ANALYSIS OF THE WIDTH OF PROTECTION ZONE NEAR A WATER SUPPLY NETWORK

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VOLUME 12 , ISSUE 1 (May 2019) > List of articles

ANALYSIS OF THE WIDTH OF PROTECTION ZONE NEAR A WATER SUPPLY NETWORK

Małgorzata IWANEK * / Paweł SUCHORAB / Łukasz SIDOROWICZ

Keywords : Failure, Water network, Protection zone, Tolerance intervals

Citation Information : Architecture, Civil Engineering, Environment. Volume 12, Issue 1, Pages 123-128, DOI: https://doi.org/10.21307/ACEE-2019-011

License : (CC-BY-NC-ND 4.0)

Received Date : 02-February-2019 / Accepted: 06-February-2019 / Published Online: 20-May-2019

ARTICLE

ABSTRACT

A protection zone near the water supply network belongs to the proposals of limiting negative results of potential breakages of buried water pipes. Water leaking from a damaged pipe can create swallow holes or hollows, dangerous especially in the urban areas. The proposed zone is an area on the soil surface along a buried water network, where the outflow of water could be expected after a potential failure of the pipe. The infrastructure in this zone should be carefully planned to limit the social, economic and environmental costs in the case of leakage.

The investigations included laboratory tests of a buried water pipe breakage for different cases of leak areas and values of hydraulic pressure head in a pipe as well as analysis of the obtained results and determination of a protection zone for the investigated cases on the basis of tolerance limits. The calculated values of the zone width (5 m if operating pressure is lower than 0.4 MPa, and 7 m otherwise) occurred high, mainly because of the high dispersion of the laboratory tests results. Moreover, we recommended the values of tolerance level to be assumed in calculations.

Graphical ABSTRACT

1. INTRODUCTION

A protection zone along water distribution network is one of the proposals to limit the negative effects of potential breakages of buried water pipes, connected with the creation of swallow holes, hollows or depressions [1, 2]. Water flowing out from a leaking pipe can wash out fine soil particles from a solid matrix and move them through soil pores causing suffosion. The phenomena of this kind are dangerous especially in urban areas with developed infrastructure. Empty spaces created incrementally under the soil surface can be hazardous for human health or property – accidents of cars falling in holes created after a water pipe failure are reported several times a year all over the world. The problem still exists because of two reasons.

The first is that water supply pipes are usually located along roads, which are used by vehicles at all times, and also where the density of infrastructure is often the highest. The second reason is connected with the fact that water pipes failures and breakages occur randomly during the whole maintenance period of every water supply system in the world [3, 4]. Plenty of methods for leakage detection, evaluating the technical conditions of operating water pipes and risk management in water distribution systems have been reported in the literature in recent years [e.g. 5–11]. Modelling and computer simulations have become common tools for the analysis of pipe failures and leakages [1217]. Moreover, mathematical approaches, such as fuzzy sets, artificial neural networks or k-nearest neighbours algorithm have been developed lately for predicting water pipes failures or evaluating leakage potential [1822]. These activities are very important in the aspect of water distribution quality and reliability, but they are not the only way to limit problems connected with pipes failures. Even the best high-tech systems do not protect against randomly-occurring failures. Thus, we propose to support the existing methods and activities by establishing a protection zone on the soil surface over a buried water network, where the outflow of water is possible after a potential failure of the pipe. Infrastructure and settlement in this zone should be carefully planned in order to exclude the possibility of diminishing stability of objects as well as to limit the social, economic and environmental costs in the case of leakage from a water pipe.

The presented investigations include the laboratory tests of a buried water pipe breakage for different cases of leak areas and values of hydraulic pressure head in the pipe and analysis of a distance between the place of water outflow on the soil surface and the location of the water failure, in the aspect of protection zone determination.

2. MATERIALS AND METHODS

The first part of investigations involved physical simulations of a water pipe breakage, conducted in a laboratory on the setup reflecting the actual conditions scaled 1:10. The main part of the setup was an intentionally damaged water pipe buried in sand, supplied with water from a container located on the assumed height. The tests were conducted for four different values of leak areas (4.71, 9.42, 15.07 and 18.84 cm2) and hydraulic pressure head in a pipe varied in the range from 3.0 to 6.0 m H2O, each 0.5 m. Detailed descriptions of the laboratory setup and realisation of the tests are given in the papers [2, 23, 24]. The statistical analysis, including the normality evaluation of measurements results obtained during physical simulations of a water pipe breakage in a laboratory, is presented in the article [25].

The subject of investigations in the range of the presented paper is a horizontal distance (y) between a leaking pipe and a location of water outflow on the soil surface (called “suffosion hole” in this paper) in the aspect of a protection zone determination. The analysis was based on tolerance intervals calculations with two assumptions. At first, it was assumed that the occurrence of water on the soil surface right above a leak is always possible and this assumption imposed the value of the lower tolerance limit as equal to 0 for all calculation cases. The second assumption – that all suffosion holes occur on one side of the leaking pipe, enabled to treat the upper tolerance limit as a half of protection zone width (Fig. 1).

Figure 1.

Scheme of protection zone along a water pipe

10.21307_ACEE-2019-011-f001.jpg

Upper limit (UTL) calculations were conducted for a tolerance level of 70%, 80% and 90%, and a confidence level of 95%, in Statistica 13 software (StatSoft, Inc.). Depending on the kind of data distribution (normal or non-normal – Tab. 1), a mean or a median was taken as a nominal specification, as well as the way of tolerance intervals determination was selected. The results of calculations for different cases corresponding to the test variants were analysed considering the influence of operating pressure in a pipe on a protection zone width and selection of an optimal value of tolerance level in calculations. The results of the analysis enabled to propose the width of a protection zone for the conditions of the investigations.

Table 1.

Kind of data y distribution depending on a leak area and hydraulic pressure head H in a pipe [25]

10.21307_ACEE-2019-011-tbl1.jpg

3. RESULTS AND DISCUSSION

The results of the summarizing data set obtained during laboratory investigations are given in Table 2. The values of standard deviation indicate a high dispersion of data y, which is caused by the complexity of the problem of water leakage from a damaged pipe to the soil, as well as the wide variety of the parameters influencing the phenomenon. In the cases of leak area of 4.71 and 15.07 cm2, an increasing tendency of data y is observed. Any dependence between a distance y and hydraulic pressure head H in a pipe is not visible for the remaining two cases.

Table 2.

Median, mean and standard deviation of data y selected according to leak area and hydraulic pressure head H in a pipe

10.21307_ACEE-2019-011-tbl2.jpg

The main part of investigations was to determine the upper tolerance limits UTL for data y selected according to pressure head in a pipe and leak area. The mean of all obtained values for tolerance level of 70% (UTL70), without any selection, equals 29.68 cm, i.e. the zone of the width of 2 · 29.68 cm = 59.36 cm under laboratory conditions (5.94 m under the actual conditions) will cover 70% of the water outflow points with a 95% confidence level. The mean of all obtained tolerance limits for 80% and 90% tolerance level (UTL80 and UTL90) equals 32.85 cm and 37.56 cm, respectively, and interpretation of these values is analogical to the UTL70.

The mean of data obtained for different H, as well as extreme values and a difference between them (a range) are given in Tab. 3. The lowest value – 11.85 cm – was obtained for the leak area of 15.07 cm2 and tolerance level of 70%, whereas the highest (75.92 cm) for the leak area of 9.42 cm2 and tolerance level of 90%. The values of range increase as a tolerance level rises and are higher than the values of mean for all but one case (leak area of 4.71 cm2). Minimal values are less diversified than the maximal ones for different leak areas, for all values of the tolerance level.

Table 3.

Mean and extremal values of calculated upper tolerance limits

10.21307_ACEE-2019-011-tbl3.jpg

More detailed information about an upper tolerance limit for data y selected according to pressure head in a pipe and leak area is shown in Figures 25. Similarly to the average values (a median and a mean) of data y, values of upper tolerance limit increase as the pressure head in a pipe rises for the cases of leak area of 4.71 and 15.07 cm2. For the other two cases, the correlation between the upper tolerance limit for data y and the pressure head in a pipe is not clear.

Figure 2.

Values of the upper tolerance limit for data y obtained for leak area of 4.71 cm2

10.21307_ACEE-2019-011-f002.jpg
Figure 3.

Values of the upper tolerance limit for data y obtained for leak area of 9.42 cm2

10.21307_ACEE-2019-011-f003.jpg
Figure 4.

Values of the upper tolerance limit for data y obtained for leak area of 15.07 cm2

10.21307_ACEE-2019-011-f004.jpg
Figure 5.

Values of the upper tolerance limit for data y obtained forleak area of 18.84 cm2

10.21307_ACEE-2019-011-f005.jpg

Obviously, higher tolerance level results in higher UTL. The analysis of Figures 25 indicates that the absolute value of the increases UTL (the difference between the values of the upper tolerance limit for 90% and 70% tolerance level – ΔUTL90-70 and the difference between the values of the upper tolerance limit for 80% and 70% tolerance level – ΔUTL80-70) rises for higher values of UTL. It is visible as linear trends in Fig. 6. The coefficient of determination (R2) is the same for both lines because the proportion of the explained variance is also the same. Assuming that the most appropriable ΔUTL does not exceed 5 cm (additional 0.5 m of the zone width on each side of a water pipe under the actual conditions), the tolerance level of 90% can be taken in calculations only for the cases with UTL80 < 22.5 cm (29% of the cases in our investigation), whereas 80% tolerance level should be taken for 22.5 cm < UTL80 < 49.2 cm (57% of the cases). For UTL80 > 49.2 cm (14% of the cases), 70% tolerance level is recommended.

Figure 6.

Dependence between the increases ΔUTL and upper tolerance limit for 80% tolerance level UTL80

10.21307_ACEE-2019-011-f006.jpg

A leak area is impossible to foresee under the actual conditions of water network maintenance before breakage occurs, whereas a range of operating pressure is always known. Thus, it seems to be sensible to examine the dependence between UTL and pressure in a pipe without selection on leak area (Fig. 7). The chart given in Fig. 7 does not indicate a clear correlation between UTL and H, however, the values for H = 3.0 m H2O and 3.5 m H2O are notably lower than for the H = 4.0–6.0 m H2O. Taking into account the values of UTL according to Fig. 7 and the foregoing discussion about choosing a value of tolerance level in UTL calculation, we have proposed the width of protection zones according to Table 4. For all cases of pressure head, 80% tolerance level was taken in calculations, because of 22.5 cm < UTL80 < 49.2 cm. The proposed values are results of rounding the calculated values to integer numbers. The zone occurred wide, mainly because of the high dispersion of laboratory data, often characterizing phenomena influenced by diverse parameters. Such a wide zone is rather impractical for application, but as an initial proposition, it provides information on the possible range of water outflow on the soil surface.

Figure 7.

Values of the upper tolerance limit for data y obtained for all leak areas

10.21307_ACEE-2019-011-f007.jpg
Table 4.

The width of protection zone according to operating pressure in a water pipe

10.21307_ACEE-2019-011-tbl4.jpg

4. CONCLUSIONS

The protection zone width should be such adjusted to ensure the adequate security of infrastructure on the one hand, and on the other hand, not to hinder a land development. An excessively wide zone can create problems connected with the location of a water pipe or other infrastructure. The increase of UTL means a wider zone along a water pipe. Two main factors influence the value of UTL: dispersion of data y and statistical assumptions. The values of standard deviation of data y obtained during laboratory investigations occurred high for all cases in question, indicating a high dispersion of data y, and in consequence causing a high UTL range. The reason for the high dispersion is the complexity of the problem of water leak from a damaged pipe to the soil, connected with the fact that many various factors influence the phenomenon. The second of the above-mentioned factors – statistical assumptions – depends on the needed accuracy of calculations. Assumed higher tolerance and confidence levels results in higher UTL value. On the other hand, to practical purposes, the UTL value should be as low as possible. Thus, after the analysis, we recommend calculations for 90% tolerance level if UTL80 < 22.5 cm, 80% if 22.5 cm < UTL80 < 49.2 cm and 70% if UTL80 > 49.2 cm.

The proposed width of a protection zone along a water pipe is an approximation and pertains to specific laboratory conditions, so it cannot be treated as a general guideline for water network designing. However, the obtained results encourage the continuation of this research direction, drawing particular attention to the parameters influencing the phenomenon of water leaking from a damaged pipe to the soil medium.

References


  1. Kowalski, D., & Jaromin, K. (2010). Metoda wyznaczania zasięgu strefy ochrony wodociągowych przewodów tranzytowych (Designing method of protection zones range of water transit pipes). Proceedings of ECOpole, 4(2), 419–424 (in Polish, with English abstract).
  2. Iwanek, M., Suchorab, P., & Karpińska-Kiełbasa, M. (2017). Suffosion holes as the results of a breakage of a buried water pipe. Periodica Polytechnica Civil Engineering, 61(4), 700–705.
  3. Hotloś, H. (2009). Analiza uszkodzeń i kosztów naprawy przewodów wodociągowych w okresie zimowym (Analysis of failure events and damage repair costs for water-pipe networks in the winter season). Ochrona Środowiska, 31(2), 41–48 (in Polish, with English abstract).
  4. Romano M., Kapelan Z., & Savić D.A. (2013). Geostatistical techniques for approximate location of pipe burst events in water distribution systems. Journal of Hydroinformatics, 15(3), 634–651.
    [CROSSREF]
  5. Zimoch, I. (2012). Regulacja ciśnienia jako element zarządzania ryzykiem eksploatacji sieci wodociągowej (Pressure Control as Part of Risk Management for a Water-pipe Network in Service). Ochrona Środowiska, 34(4), 57–62 (in Polish, with English abstract).
  6. Liu, Z., & Kleiner, Y. (2014). Computational intelligence for urban infrastructure condition assessment: Water transmission and distribution systems. IEEE Sensors Journal, 14(12), 4122–4133.
    [CROSSREF]
  7. Pérez, R., Cugueró, M.A., Cugueró, J., & Sanz, G. (2014). Accuracy assessment of leak localisation method depending on available measurements. Procedia Engineering, 70, 1304–1313.
    [CROSSREF]
  8. Gaska, K., Generowicz, A., Zimoch, I., Ciula, J. & Iwanicka, Z. (2017). A high-performance computing (HPC) based integrated multithreaded model predictive control (MPC) for water supply networks. Architecture Civil Engineering Environment, 10(4), 141–151.
    [CROSSREF]
  9. Zimoch, I. & Szymura, E. (2012). Operator reliability in risk assessment of industrial systems function. Przemysl Chemiczny, 93(1), 111–116.
  10. Cugueró-Escofet, M.À., Puig, V., & Quevedo, J. (2017). Optimal pressure sensor placement and assessment for leak location using a relaxed isolation index: Application to the Barcelona water network. Control Engineering Practice, 63, 1–12.
    [CROSSREF]
  11. Zimoch, I. & Lobos, E. (2012). Comprehensive interpretation of safety of wide water supply systems. Environment Protection Engineering, 38(3), 107–117.
  12. Cobacho, R., Arregui, F., Soriano, J., & Cabrera, E. (2015). Including leakage in network models: an application to calibrate leak valves in EPANET. Journal of Water Supply: Research and Technology-Aqua, 64(2), 130–138.
    [CROSSREF]
  13. Kowalski, D., Kowalska, B. & Kwietniewski, M. (2015). Monitoring of water distribution system effectiveness using fractal geometry. Bulletin of The Polish Academy of Sciences – Technical Sciences, 63(1), 155–161.
    [CROSSREF]
  14. Iwanek, M., Kowalski, D., & Kwietniewski, M. (2015). Badania modelowe wypływu wody z podziemnego rurociągu podczas awarii (Model studies of a water outflow from an underground pipeline upon its failure). Ochrona Środowiska, 37(4), 13–17 (in Polish, with English abstract).
  15. Okeya, I., Hutton, C., & Kapelan, Z. (2015). Locating pipe bursts in a district metered area via online hydraulic modelling. Procedia Engineering, 119, 101–110.
    [CROSSREF]
  16. Suchorab, P., Kowalska, B., & Kowalski, D. (2016). Numerical investigations of water outflow after the water pipe breakage. Rocznik Ochrona Środowiska, 18(2), 416–427.
  17. Wilson D., Filion Y., & Moore I. (2017). State-of-the-art review of water pipe failure prediction models and applicability to large-diameter mains. Urban Water Journal, 14(2), 173–184.
    [CROSSREF]
  18. Islam, M. S., Sadiq, R., Rodriguez, M. J., Francisque, A., Najjaran, H., Naser, B., & Hoorfar, M. (2012). Evaluating leakage potential in water distribution systems: a fuzzy-based methodology. Journal of Water Supply: Research and Technology – AQUA, 61(4), 240–252.
    [CROSSREF]
  19. Kutyłowska, M. (2015). Neural network approach for failure rate prediction. Engineering Failure Analysis, 47, 41–48.
    [CROSSREF]
  20. Kamiński, K., Kamiński, W., & Mizerski, T. (2017). Application of artificial neural networks to the technical condition assessment of water supply systems. Ecological Chemistry and Engineering S, 24(1), 31–40.
    [CROSSREF]
  21. Kutyłowska, M. (2017a). Comparison of two types of artificial neural networks for predicting failure frequency of water conduits. Periodica Polytechnica Civil Engineering, 61(1), 1–6.
    [CROSSREF]
  22. Kutyłowska, M. (2017b). K-Nearest Neighbours Method as a Tool for Failure Rate Prediction. Periodica Polytechnica Civil Engineering, https://doi.org/10.3311/PPci.10045
  23. Iwanek, M., Kowalski, D., Kowalska, B., Hawryluk, E., Kondraciuk, K. (2014). Experimental investigations of zones of leakage from damaged water network pipes. In C.A. Brebbia, S. Mambretti (Eds.), Urban Water II. WIT Transactions on the Built Environment, 139, 257–268, Southampton, Boston, UK: WIT Press 2014, http://dx.doi.org/10.2495/uw140221.
  24. Iwanek, M., Kowalska, B., Hawryluk, E., & Kondraciuk., K. (2016a). Distance and time of water effluence on soil surface after failure of buried water pipe. Laboratory investigations and statistical analysis. Eksploatacja i Niezawodnosc – Maintenance and Reliability, 18(2), 278–284.
    [CROSSREF]
  25. Iwanek M., Suchorab P., Budzioch M. (2016). Statystyka opisowa wyników fizycznej symulacji awarii podziemnego przewodu wodociągowego (Descriptive statistics results of physical simulation water pipe failure). In Kuś K., Piechurski F. (Eds.), Nowe technologie w sieciach i instalacjach wodociągowych i kanalizacyjnych, Gliwice: Instytut Inżynierii Wody i Ścieków. Politechnika Śląska, 37–50.
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FIGURES & TABLES

Figure 1.

Scheme of protection zone along a water pipe

Full Size   |   Slide (.pptx)

Figure 2.

Values of the upper tolerance limit for data y obtained for leak area of 4.71 cm2

Full Size   |   Slide (.pptx)

Figure 3.

Values of the upper tolerance limit for data y obtained for leak area of 9.42 cm2

Full Size   |   Slide (.pptx)

Figure 4.

Values of the upper tolerance limit for data y obtained for leak area of 15.07 cm2

Full Size   |   Slide (.pptx)

Figure 5.

Values of the upper tolerance limit for data y obtained forleak area of 18.84 cm2

Full Size   |   Slide (.pptx)

Figure 6.

Dependence between the increases ΔUTL and upper tolerance limit for 80% tolerance level UTL80

Full Size   |   Slide (.pptx)

Figure 7.

Values of the upper tolerance limit for data y obtained for all leak areas

Full Size   |   Slide (.pptx)

REFERENCES

  1. Kowalski, D., & Jaromin, K. (2010). Metoda wyznaczania zasięgu strefy ochrony wodociągowych przewodów tranzytowych (Designing method of protection zones range of water transit pipes). Proceedings of ECOpole, 4(2), 419–424 (in Polish, with English abstract).
  2. Iwanek, M., Suchorab, P., & Karpińska-Kiełbasa, M. (2017). Suffosion holes as the results of a breakage of a buried water pipe. Periodica Polytechnica Civil Engineering, 61(4), 700–705.
  3. Hotloś, H. (2009). Analiza uszkodzeń i kosztów naprawy przewodów wodociągowych w okresie zimowym (Analysis of failure events and damage repair costs for water-pipe networks in the winter season). Ochrona Środowiska, 31(2), 41–48 (in Polish, with English abstract).
  4. Romano M., Kapelan Z., & Savić D.A. (2013). Geostatistical techniques for approximate location of pipe burst events in water distribution systems. Journal of Hydroinformatics, 15(3), 634–651.
    [CROSSREF]
  5. Zimoch, I. (2012). Regulacja ciśnienia jako element zarządzania ryzykiem eksploatacji sieci wodociągowej (Pressure Control as Part of Risk Management for a Water-pipe Network in Service). Ochrona Środowiska, 34(4), 57–62 (in Polish, with English abstract).
  6. Liu, Z., & Kleiner, Y. (2014). Computational intelligence for urban infrastructure condition assessment: Water transmission and distribution systems. IEEE Sensors Journal, 14(12), 4122–4133.
    [CROSSREF]
  7. Pérez, R., Cugueró, M.A., Cugueró, J., & Sanz, G. (2014). Accuracy assessment of leak localisation method depending on available measurements. Procedia Engineering, 70, 1304–1313.
    [CROSSREF]
  8. Gaska, K., Generowicz, A., Zimoch, I., Ciula, J. & Iwanicka, Z. (2017). A high-performance computing (HPC) based integrated multithreaded model predictive control (MPC) for water supply networks. Architecture Civil Engineering Environment, 10(4), 141–151.
    [CROSSREF]
  9. Zimoch, I. & Szymura, E. (2012). Operator reliability in risk assessment of industrial systems function. Przemysl Chemiczny, 93(1), 111–116.
  10. Cugueró-Escofet, M.À., Puig, V., & Quevedo, J. (2017). Optimal pressure sensor placement and assessment for leak location using a relaxed isolation index: Application to the Barcelona water network. Control Engineering Practice, 63, 1–12.
    [CROSSREF]
  11. Zimoch, I. & Lobos, E. (2012). Comprehensive interpretation of safety of wide water supply systems. Environment Protection Engineering, 38(3), 107–117.
  12. Cobacho, R., Arregui, F., Soriano, J., & Cabrera, E. (2015). Including leakage in network models: an application to calibrate leak valves in EPANET. Journal of Water Supply: Research and Technology-Aqua, 64(2), 130–138.
    [CROSSREF]
  13. Kowalski, D., Kowalska, B. & Kwietniewski, M. (2015). Monitoring of water distribution system effectiveness using fractal geometry. Bulletin of The Polish Academy of Sciences – Technical Sciences, 63(1), 155–161.
    [CROSSREF]
  14. Iwanek, M., Kowalski, D., & Kwietniewski, M. (2015). Badania modelowe wypływu wody z podziemnego rurociągu podczas awarii (Model studies of a water outflow from an underground pipeline upon its failure). Ochrona Środowiska, 37(4), 13–17 (in Polish, with English abstract).
  15. Okeya, I., Hutton, C., & Kapelan, Z. (2015). Locating pipe bursts in a district metered area via online hydraulic modelling. Procedia Engineering, 119, 101–110.
    [CROSSREF]
  16. Suchorab, P., Kowalska, B., & Kowalski, D. (2016). Numerical investigations of water outflow after the water pipe breakage. Rocznik Ochrona Środowiska, 18(2), 416–427.
  17. Wilson D., Filion Y., & Moore I. (2017). State-of-the-art review of water pipe failure prediction models and applicability to large-diameter mains. Urban Water Journal, 14(2), 173–184.
    [CROSSREF]
  18. Islam, M. S., Sadiq, R., Rodriguez, M. J., Francisque, A., Najjaran, H., Naser, B., & Hoorfar, M. (2012). Evaluating leakage potential in water distribution systems: a fuzzy-based methodology. Journal of Water Supply: Research and Technology – AQUA, 61(4), 240–252.
    [CROSSREF]
  19. Kutyłowska, M. (2015). Neural network approach for failure rate prediction. Engineering Failure Analysis, 47, 41–48.
    [CROSSREF]
  20. Kamiński, K., Kamiński, W., & Mizerski, T. (2017). Application of artificial neural networks to the technical condition assessment of water supply systems. Ecological Chemistry and Engineering S, 24(1), 31–40.
    [CROSSREF]
  21. Kutyłowska, M. (2017a). Comparison of two types of artificial neural networks for predicting failure frequency of water conduits. Periodica Polytechnica Civil Engineering, 61(1), 1–6.
    [CROSSREF]
  22. Kutyłowska, M. (2017b). K-Nearest Neighbours Method as a Tool for Failure Rate Prediction. Periodica Polytechnica Civil Engineering, https://doi.org/10.3311/PPci.10045
  23. Iwanek, M., Kowalski, D., Kowalska, B., Hawryluk, E., Kondraciuk, K. (2014). Experimental investigations of zones of leakage from damaged water network pipes. In C.A. Brebbia, S. Mambretti (Eds.), Urban Water II. WIT Transactions on the Built Environment, 139, 257–268, Southampton, Boston, UK: WIT Press 2014, http://dx.doi.org/10.2495/uw140221.
  24. Iwanek, M., Kowalska, B., Hawryluk, E., & Kondraciuk., K. (2016a). Distance and time of water effluence on soil surface after failure of buried water pipe. Laboratory investigations and statistical analysis. Eksploatacja i Niezawodnosc – Maintenance and Reliability, 18(2), 278–284.
    [CROSSREF]
  25. Iwanek M., Suchorab P., Budzioch M. (2016). Statystyka opisowa wyników fizycznej symulacji awarii podziemnego przewodu wodociągowego (Descriptive statistics results of physical simulation water pipe failure). In Kuś K., Piechurski F. (Eds.), Nowe technologie w sieciach i instalacjach wodociągowych i kanalizacyjnych, Gliwice: Instytut Inżynierii Wody i Ścieków. Politechnika Śląska, 37–50.

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