1. Introduction
Nature-based solutions (NBSs) are widely adopted to minimize climate change impacts and enhance resilience in areas of meteorological risk to society, such as flooding and sustainable development [1,2]. In the area surrounding D.G. Khan, a total of 13 hill torrents, Kaura, SoriJanubi, Mithawan, Sanghar, Pitok, Vehova, SakhiSarwar, Kaha, Sorilund, Chadhar, SoriShumali, Zangi, and Vidor, serve as conduits for floodwater from nearby catchments [3]. These torrents enter the Indus River from the right bank of the Chashma River, the D.G. Khan canal, and the Kachhi canal, originating from the Koh-e-Suleman Range. In 2021, a research-based study was conducted to recommend risk management solutions for the Koh-e-Suleiman hilly areas to protect the nearby community, reduce damage to infrastructure, and minimize damage to already standing crops in the event of a channel breach. Climate change has increased the flood frequency and magnitude with concentrated rainfall contributing to floods in river catchments. Flash flooding is difficult to predict in countries like Pakistan, which has varied topography, with steep slopes in hilly areas, and is severely impacted by climate change [4]. When heavy monsoon rainfall take place in the Koh e Suleman hills (D.G. Khan), the surface runoff from different hilly terrains starts flowing toward the connected plain areas in the form of stormwater, causing damage to standing crops and nearby populations. The flooding generated from these hill torrents, like the Mithwan hill torrents, Kaura hill torrents, Vehova, and Sakhi Sarwar in D.G. Khan, has high peaks within a very short time [5]. In the rainy season, due to intense rainfall, water flows from the hill torrents towards the lower plain areas. This heavy rainfall is the main cause of flash flooding in these areas every year [6].
The discharge at the outflow region highly depends on various factors, including the topography of the catchment area, total catchment area, and rainfall intensity and duration. Flash floods are amongst the most dangerous types of floods, occurring suddenly and not allowing for enough response time, resulting in the enormous loss of human lives, standing crops, and livestock [7,8,9]. People usually have less time to respond to these types of flash floods, resulting in an enormous loss of human lives, standing crops, and livestock. Many hilly areas in Pakistan are hill torrents. Among these, the hill torrents in Southern Punjab and Baluchistan have steep slopes, and barren mountainous regions are responsible for flash floods. Pakistan’s constrained resources have contributed minimally to flash flood routing and management research [10].
There is a critical need for a floodwater management plan to mitigate the impacts of hill torrents, particularly during the monsoon season. Such a plan may involve structures capable of withstanding large water quantities and reducing the impact of hill torrents. While various models have quantified runoff from ungauged catchments, there has been a limited focus on developing mitigation strategies for flash floods [11]. Developing rainfall-runoff relations from green surfaces can also be used to calibrate traditional infiltration models in urban drainage engineering [12,13].
More knowledge about the potential application of nature-based solutions is needed to help engineers and practitioners to cope with flash floods. One of the main concerns is the protection of the nearby population, homes, and other assets from flash flooding in the rainy season. This research focuses on peak discharge, the time required to reach peak discharge, how the peak discharge can be decreased, and how the time to reach peak discharge can be increased.
Rainfall-runoff relation can be developed using physical, empirical, and conceptual models [14]. Based on the existing data, the empirical model can be applied to develop the relationship between rainfall and runoff. However, artificial neural networks [15] or fuzzy logic have been used by researchers as a conceptual modeling technique [16]. Moreover, rainfall simulators have been used by many researchers to generate runoff in a study area, which were further used to predict erosion along roads [17,18]. They have also been used to develop a relationship between sediment yield and runoff under variable rainfall intensities in a vineyard plantation in Spain [19]. Hence, researchers have indicated that a rainfall simulator could be a useful tool for representing natural rainfall conditions [20]. The runoff volume and peak discharge estimation are important measurements for designing hydraulic structures [21]. Various models can be used to simulate the rainfall-runoff relationship, resulting in input data used for the design of the structures. Among these models, synthetic hydrographs have been used to quantify runoff responses generated from hill torrents with a dense canopy [22,23]. In the current study, rainfall-runoff responses are analyzed by using a rainfall simulator at a laboratory scale. This study simulates natural conditions in hill torrents that are susceptible to flash floods using a lab-scaled hilly model. The complexity of computing hydrological processes in hilly terrains is addressed, in contrast to conventional studies in plain areas designed using a laboratory model, which uses impermeable thermopore sheets to reduce complexity and incorporates nature-based solutions. By considering a few variables, including land cover, rainfall intensity, and drainage channel slope, this study assesses flood hydrograph attenuation and offers insightful information about the mitigating effects of various vegetation conditions, similar to those used by [24,25,26]. Runoff was measured from the hilly model without any vegetation, and the results were compared with flexible vegetation (grass bed), rigid vegetation (tree branches), and mixed vegetation (a combination of both rigid and flexible). The role of vegetation is to create resistance on the surface flow path. Rigid vegetation, being less dense, is only a direct obstruction against rainfall impact on the land’s surface, providing limited resistance. In contrast, flexible vegetation has shown more resistance, as surface runoff is continuously facing resistance in its path [27]. The combination of both rigid and flexible vegetation results in a collaborative resistance against surface runoff, making this mixed approach the most efficient.
2. Materials and Methods
In this study, a rainfall simulator FM-1849-45 by Infinit Technologies, Rosedale, MD, USA (
The designed rigid vegetation model for the experimental analysis used in the rainfall simulator apparatus is shown in Figure 1b, representing the hilly area model with trees on a sloped surface. The dimensions of the catchment area of the rainfall simulator were 1 m (W) × 2 m (L) × 0.12 m (H). The hill model used in the experiment was constructed from polystyrene sheets with a length of 1.85 m and a width of 1 m. The width of inclined region A4 or A2 of the model represented by light blue arrows was 0.26 m, while the width of the rectangular channel A3 between the two inclined hilly areas A2 and A4 was 0.23 m. The model’s remaining 0.25 m width (A1 and A5) was flat, as depicted in Figure 1c. The model’s dimensions on each channel side are symmetrical, with areas A1 = A5 and A2 = A4.
After placing the model without vegetation (NV) (Figure 2a), the rainfall sprinkles were started, keeping uniform rainfall throughout the catchment area. The rigid vegetation (RV), i.e., tree branches, was placed on the polystyrene model (Figure 2b) to examine the effect of rigid vegetation on the runoff generation. The tree model was placed in a staggered arrangement, having two lanes on each side of A2 and A4. The average height of each tree was 25 cm. To represent flexible vegetation (FV) like grass and bushes, an artificial 9 mm tall grass carpet was used, as shown in Figure 2c. Finally, the combined effect of both flexible and rigid vegetation (MV) was observed by placing both RV and FV types of vegetation, as shown in Figure 2d. During the experimental work, there were three rainfall intensities, i.e., P1 = 0.3 cm/min, P2 = 0.4 cm/min, and P3 = 0.5 cm/min, used to examine the effect of rainfall intensity on hydrograph size and shape.
The runoff was measured using the weir installed on the downstream side of the rainfall simulator. The time-to-peak discharge (Tp) was calculated indirectly from the incrementally measured outflow data—for each unit liter, outflow time was measured with a stopwatch. The peak discharge (Qp) was measured by developing the hydrograph for each case. For each case, the rainfall simulator was run for 5 min and then switched off. After stopping the rainfall, the outflow continued through the catchment, producing different outflow hydrographs for each case, depending upon the land use conditions, the rainfall intensity, and the channel slope. The outflow hydrograph was simplified into three main components—rising limb, peak, and falling limb, as shown in Figure 3—and all the cases used in the experiments are presented in Table 1.
Peak discharge for each experimental case was measured directly from the respective hydrograph, and similarly, the time-to-peak was also calculated from the corresponding hydrograph peak, as shown in Figure 3. The relative reduction of peak discharges for rigid vegetation (RV), flexible vegetation (FV), and mixed vegetation (MV) are measured by comparing them with the benchmark values of barren (NV) conditions. For the percentage relative peak discharge reduction, the following equation was used:
(1)
For the percentage relative time-to-peak reduction, the following equation was used:
(2)
where i represents the type of vegetation to which the relative time-to-peak or the relative peak discharge is being calculated, and i,NV represents baseline condition for each respective case.3. Results
3.1. Peak Discharge
It was observed that for different vegetation conditions, i.e., NV, RV, FV, and MV with rainfall intensities of P1, P2, and P3, a similar runoff pattern emerged for all bed slopes. Initially, the peak discharge was highest for the no vegetation condition, gradually declining for the case of rigid vegetation and further for the case of flexible vegetation, as given in Table 2. The mixed vegetation condition consistently exhibited the lowest peak discharge across these scenarios. The absolute Qp values were highest for the steepest channel slope and smallest for the flat channel bed (Figure 4, Figure 5 and Figure 6), as expected.
3.2. Relative Peak Discharge
It was found that for all the vegetation conditions used at the 0° slope, the rainfall intensity from P1 to P2 and P3 increases the percentage relative peak discharge reduction compared to the respective no vegetation condition [28]. When slopes of 1° and 2° were provided to the drainage channel, this trend changed. At the 1° and 2° channel slopes, the percentage relative peak discharge was highest for P1, and it decreased as rainfall intensity increased to P2 and P3 compared to the respective no vegetation conditions. The difference in Qp attenuation for the same vegetation type was negligible under different rainfall intensities at the 0° slope. When the channel slope increased to 1° and 2°, Qp attenuation decreased with the increase in rainfall intensity, with notable differences for different experiments. This analysis shows that both the channel slope and vegetative cover contribute to Qp attenuation, but vegetation is the main parameter which has a significant influence. The most efficient vegetation type found was mixed vegetation, which offered significant improvements over RV and FV [29]. Moreover, the experimental analysis showed that flexible vegetation significantly decreased the peaks compared to rigid vegetation, as shown in Figure 7, Table 3.
3.3. Time-to-Peak
Time-to-peak discharge is the key factor in defining the time for the community to respond to a flood [30,31]. The Tp for all experiments decreased with the increase in the rainfall intensity, emphasizing the fact that the rainfall duration was shorter than the catchment concentration time (Table 4 and Table 5). Similar to the Qp analysis, the Tp increased from RV, over FV to MV, revealing that MV contributes the most to both the decrease in Qp and increase in Tp, which when combined, reduced the flood risk. The MV case at slope 0° under rainfall intensity P3 showed the maximum resistance to the flow, followed by the FV and RV cases.
It was found that the time-to-peak (Tp) increased as the vegetation condition changed from barren to rigid, from rigid to flexible, and from flexible to mixed vegetation conditions. The impact of slope and rainfall intensity followed the same trend as the Tp duration. As we increased the slope from 0° to 1° and 2° and increased the rainfall intensity from P1 to P2 and P3, the time-to-peak (Tp) duration decreased with all other conditions remaining the same (Table 4 and Table 5). The experimental analysis for Tp showed that the slope and rainfall intensity both had an inverse relation with Tp (Table 4 and Table 5). It was analyzed that Tp follows a distinct pattern, where the MV condition consistently produces the longest Tp, followed by the FV condition, which exhibits a shorter duration. Moreover, the rigid vegetation condition, although more structured than the mixed and flexible vegetations, also demonstrated a relatively shorter Tp when compared with the mixed and flexible vegetation conditions. An increase in the channel slope resulted in a decrease in Tp, but the presence of the vegetation still significantly reduced the Tp. When the channel slope was the steepest, the difference between the MV and FV cases became negligible for all rainfall intensities. The results also show that the maximum relative percentage of the Tp reduction was achieved for the mixed vegetation and 0°slope (81% under P3 rainfall) (Figure 8 and Figure 9). The minimum relative percentage of the Tp (1%) was observed in the case of the rigid vegetation condition at a 2° slope under P3 rainfall, showing that the effect of rigid vegetation is severely limited.
4. Discussion
4.1. Comparison of the Findings to the Literature
Hilly areas subject to flash flooding produced by hill torrents were the focus of this [32] study, which concentrated on applying nature-based solutions to reduce the impact of flash floods. A laboratory-scale hill slope model was developed to examine the rainfall-runoff responses and evaluate the efficiency of various NBS configurations in lowering the peak discharge and increasing the time-to-peak [33]. The rigid vegetation used in this study showed a resistance in the range of 8 to 15% to reduce flood peaks due to rainfall. Flexible and a combination of both rigid and flexible vegetations reduced the flood peaks from 12 to 33% and 27 to 39%, respectively. There are numerous instances of governments using tree planting as a flood control measure throughout the world. To lessen flooding, the municipal authorities of Pickering, North Yorkshire, England, planted trees as part of the project “Slowing the Flow.” According to a scheme analysis, the measures decreased peak river flow by 15–20%. The program was launched in 2009 following the town’s four significant floods in a ten-year period, with the 2007 floods resulting in damage estimated at around £7 million.
Similar findings were reported in the laboratory-based study by Chouksey et al. [25], who also used a rainfall simulator over an experimental hillslope plot to investigate hydrological modeling, providing a better understanding of the effectiveness of nature-based solutions in mitigating flash flood impacts. The current findings follow the same trends as the field-based study by Flores et al. [26], who compared three daily rainfall-runoff hydrological models using four evapotranspiration models in four small, forested watersheds with different land covers in South-Central Chile. This demonstrates that the presented insights can directly find application in real-world situations.
Flexible vegetation throughout the catchment retained some amount of rainfall water and also effectively resisted surface runoff to reach the catchment outlet. This vegetation effectively increased the time to flow from the catchment to the outlet and decreased the flood peak by delaying the process.
The current experimental design utilized thermopore sheets made of impermeable material, which enables controlled simulations of various NBS conditions. These design considerations simplified the experimental setup and reduced the model complexity but may also have impacted its representativeness compared to real-life scenarios. Future studies may explore more realistic soil types and vegetation characteristics for the hillslope model to offer a more accurate assessment of the effectiveness of nature-based solutions. Also, the use of a laboratory-scaled hillslope model may not fully capture the complexities of real-world hillslope conditions. The conversion of scientific discoveries into useful applications is facilitated using laboratory-sized models that enable controlled experiments that can be replicated and scaled up to real-world situations [34]. For such cases, the potential impact of human activities, such as land use changes and urbanization on flash flooding in hilly terrains, can be better assessed to inform the development of more effective strategies for flash flood resilience.
This study’s results emphasize the significant effect of vegetation and ground slope in reducing flash flooding impacts. The mixed vegetation condition with a channel slope of 0° was found to be the most effective for minimizing severe flash flooding, as was the case in other studies [35,36]. This study offers important insights into developing long-term solutions by demonstrating the efficiency of NBS in flood mitigation, particularly in mountainous regions. The peak discharge was simultaneously minimized, while the maximum Tp under P3 rainfall was produced. Compared to previous NBS cases, the mixed vegetation condition reduced the peak discharge up to 39% and increased the Tp by 81%. Practitioners should consider integrating nature-based solutions, such as mixed vegetation, into land use planning and development strategies for hilly terrains. This can help to reduce the risk of flash flooding and improve community resilience by increasing the Tp and reducing peak discharge, according to the findings herein. This could include providing financial incentives for landowners to adopt mixed vegetation or other nature-based solutions as well as incorporating these strategies into broader flood risk management plans and policies. Raising awareness about the benefits of nature-based solutions for flash flood mitigation and engaging local communities in their implementation can also help sustain their success.
4.2. Future Research Direction
The results of the conducted research show that mixed vegetation conditions with a 0° bed slope are particularly effective for increasing the Tp and lowering the Qp. This study offers important new information for applying improved flood management practices and aims to provide the groundwork for future investigations and NBS applications to lessen the negative effects of flash flooding and improve community resilience [13,35], building upon the already known benefits of nature-based solutions in reducing the impacts of floods in hilly areas subject to flash flooding [32,37]. Future research could focus on improving the laboratory-scale hillslope model to use better field conditions. This might involve incorporating more realistic soil types, vegetation characteristics, and hydrological processes to enhance the model’s accuracy and applicability to real-world scenarios. Further, additional land use and climate change conditions can be explored in future studies. These, for example, could investigate the effectiveness of different land use conditions and vegetation types in mitigating flash flooding in hilly terrains. This could help identify the most effective nature-based solutions for specific regions and inform the development of targeted strategies for flash flood resilience. Future research could examine the performance of nature-based solutions, including to what extent vegetation is useful, under various climate change scenarios, such as increased rainfall intensity or more frequent extreme weather events. This would provide valuable insights into the long-term effectiveness of these solutions and help in the form of adaptation strategies for flash flood resilience in a changing climate.
5. Conclusions
Decreasing the peak discharge and increasing the time-to-peak can reduce the impact of flash floods by providing the community with more time to respond. Laboratory models can be used to evaluate the sustainability and long-term viability of nature-based solutions for flood mitigation in real conditions, providing information about land cover density and type and different rainfall patterns that might occur due to climate change. The experimental results of this study show that the mixed vegetation condition is the best one to reduce the peak discharge and increase the time-to-peak under different rainfall intensities and channel bed slopes. It was also observed that flexible vegetation contributed much more than rigid vegetation to increasing the time-to-peak and mitigating the flood peak:
The peak discharge of a hydrograph was positively correlated with rainfall intensity and the channel bed slope. An increase in either of these factors led to a higher peak of the hydrograph and vice versa.
The hydrograph formation for the no vegetation condition exhibited the maximum peak discharge, while the mixed vegetation condition, comprising both flexible and rigid vegetation, showed the minimum peak discharge.
Flexible vegetation showed greater resistance to runoff than rigid vegetation.
The order of resistance to flow for time-to-peak discharge increased from rigid to flexible and was the highest for the mixed vegetation condition.
The time-to-peak discharge of the hydrograph was negatively correlated with rainfall intensity and channel bed slope.
The mixed vegetation condition with the lowest bed slope and maximum rainfall intensity of P3 reduced the peak discharges by 39% and increased the time-to-peak by 81%. The same mixed vegetation condition reduced the peak discharge by almost 27% and increased the time-to-peak discharge by 24% under the same rainfall condition with the maximum channel slope.
Conceptualization, S.U.R., A.A. and R.F.; methodology, S.U.R., A.A. and G.A.P.; validation, S.U.R., A.A. and A.R.G.; formal analysis, S.U.R., A.A., M.V. and G.G.; investigation, S.U.R. and A.A.; resources, S.U.R. and A.A.; data curation, S.U.R. and A.A.; writing—original draft preparation, S.U.R., A.A. and G.G.; writing—review and editing, S.U.R., A.A., M.V. and G.G.; visualization, S.U.R., A.A., M.V. and G.G.; supervision, A.A. and M.V. All authors have read and agreed to the published version of the manuscript.
Data available on request.
The authors declare no conflicts of interest.
Footnotes
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Enlarge this image.Figure 1. Photo of the rainfall simulator with featured elements—front view (a), scheme of a rigid vegetation model design (b), layout of the model topography—top view (c).
Enlarge this image.Figure 2. Photos of different model setups in the rainfall simulator: model without vegetation (a), model with rigid vegetation (b), model with flexible vegetation (c), and model with mixed vegetation (d).
Enlarge this image.Figure 4. Outflow hydrographs for channel slope of 0° for: no vegetation condition (a), rigid vegetation condition (b), flexible vegetation condition (c), and mixed vegetation condition (d).
Enlarge this image.Figure 5. Outflow hydrographs for channel slope of 1° for: no vegetation condition (a), rigid vegetation condition (b), flexible vegetation condition (c), and mixed vegetation condition (d).
Enlarge this image.Figure 6. Outflow hydrographs for channel slope of 2° for: no vegetation condition (a), rigid vegetation condition (b), flexible vegetation condition (c), and mixed vegetation condition (d).
Enlarge this image.Figure 7. Change in relative peak discharge in response to variation of rainfall intensities for RV, FV, and MV: channel slope 0° (a), channel slope 1° (b), and channel slope 2° (c).
Enlarge this image.Figure 8. Change in time-to-peak in response to variation of rainfall intensities for RV, FV, and MV: channel slope 0° (a), channel slope 1° (b), and channel slope 2° (c).
Enlarge this image.Figure 9. Change in relative time-to-peak in response to variations in rainfall intensities for RV, FV, and MV: channel slope 0° (a), channel slope 1° (b), and channel slope 2° (c).
The experimental matrix of conditions for the rainfall simulations.
| Simulation No. | Vegetation Cover | Rainfall Intensity [cm/min] | Drainage Channel Slope | Total Number of Simulations | ||
|---|---|---|---|---|---|---|
| P1 | P2 | P3 | ||||
| 1 | NV | 0.3 | 0.4 | 0.5 | 0° | 3 |
| 2 | 0.3 | 0.4 | 0.5 | 1° | 3 | |
| 3 | 0.3 | 0.4 | 0.5 | 2° | 3 | |
| 4 | RV | 0.3 | 0.4 | 0.5 | 0° | 3 |
| 5 | 0.3 | 0.4 | 0.5 | 1° | 3 | |
| 6 | 0.3 | 0.4 | 0.5 | 2° | 3 | |
| 7 | FV | 0.3 | 0.4 | 0.5 | 0° | 3 |
| 8 | 0.3 | 0.4 | 0.5 | 1° | 3 | |
| 9 | 0.3 | 0.4 | 0.5 | 2° | 3 | |
| 10 | MV | 0.3 | 0.4 | 0.5 | 0° | 3 |
| 11 | 0.3 | 0.4 | 0.5 | 1° | 3 | |
| 12 | 0.3 | 0.4 | 0.5 | 2° | 3 | |
Peak discharge observed at the system outlet for all experiments.
| Channel Slope | Rainfall Intensity |
Qp(NV) |
Qp(RV) |
Qp(FV) |
Qp(MV) |
|---|---|---|---|---|---|
| 0° | 0.3 | 62 | 53 | 45 | 41 |
| 0.4 | 71 | 59 | 50 | 44 | |
| 0.5 | 80 | 65 | 54 | 49 | |
| 1° | 0.3 | 71 | 59 | 50 | 43 |
| 0.4 | 77 | 71 | 67 | 56 | |
| 0.5 | 83 | 77 | 73 | 61 | |
| 2° | 0.3 | 71 | 63 | 47 | 44 |
| 0.4 | 83 | 77 | 67 | 56 | |
| 0.5 | 91 | 83 | 77 | 67 |
Reduction in peak discharge of the hydrograph at the system outlet, expressed as percentage relative peak discharge.
| Channel Slope | Rainfall Intensity |
Qp,red(RV) |
Qp,red(FV) |
Qp,red(MV) |
|---|---|---|---|---|
| 0° | 0.3 | 15 | 27 | 34 |
| 0.4 | 18 | 30 | 38 | |
| 0.5 | 19 | 33 | 39 | |
| 1° | 0.3 | 18 | 30 | 39 |
| 0.4 | 8 | 13 | 27 | |
| 0.5 | 8 | 12 | 27 | |
| 2° | 0.3 | 12 | 34 | 38 |
| 0.4 | 8 | 20 | 33 | |
| 0.5 | 8 | 15 | 27 |
Time-to-peak for different land cover conditions.
| Channel Slope | Rainfall Intensity |
Tp(NV) |
Tp(RV) |
Tp(FV) |
Tp(MV) |
|---|---|---|---|---|---|
| 0° | 0.3 | 337 | 411 | 452 | 501 |
| 0.4 | 277 | 330 | 416 | 452 | |
| 0.5 | 249 | 283 | 396 | 450 | |
| 1° | 0.3 | 304 | 341 | 367 | 412 |
| 0.4 | 266 | 286 | 312 | 367 | |
| 0.5 | 245 | 277 | 291 | 363 | |
| 2° | 0.3 | 300 | 310 | 375 | 376 |
| 0.4 | 273 | 280 | 320 | 330 | |
| 0.5 | 245 | 247 | 297 | 303 |
Relative increase in time-to-peak for different land cover conditions in comparison to no vegetation condition.
| Channel Slope | Rainfall Intensity |
Tp,red(RV) |
Tp,red(FV) |
Tp,red(MV) |
|---|---|---|---|---|
| 0° | 0.3 | 22 | 34 | 49 |
| 0.4 | 19 | 50 | 63 | |
| 0.5 | 14 | 59 | 81 | |
| 1° | 0.3 | 18 | 21 | 36 |
| 0.4 | 8 | 17 | 38 | |
| 0.5 | 13 | 19 | 48 | |
| 2° | 0.3 | 3 | 25 | 25 |
| 0.4 | 3 | 17 | 21 | |
| 0.5 | 1 | 21 | 24 |
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; Valyrakis, Manousos 3
; Abdul Razzaq Ghumman 4
; Ghufran Ahmed Pasha 1
; Rashid Farooq 5
1 Department of Civil Engineering, University of Engineering and Technology, Taxila 47050, Pakistan;
2 Department of Hydroscience and Engineering, Faculty of Civil Engineering, University of Zagreb, Fra Andrije Kacica Miosica 26, 10000 Zagreb, Croatia
3 Department of Civil Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;
4 Department of Civil Engineering, College of Engineering, Qassim University, Buraydah 51452, Saudi Arabia;
5 Department of Civil Engineering, International Islamic University, Islamabad 44000, Pakistan;