Comparing Actual and Designed Water Demand in Australian Multilevel Residential Buildings | Journal of Water Resources Planning and Management | Vol 149, No 1

IntroductionIn plumbing system design, the estimated peak flow rate has a significant influence in determining the size of plumbing systems (e.g., meters, pipes, pumps, and control valves). Typically, peak flow rates are estimated through the application of plumbing standards. However, several recent studies have demonstrated that methodology stipulated within international plumbing codes can significantly overestimate the actual peak hydraulic demand in residential apartments and mixed-use buildings (Bleys et al. 2012; Douglas et al. 2019; Jack et al. 2017; Mayer et al. 2019; Tindall and Pendle 2015). Overestimation of peak demands leads to oversized plumbing systems that drive up construction costs, elevate energy consumption, degrade water quality, and can increase the risk of premature failure of plumbing hardware and fixtures (Douglas et al. 2019).Building regulators in many countries now have the awareness that their current plumbing standards may significantly overestimate the design peak demand in multilevel residential buildings, and some have been working on updating the standards. The International Association of Plumbing and Mechanical Officials (IAPMO) has developed a new water demand calculator (WDC) (IAPMO 2020), which has been included in the Uniform Plumbing Code [UPC:2018 (IAPMO 2018)]. The WDC was developed based on residential end-use studies conducted at over 1,000 homes in the United States (Buchberger et al. 2017; Mayer et al. 2019). In Australia, the Australian Building Codes Board (ABCB) now allows the use of performance solutions instead of the traditional approach that has been endorsed by the Australian and New Zealand plumbing standard AS/NZS 3500.1:2021 (Standards Australia 2021) since the 1970s. The Hydraulic Consultants Association of Australasia (HCAA) has initiated a project to monitor water consumption of four multilevel residential buildings with water-conserving fixtures (HCAA 2019). However, there is a lack of knowledge and consensus in how to quantify the peak demand overestimation problem and how to modernize the peak demand design practice with consideration of variations in water use behavior across countries or jurisdictions.This case study demonstrates how the current Australian and New Zealand cold water plumbing standard AS/NZS 3500.1:2021 is quantitatively assessed in peak demand design for multilevel residential buildings. The literature review covers international development of residential water demand estimation models, the development history of Australian and New Zealand plumbing standards, and contributors and implications of peak demand overestimation. The quantitative assessment includes two parts: (1) comparing the current standard AS/NZS 3500.1:2021 against comparable international plumbing codes and some newly proposed approaches using a fixed apartment design to establish a benchmark; and (2) comparing the measured actual flow in selected multilevel residential buildings with the design peak flow estimated from the standard. Four representative multilevel residential buildings with fixture counts ranging between 800 and 3,000 in two Australian capital cities (Sydney and Canberra) are used in the case study. The discussion highlights the need of future research in several topics, including the impact of the sampling interval, the practical definition of the 99th percentile flow, the implications of reduced pipe sizes, and other limitations in current plumbing system design practices. Australian and New Zealand plumbing practices and water use behaviors are similar to many other countries in Europe and North America; hence, this research serves as an informative reference for researchers, industry, and regulatory bodies working on improving their existing plumbing design standards.Literature ReviewInternational Development of Residential Water Demand Estimation ModelsMost plumbing standards used to predict peak flow rates for plumbing systems are built on the research conducted by Hunter (1940). Hunter employed a probabilistic approach, recognizing the use for plumbing fixtures followed a binomial distribution, where a fixture is either in use (busy) or off (idle) (Douglas et al. 2019). Through monitoring conducted for two hotel buildings during congested usage periods, the duration of use (t) and time between successive uses (T) was observed to determine a specific fixture’s probability of use (p). Striking a balance between performance and cost, Hunter (1940) developed a design curve that allowed a 1% chance of system overload during the peak demand period. Overload does not imply system failure, but rather a condition where the actual peak flows would exceed expected design values. The corresponding probability of fixture usage combined with the known fixture flow rate (q) and specific fixture count (n) was used to determine the 99th percentile design flow that appears on Hunter’s famous design chart as shown in Fig. 1.Recognizing that probability of usage and subsequent flow rate varied between fixture groups, Hunter (1940) developed a weighting system, known as fixture or loading units within comparable international plumbing codes and standards nowadays. The weight for a particular fixture group is inversely related to the total number of fixtures in that group needed to impose a specified demand under design conditions relative to other fixture groups. The resultant design curves were based on three fixture groups: flush tank toilet, flush valve toilet, and bathtubs. This approach was adopted on an international scale and modified versions of the curve currently exist in many international plumbing codes (AWWA 2014).Over the past few decades, adoption of water-efficient fixtures has led to reduced water consumption and lower peak demand during congested periods (Hobbs et al. 2019). As an attempt to rectify the known overestimation, international plumbing codes have made modifications to the developed design curves, probability values, or fixture units. However, these reductions are based on engineering experience with limited evidence supporting their suitability (AWWA 2014; Omaghomi et al. 2020).In-depth reviews of water demand models for residential buildings have been conducted in many studies (Hobbs et al. 2019; Jack et al. 2017; Mangalekar and Gumaste 2021; Wong and Mui 2018). Existing techniques for peak demand estimation can be categorized as (1) probabilistic-, (2) empirical-, and (3) stochastic-based models. Most international plumbing codes employ either probabilistic or empirical formulas (Jack et al. 2017). Probabilistic-based models are attributed to the work conducted by Hunter and have undergone refinement to probability and fixture units values to account for water-efficient fixtures (Chance 2015). Empirical models employ nonlinear regression curves to show a relationship between potential maximum building demand (x-axis) and measured maximum building demand (y-axis) (Jack et al. 2017). Stochastic-based models employ a time-series approach utilizing frequency sampling data obtained from user behavioral patterns (Wong and Mui 2018). While Jack et al. (2017) warned of the limitations of the data assessed, they suggested empirical-based models were better suited for pipe sizing compared to probabilistic methods after reviewing observed flow data taken from several buildings and comparing them against selected plumbing codes and standards.Research efforts have focused on obtaining fine-scale water consumption data to develop and improve residential end-use models. The stochastic-based model SIMDEUM developed by Blokker et al. (2010) integrates end-user data obtained from water usage journals where study participants recorded household fixture usage events. User characteristics such as age, gender, and occupation are accompanied by diurnal patterns to simulate residential water usage. Oliveira et al. (2013) presented a model that uses Monte Carlo simulations and integrates fuzzy logic to simulate user behavior and calculate peak hydraulic demand in residential buildings. Bennett et al. (2013) implemented artificial neural networks trained on residential end-use data obtained from Beal and Stewart (2011). The predictive model has helped to clarify the complex nature and identify key determinants that influence residential water usage behavior. Wong and Mui (2018) used Bayes’ theorem to predict peak demand in residential buildings. Ferreira and Goncalves (2020) developed a stochastic water demand model to analyze how building plumbing systems behave over an extended period.While many methods to predict residential water demand at household and end-user scales exist, Mangalekar and Gumaste (2021) suggested that standing models require simplification to enable their use in plumbing codes and standards. Researchers are beginning to bridge this gap. SIMDEUM (Blokker et al. 2010) has been implemented as an empirical plumbing curve within the Dutch standard ISSO-55 to assist practitioners in estimating plumbing system loads and determining pipe sizing (Jack et al. 2017). Work by Buchberger et al. (2017) to estimate peak demand has been incorporated into UPC:2018 and is available as a downloadable Excel spreadsheet (IAPMO 2020). The values for the probability of fixture use in IAPMO’s WDC are gleaned from data obtained from residential end-use studies conducted at over 1,000 homes within 62 cities across nine states within the United States (Buchberger et al. 2017; Mayer et al. 2019). Recently, updated probability functions presented by Omaghomi et al. (2020) have been included in a revised version of the WDC to reflect diminishing p-values with increasing building size.Australian and New Zealand Plumbing Standards for Premises Cold Water SystemsThe British plumbing codes have adopted a loading units approach (similar to Hunter’s fixture unit theory), where the summation of the total number of loading units downstream of a pipe are referenced against a curve to estimate design flow rates (IoP/CIPHE 2002). Australia modified British building codes to suit the Australian climate (Chance 2015). The resulting document, known to industry as the Barrie Book (Smith 1976), introduced a probabilistic methodology to predict peak flow rates in buildings, which now exists as a codified method within the Plumbing Code of Australia (PCA) deemed-to-satisfy (DtS) solution (AS/NZS 3500.1:2021) shown in Eq. (1) (1) where Q (L/s) = peak water demand; and d = number of dwellings. This expression for estimating peak flow is different from other design approaches because it uses the number of dwellings as the defining parameter rather than fixture counts or fixture units. Therefore, Eq. (1) gives the same flow rate for a specific number of apartments, irrespective of the number of fixtures.The Australian methodology to estimate peak flow rates in buildings has not changed in nearly 50 years. During this period there has been a well-documented increased adoption of water-efficient fixtures and a behavioral shift toward reduced water usage after the Australian millennium drought (Arbon et al. 2014; Beal and Stewart 2011; Willis et al. 2011). As a result, like many comparable international plumbing codes, the current methodology in the Australian plumbing standard overestimates the peak water demand in buildings.Recognizing limitations of the current standard, ABCB has proposed amendments to PCA that would allow designers to use any methodology that demonstrates performance-based requirements are met (Zeller and Ashe 2019). An approach for estimating peak water demands outside of PCA is known as the performance solution (PS).To assess PS suitability, ABCB is investigating a verification method (VM) that employs a modified formula, derived from an approach developed by Robert Wistort (1994). Wistort (1994) hypothesized that the number of fixtures simultaneously running water in a building follows a binomial distribution having the mean and variance described by Eqs. (2) and (3), respectively (2) (3) σ2=np(1−p)=nt(T−t)T2where μ = mean number of busy fixtures at any instant during the peak period; σ2 = variance of the busy fixtures; n = total number of fixtures of a given type in the building; p = probability an individual fixture is running water during the peak period; t = average duration of demand; and T = average time between successive operations of the fixture.The peak water demand is estimated as the 99th percentile of the demands assuming a normal distribution as shown in Eq. (4) (4) PSFR=∑k=1KnktkqkTk+z99∑k=1Knktkqk2(Tk−tk)Tk2where K = total number of fixtures groups along a downstream pipe; nk = number of fixtures for a specific fixture group downstream of a pipework section; qk = specific fixture flow rate; tk = average duration of usage; Tk = average time between successive operations of an individual fixture; and z99=99th percentile of the standard normal distribution, approximated as 2.326.The formula in the VM allows practitioners the flexibility to adjust fixture usage probability, through parameters tk and Tk, to values that are suited to their application. Current Australian plumbing standards specify the fixture probability values. However, there are many challenges in confirming and promoting the VM. The Wistort (1995) method is suitable only for buildings with relatively high fixture counts (Buchberger et al. 2017). In addition, probabilities of fixture usage in peak periods are difficult to estimate and may be a function of building size (Omaghomi et al. 2020). The WDC implements the Wistort (1995) approach under certain conditions but does not allow the user to change the fixture probabilities.Contributors and Implications of Peak Demand OverestimationIt is now recognized by the plumbing industry that the approach used in most international plumbing codes to predict peak flow rates leads to significant overestimation (Bleys et al. 2012; Douglas et al. 2019; Jack et al. 2017; Mayer et al. 2019; Tindall and Pendle 2015). A key reason is the widespread adoption of water-efficient fixtures (Hobbs et al. 2019).As a consequence, plumbing system design tends to oversize plumbing pipes, pumps, and control devices because their selection is typically driven by the peak condition as required by most plumbing standards. International Copper Association Australia (2021) recommends a minimal velocity of 0.5  m/s for copper pipes. In pressure pipelines, minimum velocities are required to cleanse entrained air by hydraulic means (Wisner et al. 1975) and low flows may contribute to compromised water quality (Farooqi et al. 2009). Considering that some specific plumbing hardware and fixtures are known to have optimal velocity ranges for effective operations, the overestimation of peak flow and the peak-flow-driven design philosophy implies that some hydraulic components used to control and regulate plumbing systems may rarely operate within the manufacturer-specified hydraulic range. Douglas et al. (2019) indicate an upper-limit design philosophy as a potential contributor to the oversizing of water service lines and water meters that results in poor performance and reduced accuracy. In addition, this design practice may be a significant contributor to water quality issues and premature failures (Farooqi et al. 2009) seen in many plumbing systems.Materials and MethodologyQuantitative assessment of the current Australian and New Zealand plumbing standard (AS/AZS 3500.1:2021) in peak demand estimation for multilevel residential buildings is described here. The assessment includes two main parts: (1) comparing the current standard AS/NZS 3500.1:2021 against comparable international plumbing codes and some new approaches using a fixed apartment design to establish a benchmark; and (2) comparing the measured actual flow in selected multilevel residential buildings with the design peak flow estimated from the standard. The methodology for each part is discussed in detail in the following sections.Comparison with Major International Plumbing Codes and New ApproachesTo establish a benchmark for comparison, the current Australian and New Zealand cold water plumbing standard (AS/AZS 3500.1:2021) and several other international codes were used to estimate the peak flow rate for a fixed apartment design. Displayed in Table 1, the standards and codes selected employ either probabilistic or empirical approaches and were taken from the US, UK, Germany, and Australia. The US and UK plumbing codes and standards were selected for review because of the similarities in methodology and the water consumption habits compared with Australia. The German standards were selected because of their known feature of lower peak demand estimation. The WDC and ABCB VM were selected to serve as a reference of newly proposed methods compared to current plumbing codes and standards.Table 1. Summary of parameters used for plumbing code comparisonTable 1. Summary of parameters used for plumbing code comparisonPlumbing code (year)AbbreviationUnitsaTotalKitchen sinkLaundry troughWashing machineDishwasherShowerBasin (lavatory)Toilet (water closet)Bathroom groupbUnits per apartmentAustralian Standard AS/NZS 3500.1:2021 (2021)AS/NZS3500.1:2021Dwellings————————1.00International Plumbing Code (2017)IPC:2018WSFU1.401.401.401.40———6.2011.80Uniform Plumbing Code (2018)UPC:2018WSFU1.501.504.001.50———7.0015.50National Standards Plumbing Code (2015)NSPC:2015WSFUc1.001.002.501.00———4.5010.00German Standard DIN 1988–300:2012 (2012)DIN 1988-300:2012Flow rate (L/s)0.070.150.150.070.150.070.13—1.14German Standard DIN 1988-3:1988 (1988)DIN 1988-3:1988Flow rate (L/s)0.070.150.250.150.200.070.13—1.42British Standard BS 6700:1997 (1997)BS 6700:1997Loading units3.003.003.003.003.003.002.00—28.00British Standard BS EN 806-3:2006 (2006)BS EN 806-3:2006Loading units2.002.002.002.002.001.001.00—16.00Institute of Plumbing (2002) low usageIoP/CIPHE:2002Demand units1.001.002.002.002.001.001.00—14.00IAPMO water demand calculatorWDC V2 (IAPMO)Flow rate (L/s)0.140.130.220.080.130.090.19—1.39ABCB (Wistort method)dVM (ABCB)Flow rate (L/s)0.100.150.200.050.150.100.12—1.24To enable the comparison among the various approaches, a fixed apartment design is proposed, and the corresponding peak demand design information specified in each approach is summarized in Table 1. The assumed apartment design includes one common area and two bathrooms. Common area fixtures incorporate a kitchen sink, a dishwasher, a laundry trough, and a washing machine. Bathroom areas consist of a shower, a basin (lavatory), and a toilet (flush-tank water closet). Most plumbing codes define a specific value that is aligned to the number of fixtures and the subsequent water demand or load downstream of a specific pipe section. This value is mapped to a curve that returns a corresponding design 99th percentile flow rate. Terminology of this value varies between international plumbing codes (as shown in the Units column in Table 1). Common terms are fixture units, loading units, demand units, or in the case of the German standards, the summation of fixture flow rates termed design flow rate. These values are specific to hot (heated), cold, and total water demand. The typical plumbing layout for multilevel residential buildings consists of a main cold water supply directly connected to cold water fixtures and the building’s hot water storage unit. As a result, the comparative data assume values for total water consumption.Alongside the codified methods, newly proposed methods such as IAPMO’s WDC and ABCB’s VM are included in the comparison. As stated previously, both the WDC and VM employ Wistort’s approach [Eq. (4)]. The WDC limits its use of the Wistort method to larger fixture counts when the Hunter number H(n,p)>5.0. The Hunter number is a dimensionless term computed as the product of the number of fixtures, n, and a specific fixture’s probability of usage, p, and hence represents the average number of fixtures that are simultaneously busy during the peak period of water use (Buchberger et al. 2017). Using the example apartment structure mentioned previously (one common area and two bathrooms), H(n,p)>5.0 at approximately 49 apartments and a total of 490 fixtures. For smaller apartments and fixture counts, the WDC employs other algorithms based on specific thresholds of the developed Hunter number.Comparison with Flow Measured from Australian Multilevel Residential BuildingsA five-step procedure was used to compare predicted peak demands against measured peak flows. Details for each step are discussed in the following sections.Step 1: Building Selection and Information CollectionFour representative modern multilevel residential buildings were selected. Three of the buildings have between 100 and 200 apartments, representing common medium-sized apartment buildings. Another building has more than 300 apartments, representing a large apartment building. The number of total fixtures ranges from less than 1,000 to above 2,000. Summarized in Table 2 is each of the monitored buildings’ information regarding occupancy, apartment quantity, and respective fixture counts. Building profile information was collated through plumbing design documentation and building management records for each site. All four buildings were constructed after 2013 and are fitted with water-efficient appliances, rated between three and six stars compliant to water efficiency labeling and standards in Australia (WELS 2020). The Water Efficiency Labelling and Standards (WELS) scheme is comparable to the USEPA’s WaterSense accreditation (USEPA 2021). A three-star WELS shower head corresponds to a WaterSense label where consumption must be below 7.5  L/m (2.0 gpm).Table 2. Apartment structure and building fixtures for the four buildings under investigationTable 2. Apartment structure and building fixtures for the four buildings under investigationSite location monitoring period estimated occupancyBedroomsBathroomsKitchen sinkDishwasherLaundry tubWashing machineShowerBasinToiletBathUrinalApartmentsTotal fixturesSite 1: Waterloo, Sydney, New South Wales, Australia252239145143143143239239239——143a1,291Monitoring period: August 13, 2019 to March 14, 202020Estimated occupancy: 90% (∼7 months)Site 2: Milsons Point, Sydney, New South Wales, Australia228228123123115123228254230——1231,223Monitoring period: October 17, 2019 to March 14, 2020Estimated occupancy: 90% (∼5 months)Site 3: Manhattan, Canberra, Australian Capital Territory, Austr alia5225243303303303305275365254—3302,912Monitoring period: December 14, 2019 to March 14, 2020Estimated occupancy: 95% (∼3 months)(15% of apartments are short-term stay)Site 4: Braddon, Canberra, Australian Capital Territory, Australia189158124—11711515017817122115859Monitoring period: January 17, 2020 to March 14, 2020Estimated occupancy: unknown (∼2 months)Site 1: Waterloo, Sydney, New South Wales, Australia, 143-Unit Residential Apartment Building (7 months). Site 1 is located in Waterloo (Sydney, New South Wales, Australia). The building consists of 143 residential apartments and four common-use areas split across three towers A, B, and C ranging from 7 to 10 levels. The monitored cold water pipe supplies 143 apartments, two common areas, two hot water storage units placed on top of Towers B and C, and three DN20 pipes to supply each respective tower’s garbage chutes.Site 2: Milsons Point, Sydney, New South Wales, Australia, 123-Unit Residential Apartment Building (5 months). Site 2 is situated in Milsons Point Sydney, New South Wales, Australia. It has 16 stories and 123 residential apartments. The monitored cold water plumbing system consists of two cold water risers that supply all apartments and one hot water storage unit situated on the roof of the building.Site 3: Manhattan, Canberra, Australian Capital Territory, Australia, 330-Unit Residential Apartment Building (3 months). Positioned in Canberra’s central business district (CBD), Site 3 the Manhattan building has 330 apartments with predominantly one or two bedrooms and a small number of three-bedroom apartments. The cold water plumbing network supplies all 330 apartments, a residential gym, provisions for irrigation and pool contractors, and a series of hot water units located on level three. Of the 330 apartments, 15% are occupied under short-term agreements. A review of monthly water consumption data showed that water usage for recreational amenities such as the pool and gym area contributed to less than 3% of total water consumption.Site 4: Braddon, Canberra, Australian Capital Territory, Australia, 115 Apartments and Two Office Levels, Mixed-Use Building. Site 4 is a five-level mixed-use residential building located in the suburb of Braddon in Canberra, Australian Capital Territory, Australia. The ground floor consists of varying business types including bars and restaurants, a laundromat, retail stores, jewelers, and hairdressing businesses. The first floor is designated for office spaces. Through the review of hydraulic design documentation, the ground and first floors consist of kitchen sinks and public bathrooms. There are 115 residential apartments on the upper three floors. The monitored cold water plumbing system supplies all amenities to office levels, residential apartments, and a hot water storage unit place on the building’s roof. Of the 859 fixtures, 39 are in commercial and office spaces, contributing to 4.5% of the total building’s plumbing system.Step 2: Flow Data CollectionData were collected between August 2019 and March 2020, before any COVID-19-related restrictions. At each building, the main residential cold water distribution pipes were fitted with Flexus F501 ultrasonic flowmeters (FLEXIM, Edgewood, New York) capable of recording a flow velocity range of 0.01 to 25  m/s with a repeatability of 0.25% and measurement uncertainty of ±1.5% at a reading of ±0.01  m/s. To enable long-term monitoring, pulse emissions were set to 1 pulse/3  L of volumetric flow. Data were acquired at 5-s intervals (provided there was enough flow to activate the pulses), but only the 1-min average values of the (previous 12) acquisitions were logged and sent to cloud storage via a GSM network. The minimum recording flow rate was set to 0.1  L/s, and all logged values below this threshold default to a value of 0  L/s. Data were downloaded in .csv format and arranged into flow recordings for each day and time in 1-min intervals.Step 3: Peak Flow Adjustment FactorIn general, the magnitude of measured peak flow increases as the sampling interval decreases. The current study logged flow rates at 1-min intervals. Previous studies of water demand in multilevel residential buildings have logged flow rates at intervals between 1 and 10 s (Bleys et al. 2012; Douglas 2019; Mayer et al. 2019; Stråby et al. 2019; Tindall and Pendle 2015).Monte Carlo simulations were performed to investigate the impact of logging interval on the magnitude of measured peak flow. A Poisson rectangular pulse (PRP) model described by Buchberger and Wells (1996) was used to simulate the instantaneous flow through the service line during the peak hour for 30 days of indoor residential water use at four buildings ranging in size from 1 to 1,000 apartments. Each unit had nine water-conserving fixtures from six fixture groups: dishwasher, clothes washer, kitchen faucet, shower (two), toilet (two), and bath faucet (two). The instantaneous (1-s) PRP flows were averaged over five different time steps—5, 10, 30, 60, and 120 s—while ensuring that each series conserved mass. From these time series, the ratios of the peak demand relative to the 10-s logging interval were determined. The results presented in Fig. 2 show that the peak demands captured over 1- and 10-s intervals are approximately 1.17 and 1.12 times that from the 1-min interval, respectively. These ratios agree well with Cominola et al. (2018), who found the ratio of the 10-s peak indoor demand to the 1-min peak demand was 1.12 in a building with 500 units.A 10-s logging interval was selected to permit direct comparison between previous studies that used the same logging frequencies (Douglas et al. 2019; Mayer et al. 2019).The current study presents an adjusted peak flow. Measured 1-min peak flow rate values were increased by 1.2 to allow a direct comparison to 10-s logging interval, as shown in Fig. 2.Step 4: Observed Peak Water Demand in BuildingsTwo pieces of information are extracted from the measured water consumption data to characterize water demand observed at each of the four monitored buildings. They are termed (1) adjusted 99th percentile flow; and (2) adjusted peak flow. Both are measured values, increased by a factor of 1.2 to enable direct comparison of 60-s flow rate data against 10-s flow rate data (Fig. 2).The adjusted 99th percentile flow is the focus because it is comparable to the design peak demand estimated by plumbing standards. There is a lack of consensus, however, on the operational definition of the 99th percentile flow. In this study, the measured 99th percentile flow is adapted from Omaghomi et al. (2020). At each of the four buildings, the hour having the largest water consumption during the entire monitoring period is identified, resulting in a data set of 60 1-min flow rate observations. From these flow rate observations, the 99th percentile of each vector was estimated. The 99th percentile value assumes values were normally distributed and excludes all zero flow rate values (if any) because it is assumed a value of 0 would impose zero load on the plumbing system. The 99th percentiles were then increased by a factor of 1.2 to give the adjusted 99th percentile flow.Finally, the measured peak flow is the maximum observed flow rate over the entire observation period for each building. This is also multiplied by the adjustment factor of 1.2 to establish the adjusted peak flow and serves as a reference to the adjusted 99th percentile flow.Step 5: Comparison with the Design Peak DemandTo provide a quantitative assessment of the overestimation problem in AS/AZS 3500.1:2021, the adjusted 99th percentile flow and adjusted peak flow were compared with the design peak demand values determined from the current Australian and New Zealand plumbing standard AS/AZS 3500.1:2021 using a bar chart.To provide a quantitative assessment on how selected international codes and new approaches would perform in Australian multilevel residential buildings, the adjusted 99th percentile flow was compared with the two German codes [DIN 1988-3:1988 (DIN 1988) and DIN 1988-300:2012 (DIN 2012)], the British and European code [BS EN 806-3:2006 (BSI 2006)], the WDC from IAPMO, and the VM from ABCB. These codes and approaches were selected because they result in less design peak demand when compared with the current Australian and New Zealand plumbing standard AS/AZS 3500.1:2021 (discussed in detail in the “Results” section).DiscussionThis study identified several issues that require further research. They are discussed in the following sections.Sampling Design for Peak Flow MeasurementFurther considerations are needed toward defining what is an accepted temporal resolution to accurately capture peak hydraulic events. From a plumbing design perspective, the wording used in international plumbing codes such as instantaneous or simultaneous does not clearly define the intended temporal resolution of a design peak flow rate. The rates observed in standing studies are varied. In the HCAA water demand investigation, observations captured over 1 min show attenuation of peak hydraulic events associated with the short and sporadic use of plumbing fixtures. Yet collection of data at the 1-min temporal resolution allows long-term monitoring over several months, providing confidence toward representative conditions, which is typically a limitation of smaller time scales because of reduced monitoring periods as a result of the increased data storage requirements. Larger time steps may be better suited to larger buildings because the impact of short erratic fixture events is less significant on the total instantaneous demand due to the increased number of fixtures in operation during peak water consumption periods.Definition of 99th Percentile FlowResearch efforts are needed to develop a practical definition of a specific building’s 99th percentile flow for comparison with plumbing design codes and standards. In current plumbing codes and standards, the 99th percentile flow is typically considered as the 99th percentile of likely demand during congested period of water use, where it is thought that queuing for fixture use is present. However, water consumption data obtained from many studies suggest that queuing is not present or occurs rarely during typical peak water usage behavior. The current study and the recent study by Omaghomi et al. (2020) used observations inside the single hour of largest water consumption (period of congested use) and a normal distribution to derive the observed 99th percentile flow. This 99th percentile flow rate was used to assess the accuracy of selected methods to design peak flow rates within international plumbing codes and standards. There is a need to define (1) the appropriate worst-case scenario in modern residential buildings regarding water demand; and (2) the 99th percentile flow from this worst-case scenario.Implications of Reduced Pipe SizeA systematic approach should be adopted when creating the next generation of plumbing codes. Simply reducing the size of the pipe may cause unforeseen side effects. An increase in energy required to transport water throughout a building’s plumbing system will be a negative by-product of a reduced pipe size because of increased pipe friction. Currently, the energy balance for a specific water distribution system, described by Walski (2016), is seldom considered in plumbing design. In addition, research has shown that erosion or velocity-induced corrosion is a significant problem in copper plumbing systems (Roy et al. 2018), even though almost all the systems are already oversized under current design standards. Biofilms grow in plumbing systems (Zlatanović et al. 2017) and the biofilm growth and the hydraulic condition can be interdependent (Gong et al. 2019). It is unclear how the increased velocity in future systems would affect biofilm growth and water quality. However, new developments in water quality modeling presented by Palmegiani et al. (2022) suggest the influence of pipe size on water quality can be evaluated.Another important factor is the impact of hydraulic transients, also known as water hammer. As pipe size reduces, pipe impedance and flow velocity increases, which amplifies the magnitude of the pressure waves for the same flow change (Gong et al. 2013). Continuous pressure monitoring in a municipal water distribution system has shown that the transient pressure waves induced by building water usages can be significant and negatively impact the water distribution pipes (Stephens et al. 2017). Conversely, Lee et al. (2012) demonstrates that the hydraulic transients triggered within building services lines, and by extension, the municipal system, induce pressures low enough to allow the intrusion of microbial and chemical contaminants. The mixed use of plastic and metallic pipes can affect the hydraulic transient behavior significantly (Bohorquez et al. 2020; Gong et al. 2018). Moreover, specific considerations to transients may be overlooked because the process to calculate transients can be seen as complex and time-consuming (Izquierdo and Iglesias 2002). While many steady-state hydraulic software packages are relatively inexpensive, this is not the case for hydraulic transient software packages, suggesting that cost may be a deterrent to conduct such an analysis (Soriano et al. 2016).Other Limitations in Plumbing DesignBeyond maximum flow, velocity, and standing pressure, other aspects of hydraulic performance are rarely considered in plumbing system design. While extended-period hydraulic modeling is now standard practice when designing municipal water distribution systems, plumbing network design is typically based only on simple calculations of peak flow and head loss, as evidenced by the plumbing codes reviewed in this research. Sharp and Sharp (1996) note there is a misconception within the industry that hydraulic design involves only the steady-state flows and the associated head losses. The skills formerly used to design smaller, less complex plumbing systems are not well suited to large premises plumbing systems like those found in modern high-rise buildings. A tabular method to determine system losses is a common practice for pipe sizing (Garret 2008). Examples of this approach are presented in many published design aids and plumbing standards. Gad and Abd-Elaal (2016) state that current methods used to size the water supply network in residential buildings do not consider reliability of a system to overcome unexpected demand or system failures. Additionally, Ferreira and Goncalves (2020) highlight that traditional pipe sizing methods ignore how a plumbing system operates outside the peak period. Ferreira and Goncalves (2020) suggest there may be opportunities for further reduction to pipe sizing and economics savings if designers considered whole-system behavior. In addition, existing research has a strong focus toward residential buildings. There is a lack of understanding and design references for other building types.ConclusionsThis research conducted a performance assessment of the current Australian and New Zealand cold water plumbing standard AS/NZS 3500.1∶2021 in peak demand design for multilevel residential buildings. A literature review was conducted on the international development of residential water demand estimation models, the development history of Australian and New Zealand plumbing standards, and contributors and implications of peak demand overestimation. A methodology was developed to enable a benchmarking study among comparable international practices in peak demand design and to enable a quantitative assessment of the overestimation by comparing the design value with the actual flow in modern multilevel residential buildings. The review and methodology provide a reference to study other plumbing standards and codes.The comparison between AS/NZS 3500.1:2021 and comparable international standards (Fig. 3) shows that the standard Australian and New Zealand practice results in much higher estimation of the peak demand than the British and European code BS EN 806-3:2006, the previous and current German codes (DIN 1988-3:1988 and DIN 1988-300:2012) and the WDC from International Association of Plumbing and Mechanical Officials (IAPMO 2020). Other international codes considered, including US-based codes [International Plumbing Code, Uniform Plumbing Code (UPC:2018), and National Standards Plumbing Code (NSPC:2015)], British plumbing code (BS 6700:1997), and the Institute of Plumbing code (IoP/CIPHE 2002), all result in even higher estimation of the peak demand than the Australian and New Zealand practice. The VM under investigation by ABCB produces smaller values in design peak flow; however, they are larger than the design values from BS EN 806-3:2006, DIN 1988-3:1988, DIN 1988-300:2012, and the WDC when the number of apartments is large (>75 for the fixed design considered in this study).The analysis of the actual water demand at four Australian multilevel residential buildings has confirmed that, like many other existing plumbing codes, AS/NZS 3500.1:2021 significantly overestimates the design peak flow rate used to guide plumbing system design. The adjusted 99th percentile design peak flow rates measured in the four buildings are a fraction (23%–28%) of peak flows determined using the standard approach defined in AS/NZS 3500.1:2021. None of the international peak demand design standards considered in this research can be readily applied to Australian multilevel residential buildings. They either overestimate or underestimate the peak flow for all or some of the buildings.A systematic approach in needed to address the peak demand overestimation issue. Sampling design for capturing the actual peak flow and practical definition for a specific building’s 99th percentile flow are recommended for future work. Simply reducing the design flow and pipe size may induce unexpected consequences. Lack of hydraulic modeling and lack of consideration of hydraulic transients in the design are two of the many issues confronting the design of complex premise plumbing systems.References Arbon, N., M. Thyer, D. H. MacDonald, K. Beverley, and M. 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