a synopsis of investigations concerning corrosion detection using GPR. The research findings are grouped by authors and encompass aspects like experimental setups and alterations in GPR-detected signals.

Rebar diameter reduction and corrosion product layer formation decrease the amplitude of the reflected wave, while cover concrete cracking and corrosion product filling increase the amplitude of the reflected wave. The travel time of the reflected wave slightly increases with the reduction of rebar diameter, but it decreases with the development of the corrosion. This study found reasonable agreement between the numerical models and the experimental results available in the literature. However, the authors acknowledge that the modeling approach has some simplifications that may affect the accuracy of the predictions (Table 4).

Table 4.

Overview of research in GPR-Based reinforcement corrosion detection

Experimental SetupChange in GPR Reflected Wave
Rebar, corroded artificially for 10 days in consistently wet concrete, exhibited changes in GPR signals [15]Decreased amplitude; Increased travel time
Corrosion through impressed current caused considerable accumulation of corrosion product on the concrete surface[17–18]Increased amplitude; Decreased travel time
Impressed current-induced corrosion resulted in observable accumulation and filling of corrosion product via core samples [19]Increased amplitude; Decreased travel time
Rebars of varying diameters were directly embedded into concrete, showing no concrete surface cracks [11].Decreased amplitude; Unchanged travel time
Rebars, pre-corroded and meticulously cleaned, were embedded in a concrete-like oil emulsion. No cracks appeared on the specimen [12].Decreased amplitude; Increased travel time
Rebars corroded to differing percentage losses in a sodium chloride solution were embedded in concrete. No concrete surface cracks were visible [37].Decreased amplitude; Unchanged travel time
Rebar, corroded within concrete for 30 days, displayed substantial accumulation of corrosion products atop the concrete surface [16]Increased amplitude; Decreased travel time
Test A: Concrete-embedded rebar with advanced corrosion (reduced diameter, expanded rust layer) showed no concrete surface cracks.

Test B: Rebars corroded under artificial ambient conditions, embedded in concrete, revealed no concrete surface cracks [14]
Test A: Decreased amplitude; Increased travel time.

Test B: Decreased amplitude; Increased travel time.
Rebar corrosion in concrete led to the emergence of wide-open cracks post-accelerated corrosion [38].Unchanged amplitude; Decreased travel time

5 Conclusions

This study investigated the effects of corrosion on GPR responses through practical experiments and numerical simulations. The practical experiments involved GPR testing of a reinforced concrete slab with different levels of corrosion. GPR testing revealed significant corrosion in a reinforced concrete slab. B-scans identified corrosion-induced porous layers, cracks, and potential structural deterioration. This research contributes to understanding corrosion detection and its impact on concrete structures.

The numerical simulations employed the FDTD method to model GPR signal propagation in a concrete deck bridge with corrosion. The main findings of the study are as follows:

  1. We observed that the reduction in rebar diameter by 24% resulted in a 9.79% decrease in reflected wave amplitude and a 0.06 ns delay in arrival time.

  2. Increasing the thickness of the corrosion product layer by 176% led to a 0.06 ns delay in arrival time, with significant effects on the amplitude of the GPR signal.

  3. Reduction in rebar diameter and increase in corrosion product thickness resulted in an 8.23% decrease in reflected wave amplitude and a 0.05 ns delay in arrival time.

  4. Corrosion progression to a 4.0% Critical Corrosion Amount (CCA) caused a 4.04% decrease in amplitude and a 0.02 ns increase in travel time.

  5. Varying crack width due to corrosion resulted in a 9.92% amplitude reduction for vertical cracks and a 23.76% reduction for transverse cracks with a 300% increase in crack width.

  6. Increasing the height of corrosion product filling in vertical cracks by 200% resulted in a 25.51% increase in reflected wave amplitude and a 0.01 ns delay in arrival time.

  7. Expanding corrosion product filling within vertical cracks by 400% led to a substantial 26.70% increase in reflected wave amplitude. These findings highlight the significant impact of various corrosion-related factors on GPR responses.

These findings can be used to develop more robust and accurate GPR-based corrosion detection and assessment methods. Additionally, the FDTD model developed in this study can be used to generate synthetic GPR data for training and testing models for corrosion detection.

The findings of this study have important implications, which are developing a novel powered GPR system for corrosion detection and assessment in concrete structures. The study's results can be used to improve the understanding of the relationship between GPR responses and corrosion parameters.

Additionally, the FDTD model developed in this study can be used to generate synthetic GPR data for training and testing models. This will help to improve the performance of the models in real-world applications.

Future research will investigate the cracking process of the cover concrete induced by rust expansion through a mechanical model of rust expansion. This will help to better understand the quantitative relationship among the corrosion loss of the corroded rebar, the distribution and density of the rust, and the crack width of the cover concrete, which will lead to the optimization of the proposed modeling approach.

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  • [1]

    A. Tarussov, M. Vandry, and A. De La Haza, “Condition assessment of concrete structures using a new analysis method: ground-penetrating radar computer-assisted visual interpretation,” Constr. Build. Mater., vol. 38, pp. 12461254, 2013.

    • Search Google Scholar
    • Export Citation
  • [2]

    N. J. Cassidy, “A review of practical numerical modelling methods for the advanced interpretation of ground‐penetrating radar in near‐surface environments,” Near Surf. Geophys., vol. 5, no. 1, pp. 521, 2007.

    • Search Google Scholar
    • Export Citation
  • [3]

    W. W. L. Lai, X. Derobert, and P. Annan, “A review of ground penetrating radar application in civil engineering: a 30-year journey from locating and testing to imaging and Diagnosis,” Ndt & E Int., vol. 96, pp. 5878, 2018.

    • Search Google Scholar
    • Export Citation
  • [4]

    F. Tosti and C. Ferrante, “Using ground penetrating radar methods to investigate reinforced concrete structures,” Surv. Geophys., vol. 41, no. 3, pp. 485530, 2020.

    • Search Google Scholar
    • Export Citation
  • [5]

    S. A. Dabous and S. Feroz, “Condition monitoring of bridges with non-contact testing technologies,” Automat. Constr., vol. 116, 2020, Art no. 103224.

    • Search Google Scholar
    • Export Citation
  • [6]

    T. Bachiri, G. Alsharahi, A. Khamlichi, M. Bezzazi, and A. Faize, “GPR application in Civil Engineering to search and detect underground Networks,” Int. J. Emerg. Trends Eng. Res., vol. 8, no. 5, 2020. https://doi.org/10.30534/ijeter/2020/59852020.

    • Search Google Scholar
    • Export Citation
  • [7]

    T. Bachiri, M. Hamdaoui, G. Alsharahi, M. Bezzazi, and A. Faize, “Numerically based qualitative evaluation of GPR vertical and horizontal resolutions for reinforced concrete structures,” in MATEC Web of Conferences, vol. 371, EDP Sciences, 2022, 02002. https://doi.org/10.1051/matecconf/202237102002.

    • Search Google Scholar
    • Export Citation
  • [8]

    T. Bachiri, G. Alsharahi, A. Khamlichi, M. Bezzazi, and A. Faize, “Ground penetrating radar data acquisition to detect imbalances and underground pipes,” in WITS 2020. Lecture Notes in Electrical Engineering, vol. 745, S. Bennani, Y. Lakhrissi, G. Khaissidi, A. Mansouri, and Y. Khamlichi, Eds., Singapore: Springer, 2022. https://doi.org/10.1007/978-981-33-6893-4_92.

    • Search Google Scholar
    • Export Citation
  • [9]

    S. Hong, D. Chen, and B. Dong, “Numerical simulation and mechanism analysis of GPR-based reinforcement corrosion detection,” Constr. Build. Mater., vol. 317, 2022, Art no. 125913.

    • Search Google Scholar
    • Export Citation
  • [10]

    T. Bachiri, A. Khamlichi, and M. Bezzazi, “Bridge deck condition assessment by using GPR: a review,” in MATEC Web of Conferences, vol. 191, no. 1, EDP Sciences, 2018, 00004. https://doi.org/10.1051/matecconf/201819100004.

    • Search Google Scholar
    • Export Citation
  • [11]

    D. Eisenmann, F. Margetan, C. P. T. Chiou, R. Roberts, and S. Wendt, “Ground penetrating radar applied to rebar corrosion inspection,” in AIP Conference Proceedings, vol. 1511, American Institute of Physics, 2013, January, pp. 13411348.

    • Search Google Scholar
    • Export Citation
  • [12]

    M. I. Hasan and N. Yazdani, “An experimental study for quantitative estimation of rebar corrosion in concrete using ground penetrating radar,” J. Eng., 2016, 2016.

    • Search Google Scholar
    • Export Citation
  • [13]

    K. Dinh and N. Gucunski, “Factors affecting the detectability of concrete delamination in GPR images,” Constr. Build. Mater., vol. 274, 2021, Art no. 121837.

    • Search Google Scholar
    • Export Citation
  • [14]

    V. Sossa, V. Pérez-Gracia, R. González-Drigo, and M. A. Rasol, “Lab non destructive test to analyze the effect of corrosion on ground penetrating radar scans,” Remote Sensing, vol. 11, no. 23, p. 2814, 2019.

    • Search Google Scholar
    • Export Citation
  • [15]

    S. S. Hubbard, J. Zhang, P. J. Monteiro, J. E. Peterson, and Y. Rubin, “Experimental detection of reinforcing bar corrosion using nondestructive geophysical techniques,” Mater. J., vol. 100, no. 6, pp. 501510, 2003.

    • Search Google Scholar
    • Export Citation
  • [16]

    R. K. Raju, M. I. Hasan, and N. Yazdani, “Quantitative relationship involving reinforcing bar corrosion and ground-penetrating radar amplitude,” ACI Mater. J., vol. 115, no. 3, pp. 449457, 2018.

    • Search Google Scholar
    • Export Citation
  • [17]

    W. L. Lai, T. Kind, and H. Wiggenhauser, “Using ground penetrating radar and time–frequency analysis to characterize construction materials,” NDT & E Int., vol. 44, no. 1, pp. 111120, 2011.

    • Search Google Scholar
    • Export Citation
  • [18]

    W. L. Lai, T. Kind, M. Stoppel, and H. Wiggenhauser, “Measurement of accelerated steel corrosion in concrete using ground-penetrating radar and a modified half-cell potential method,” J. Infrastruct. Syst., vol. 19, no. 2, pp. 205220, 2013.

    • Search Google Scholar
    • Export Citation
  • [19]

    S. Hong, W. W. L. Lai, G. Wilsch, R. Helmerich, R. Helmerich, T. Günther, and H. Wiggenhauser, “Periodic mapping of reinforcement corrosion in intrusive chloride contaminated concrete with GPR,” Constr. Build. Mater., vol. 66, pp. 671684, 2014.

    • Search Google Scholar
    • Export Citation
  • [20]

    Z. Mechbal and A. Khamlichi, “Detection of corrosion product in composites by using electromagnetic penetrating radar,” Int. Rev. Appl. Sci. Eng., vol. 5, no. 2, pp. 151156, 2014. https://doi.org/10.1556/irase.5.2014.2.7.

    • Search Google Scholar
    • Export Citation
  • [21]

    Z. Mechbal and A. Khamlichi, “The effect of noise on the estimation of rebar radius by inversion of GPR scan data,” Int. Rev. Appl. Sci. Eng., vol. 6, no. 2, pp. 95100, 2015.

    • Search Google Scholar
    • Export Citation
  • [22]

    F. Tosti and C. Ferrante, “Using ground penetrating radar methods to investigate reinforced concrete structures,” Surv. Geophys., vol. 41, no. 3, pp. 485530, 2020.

    • Search Google Scholar
    • Export Citation
  • [23]

    T. Bachiri, A. Khamlichi, and M. Bezzazi, “Detection of rebar corrosion in bridge deck by using GPR,” in MATEC web of conferences, vol. 191, EDP Sciences, 2018, 00009 https://doi.org/10.1051/matecconf/201819100009.

    • Search Google Scholar
    • Export Citation
  • [24]

    T. Bachiri, M. Hamdaoui, G. Alsharahi, M. Bezzazi, and A. Faize, “Examinating GPR based detection of defects in RC builds,” in MATEC Web of Conferences, vol. 360, EDP Sciences, 2022, 00003.

    • Search Google Scholar
    • Export Citation
  • [25]

    K. Dinh, T. Zayed, S. Moufti, A. Shami, A. Jabri, M. Abouhamad, and T. Dawood, “Clustering-based threshold model for condition assessment of concrete bridge decks with ground-penetrating radar,” Transport. Res. Rec., vol. 2522, no. 1, pp. 8189, 2015.

    • Search Google Scholar
    • Export Citation
  • [26]

    M. Solla, S. Lagüela, N. Fernández, and I. Garrido, “Assessing rebar corrosion through the combination of nondestructive GPR and IRT methodologies,” Remote Sensing, vol. 11, no. 14, p. 1705, 2019.

    • Search Google Scholar
    • Export Citation
  • [27]

    K. Dinh, T. Zayed, and A. Tarussov, “GPR image analysis for corrosion mapping in concrete slabs,” in Canadian Society of Civil Engineering 2013 Conference Proceedings, vol. 30, 2013, p. 1.

    • Search Google Scholar
    • Export Citation
  • [28]

    H. M. Jol, “Ground penetrating radar antennae frequencies and transmitter powers compared for penetration depth, resolution and reflection continuity1,” Geophys. Prospecting, vol. 43, no. 5, pp. 693709, 1995.

    • Search Google Scholar
    • Export Citation
  • [29]

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Senior editors

Editor-in-Chief: Ákos, Lakatos University of Debrecen (Hungary)

Founder, former Editor-in-Chief (2011-2020): Ferenc Kalmár University of Debrecen (Hungary)

Founding Editor: György Csomós University of Debrecen (Hungary)

Associate Editor: Derek Clements Croome University of Reading (UK)

Associate Editor: Dezső Beke University of Debrecen (Hungary)

Editorial Board

  • Mohammad Nazir AHMAD Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Malaysia

    Murat BAKIROV Center for Materials and Lifetime Management Ltd. Moscow Russia

    Nicolae BALC Technical University of Cluj-Napoca Cluj-Napoca Romania

    Umberto BERARDI Ryerson University Toronto Canada

    Ildikó BODNÁR University of Debrecen Debrecen Hungary

    Sándor BODZÁS University of Debrecen Debrecen Hungary

    Fatih Mehmet BOTSALI Selçuk University Konya Turkey

    Samuel BRUNNER Empa Swiss Federal Laboratories for Materials Science and Technology

    István BUDAI University of Debrecen Debrecen Hungary

    Constantin BUNGAU University of Oradea Oradea Romania

    Shanshan CAI Huazhong University of Science and Technology Wuhan China

    Michele De CARLI University of Padua Padua Italy

    Robert CERNY Czech Technical University in Prague Czech Republic

    György CSOMÓS University of Debrecen Debrecen Hungary

    Tamás CSOKNYAI Budapest University of Technology and Economics Budapest Hungary

    Eugen Ioan GERGELY University of Oradea Oradea Romania

    József FINTA University of Pécs Pécs Hungary

    Anna FORMICA IASI National Research Council Rome

    Alexandru GACSADI University of Oradea Oradea Romania

    Eric A. GRULKE University of Kentucky Lexington United States

    Janez GRUM University of Ljubljana Ljubljana Slovenia

    Géza HUSI University of Debrecen Debrecen Hungary

    Ghaleb A. HUSSEINI American University of Sharjah Sharjah United Arab Emirates

    Nikolay IVANOV Peter the Great St.Petersburg Polytechnic University St. Petersburg Russia

    Antal JÁRAI Eötvös Loránd University Budapest Hungary

    Gudni JÓHANNESSON The National Energy Authority of Iceland Reykjavik Iceland

    László KAJTÁR Budapest University of Technology and Economics Budapest Hungary

    Ferenc KALMÁR University of Debrecen Debrecen Hungary

    Tünde KALMÁR University of Debrecen Debrecen Hungary

    Milos KALOUSEK Brno University of Technology Brno Czech Republik

    Jan KOCI Czech Technical University in Prague Prague Czech Republic

    Vaclav KOCI Czech Technical University in Prague Prague Czech Republic

    Imra KOCSIS University of Debrecen Debrecen Hungary

    Imre KOVÁCS University of Debrecen Debrecen Hungary

    Angela Daniela LA ROSA Norwegian University of Science and Technology

    Éva LOVRA Univeqrsity of Debrecen Debrecen Hungary

    Elena LUCCHI Eurac Research, Institute for Renewable Energy Bolzano Italy

    Tamás MANKOVITS University of Debrecen Debrecen Hungary

    Igor MEDVED Slovak Technical University in Bratislava Bratislava Slovakia

    Ligia MOGA Technical University of Cluj-Napoca Cluj-Napoca Romania

    Marco MOLINARI Royal Institute of Technology Stockholm Sweden

    Henrieta MORAVCIKOVA Slovak Academy of Sciences Bratislava Slovakia

    Phalguni MUKHOPHADYAYA University of Victoria Victoria Canada

    Balázs NAGY Budapest University of Technology and Economics Budapest Hungary

    Husam S. NAJM Rutgers University New Brunswick United States

    Jozsef NYERS Subotica Tech College of Applied Sciences Subotica Serbia

    Bjarne W. OLESEN Technical University of Denmark Lyngby Denmark

    Stefan ONIGA North University of Baia Mare Baia Mare Romania

    Joaquim Norberto PIRES Universidade de Coimbra Coimbra Portugal

    László POKORÁDI Óbuda University Budapest Hungary

    Antal PUHL (1950–2023) University of Debrecen Debrecen Hungary

    Roman RABENSEIFER Slovak University of Technology in Bratislava Bratislava Slovak Republik

    Mohammad H. A. SALAH Hashemite University Zarqua Jordan

    Dietrich SCHMIDT Fraunhofer Institute for Wind Energy and Energy System Technology IWES Kassel Germany

    Lorand SZABÓ Technical University of Cluj-Napoca Cluj-Napoca Romania

    Csaba SZÁSZ Technical University of Cluj-Napoca Cluj-Napoca Romania

    Ioan SZÁVA Transylvania University of Brasov Brasov Romania

    Péter SZEMES University of Debrecen Debrecen Hungary

    Edit SZŰCS University of Debrecen Debrecen Hungary

    Radu TARCA University of Oradea Oradea Romania

    Zsolt TIBA University of Debrecen Debrecen Hungary

    László TÓTH University of Debrecen Debrecen Hungary

    László TÖRÖK University of Debrecen Debrecen Hungary

    Anton TRNIK Constantine the Philosopher University in Nitra Nitra Slovakia

    Ibrahim UZMAY Erciyes University Kayseri Turkey

    Tibor VESSELÉNYI University of Oradea Oradea Romania

    Nalinaksh S. VYAS Indian Institute of Technology Kanpur India

    Deborah WHITE The University of Adelaide Adelaide Australia

International Review of Applied Sciences and Engineering
Address of the institute: Faculty of Engineering, University of Debrecen
H-4028 Debrecen, Ótemető u. 2-4. Hungary
Email: irase@eng.unideb.hu

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2022  
Scimago  
Scimago
H-index
9
Scimago
Journal Rank
0.235
Scimago Quartile Score Architecture (Q2)
Engineering (miscellaneous) (Q3)
Environmental Engineering (Q3)
Information Systems (Q4)
Management Science and Operations Research (Q4)
Materials Science (miscellaneous) Q3)
Scopus  
Scopus
Cite Score
1.6
Scopus
CIte Score Rank
Architecture 46/170 (73rd PCTL)
General Engineering 174/302 (42nd PCTL)
Materials Science (miscellaneous) 93/150 (38th PCTL)
Environmental Engineering 123/184 (33rd PCTL)
Management Science and Operations Research 142/198 (28th PCTL)
Information Systems 281/379 (25th PCTL)
 
Scopus
SNIP
0.686

2021  
Scimago  
Scimago
H-index
7
Scimago
Journal Rank
0,199
Scimago Quartile Score Engineering (miscellaneous) (Q3)
Environmental Engineering (Q4)
Information Systems (Q4)
Management Science and Operations Research (Q4)
Materials Science (miscellaneous) (Q4)
Scopus  
Scopus
Cite Score
1,2
Scopus
CIte Score Rank
Architecture 48/149 (Q2)
General Engineering 186/300 (Q3)
Materials Science (miscellaneous) 79/124 (Q3)
Environmental Engineering 118/173 (Q3)
Management Science and Operations Research 141/184 (Q4)
Information Systems 274/353 (Q4)
Scopus
SNIP
0,457

2020  
Scimago
H-index
5
Scimago
Journal Rank
0,165
Scimago
Quartile Score
Engineering (miscellaneous) Q3
Environmental Engineering Q4
Information Systems Q4
Management Science and Operations Research Q4
Materials Science (miscellaneous) Q4
Scopus
Cite Score
102/116=0,9
Scopus
Cite Score Rank
General Engineering 205/297 (Q3)
Environmental Engineering 107/146 (Q3)
Information Systems 269/329 (Q4)
Management Science and Operations Research 139/166 (Q4)
Materials Science (miscellaneous) 64/98 (Q3)
Scopus
SNIP
0,26
Scopus
Cites
57
Scopus
Documents
36
Days from submission to acceptance 84
Days from acceptance to publication 348
Acceptance
Rate

23%

 

2019  
Scimago
H-index
4
Scimago
Journal Rank
0,229
Scimago
Quartile Score
Engineering (miscellaneous) Q2
Environmental Engineering Q3
Information Systems Q3
Management Science and Operations Research Q4
Materials Science (miscellaneous) Q3
Scopus
Cite Score
46/81=0,6
Scopus
Cite Score Rank
General Engineering 227/299 (Q4)
Environmental Engineering 107/132 (Q4)
Information Systems 259/300 (Q4)
Management Science and Operations Research 136/161 (Q4)
Materials Science (miscellaneous) 60/86 (Q3)
Scopus
SNIP
0,866
Scopus
Cites
35
Scopus
Documents
47
Acceptance
Rate
21%

 

International Review of Applied Sciences and Engineering
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 1100 EUR/article
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Limited number of full waiver available. Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription Information Gold Open Access

International Review of Applied Sciences and Engineering
Language English
Size A4
Year of
Foundation
2010
Volumes
per Year
1
Issues
per Year
3
Founder Debreceni Egyetem
Founder's
Address
H-4032 Debrecen, Hungary Egyetem tér 1
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-0810 (Print)
ISSN 2063-4269 (Online)

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