1 Introduction

Urban planning is understood as the collectivity of all organised material and immaterial processes changing our cities. This refers to different dimensions such as physical forms, economic functions, social activities, and natural aspects. The fields of application of urban planning are wide-ranging and cover a large set of subjects such as general design of urban districts, urban redevelopments and the initiation of blue and green spaces (BBR 2000). The foundation of all urban development and upgrading processes lies within technical infrastructures, such as energy networks, transportation services, fresh water distribution and wastewater management and its significance and potential for creating more sustainable and resilient cities is been known for decades (EC 1996; Neuman 2011). To govern and conduct planning processes, a wide range of conventional tools can be applied and digital communication systems such as geographic information systems (GIS) are commonly used (Dosch 1998; Martinez et al. 2019). The ‘megatrend of digitalisation’ (BSt 2019) accelerates the transition towards a broader and more innovative spectrum of digital planning. Politically, this trend is prompted by the European Union through the Digital Decade policy programme 2030 aiming to foster the digitalisation of its member states (EU 2022). On a national level, the German government has identified infrastructure and equipment as one of four key areas to shape the digital transformation (BPA 2021) and fosters the intelligent networking of municipal infrastructures to transform cities into more efficient, technologically advanced, sustainable and socially inclusive spaces via its Smart City Charter (BBSR 2017). Simultaneously, local, national, and international networks support the collaboration of involved stakeholders and the integration of digital approaches into urban development processes (Berlin Partner 2023; BMWSB 2023a, 2023b). In the following, the synergies among urban planning and wastewater infrastructure planning and means of digitalisation, such as the metaverse and augmented reality, are described.

The notion of the metaverse may be described as a digitally extended space that is created through the interaction of virtual and physical reality (Mystakidis 2022). It is argued that the metaverse is not all-encompassing, but that various digital additions allow locally or temporally limited digitally expanded spaces to be created (Buchholz et al. 2022), accordingly the term ‘decentralised metaverses’ is sometimes used (Hashash et al. 2023). Wang et al. (2023) see the metaverse as ‘an evolving paradigm of the next-generation Internet, [which] aims to build a fully immersive, hyper-spatiotemporal, and self-sustaining virtual shared space for humans to play, work, and socialise’. A number of technologies contribute to the metaverse, including the concept of digital twins (Far and Rad 2022; Hudson-Smith 2022), mobile networks (Tang et al. 2023), virtual reality (Wu et al. 2023) and augmented reality (Baer et al. 2022). Augmented reality in particular is to be considered a key element in establishing the metaverse, since the digital extension of physical spaces is an inherent feature of augmented reality (Arena et al. 2022). At the same time, data, which is usually visualised through augmented reality, also takes on a key position (Almeida Pereira et al. 2017; Baer et al. 2022; Reipschlager et al. 2021) and thus forms a defining component of the metaverse. The strands of (i) augmented reality-driven digital extension of (ii) physical space via (iii) data, which represent both a prerequisite and a result of planning processes, bring together the concepts of metaverse and urban planning. Accordingly, Hudson-Smith (2022) combines these three strands. He sees these digital spaces as a powerful tool for urban planning that cannot be ignored by the discipline any more in the same way as the internet has not been ignored since 1989: The metaverse will open up new use cases for urban planning (Dorostkar and Najarsadeghi 2023). At the same time, however, Hudson-Smith and Shakeri (2022) fear that the discipline of urban planning is still somewhat overburdened by the affordances of the metaverse. However, we believe that being overburdened or, in positive terms, maturity is not dichotomous, and that this study can contribute to narrowing the distance between urban planning and the metaverse: In our view, the use of augmented reality technology presented in this article increases the operationality of the metaverse in urban planning.

In the last years, a fast technological development of AR technology was observed leading to numerous AR applications complement existing conventional planning tools or offering new opportunities for practitioners and learners (Wolf et al. 2019, 2020). For example, maps are an important tool in urban planning. To overcome challenges in understanding those traditionally two-dimensional presented information, several protypes of augmented plans had been developed so far. Broschart and Höhl (2015) introduced the augmented development plan allowing stakeholders to view administrative requirements regarding the position and volume of buildings in 3D by positioning their smartphone over a marker-based plan. Tomkins and Lange (2020) and Rohil and Ashok (2022) applied mobile AR visualisation techniques to landscape architecture, visualising larger plans and presenting information such as building shapes, land-use allocations, transport facilities, and the design of green spaces. Another approach of using AR in urban planning it to visualising proposed changes to the real environment. Explored were, for example, the creation of virtual static structures (Awang et al. 2020; Rohil and Ashok 2022), dynamic objects such as people (Carozza et al. 2014) as well as general design proposals (Shouman et al. 2022) for full-scale walkthroughs intended to support decision makers. Allen et al. (2011) developed a system to visualise proposed 3D architectural designs on existing buildings, which can be rated by users. Similar prototypes to encourage the public to participate in urban planning projects at a early planning stage are designed by Imottesjo and Kain (2018), Fegert et al. (2021), and Saßmannshausen et al. (2021). AR prototypes covering large-scale planning interventions are increasingly used by cities (Schürmann et al. 2021; Tatwort 2022). The City of Lucerne designed in collaboration with the Lucerne University of Applied Sciences and Arts an AR application to visualise planned structural interventions along a major road incorporating trees, a new bicycle station, public seating and plantings (Schürmann et al. 2021). An approach, which can be also applied for specialised planning features, such as wind farms (Grassi and Klein 2016) or in the context of tourism developments by visualising previous historic sceneries (Cibilić et al. 2021; Özkul and Kumlu 2019). The increasing presence of various data offers additional opportunities for AR tools in urban planning to take invisible aspects like noise (Maffei et al. 2016), wind (Harbola et al. 2022), temperature (Fluxguide 2023) and environmental pollution (White and Feiner 2009) into consideration. In 2010 Graf et al. created a framework ‘based on a multi-source urban planning backbone aiming at interactively investigating fast ‘what-if’ analysis of urban planning simulations and creating awareness of possible environmental impact’ and Veas et al. (2013) suggest a prototype for environmental experts to monitoring natural dynamics such as snow fields. To address the issue of air pollution researchers have designed AR apps to visualise air pollutants aiming to raise awareness of potential risks and leading to pro-ecological behaviour. Mathews et al. (2021) propose a prototype system to visualise 12 pollutants in the air based on previously collected data of weather stations. Pochwatko et al. (2023) present such complex scientific data in a comprehensive way through a system allowing smartphone users to view the process of how air pollution particles were entering the trachea and obscuring the lungs during regular breathing. Available real-time data enable user to run simulations on-site.

Referring to technical infrastructures in general and wastewater infrastructure planning in particular, research regarding AR technology is still in its initial stage. In construction, AR is mostly used as part of demonstration projects with a strong focus on the implementation of the technology (Heinzel et al. 2017; Nassereddine et al. 2022; Zollmann et al. 2014). A similar trend can be generally observed in the energy (Revolti et al. 2023), traffic and wastewater sector. For the latter, the major research focus lies on the spatial visualisations of the buried infrastructure using mobile devices (Dolaş and Ulukavak 2023) as well as on the windscreen of mechanical equipment (Hansen et al. 2021; Talmaki et al. 2010) in the context of construction and maintenance. Van Nguyen et al. (2023) develop a framework for integrating geographic information system (GIS), a 3D-creation platform, AR techniques and machine-learning algorithms aiming at generating visualisations of sewers ‘ condition via a Microsoft HoloLens. The motivation to use the HoloLens steered from the potential to offer users several options of interaction, e.g., gaze, gesture and voice. Other researchers investigate the technical potential of AR for environmental planning. In detail, Tomkins and Lange (2019) explore the options for flood visualisations on maps and Cheok et al. (2022) in virtual environments. Haynes et al. (2018) develop an AR app for on-site content authoring and flood visualisations. Based on real time data, potential flooding along river beds can be visualised with the opportunity to add annotations on mobile devices to support water experts in situation of natural hazards. Within the scope of a user study the app was generally rated as utilisable by professionals and the authors claim that ‘the flexibility of the app permits a broad range of applications in planning, design and environmental management’. A similar approach with the focus on showing residents the potential effect of flooding with reference to their building was explored by Andrade et al. (2022).

Although one might argue that findings, such as a higher willingness to participate in urban interventions processes if AR technologies are used, present an immanent chance of transferability towards the wastewater sector, insights from original studies bear a high relevance for introducing AR into that sector. Additionally, studies addressing the perceptions of practitioners are generally limited. Thus, the motivation of the present chapter is to explore AR in the context of wastewater infrastructure planning with the focus on the perspectives of practitioners who are not familiar with that technology yet. Consequently, an AR-scenario was designed and presented to practitioners in order to evaluate the perceived value of AR for wastewater infrastructure planning.

The remainder of the article is structured as follows: In the next section, the experimental set-up, scope, and methodology of the study are described. Section 16.3 presents the results, which are discussed in Sect. 16.4. Conclusions in Sect. 16.5 end the article.

2 Study Design

2.1 Experimental Set-Up

Selection of the vGIS app: To identify a suitable app at the intersection of technical infrastructure planning and AR visualisation a market analysis was carried out. The defined inclusion criteria an AR app should meet in order to be considered for the study, are presented in Table 16.1.

Table 16.1 Inclusion criteria for AR apps
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Previously conducted research by the authors (Söbke et al. 2018) and a web-search resulted in identifying four AR apps. Relevant attributes of the four AR apps are presented in Table 16.2. Based on an assessment of those four apps, the researchers opted to work with vGIS for the study as it seemed to be most suitable. Tested were the practicability and functionality of the apps (e.g., required preparation of data, process of data transfer into the app, accuracy and design of visualisation, handling of the app). This included test runs with sample data of technical infrastructures in case a trial version of an app was available.

Table 16.2 Comparison of four AR apps for infrastructure planning (selected app vGIS in italic)
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Soft- and hardware: vGIS is an AR app specifically developed to support various stakeholders such as public service providers, engineers and planners in the management of technical infrastructures (vGIS 2023). As a high accuracy AR application, it converts geographically referenced information models from multiple sources into location-based augmented visuals. For operation, vGIS runs a cloud-based platform to save data layer and stream its information via the installed vGIS app to a mobile device (vGIS 2023). In our case, IFC format were stored. For the technical set up, two hardware components were used. For operating vGIS and visualising and interaction with the AR scenario an Android tablet Samsung Galaxy Tab Active 3 Enterprise Edition was used (Android version 12, 8 inch). To receive the GPS location of the physical location with a high accuracy, an external antenna was applied. To improve the localisation data, the researchers worked with the Leica Zeno FLX100 as a previous study had shown its accuracy, performance, and reliability (Osipova et al. 2022). The tablet and the antenna are connected via bluetooth. To exchange the GPS data, the ‘Zeno connect app’ was installed on the tablet. The correction factors for the GPS were retrieved from the state-owned satellite positioning service via an internet connection. The antenna was mounted to the tablet via a physical holder enabling users to hold the tablet and the antenna as one item in one hand while the other hand could be used to navigate the AR app. The technical set-up is presented in Fig. 16.1.

Fig. 16.1
An illustration of a model. A tablet with v G I S and Zeno-connect app has bluetooth connection with external antenna receiving G P S data from 3 satellites. The tablet with environment to be complemented by augmentations interacts with cloud for data from v G I S platform and world wide web for G N S S correction factors.

Technical set-up of the AR app

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AR Scenario: As location for the study a public square (Herderplatz) in the German town of Weimar was selected. The chosen study area consists of a historical square of approximately 100 × 100 m and is located in the city centre. The square is surrounding an old church and is flanked by historical two to three storey high houses (see in Fig. 16.2a). The location was selected based on its suitable environment to conduct the study, such as its open terrain, low traffic, interesting factors regarding the wastewater infrastructure like sewers coming from multiple directions merging at the square as well as the availability of georeferenced data. Wastewater infrastructure systems are complex technical systems that aim to provide various services simultaneously. Their purpose lies in the discharge of wastewater and rainwater from private and public areas to ensure urban hygiene, water sanitation and water protection (Hillenbrand et al. 2010). In this respect, the existing wastewater infrastructure consists of a wide range of technical elements (BDE 1997). The augmented scenario encompassed drainage covers, manholes, pipes, and conflux installations for discharge of rain water and wastewater originating from households (see in Fig. 16.2c). Additional to the wastewater system, geographically referenced information of building shapes and a digital terrain model of the study area was incorporated.

Fig. 16.2
3 parts. a and c. Photographs. a. An open, paved public square with 2 or 3-storeyed houses on either side. b. A map with layout of waste and rain water pipes around the square. c. A person has a mobile phone with an attachment collecting geographically referenced information for digital model of the study area.

a Study area Herderplatz (Weimar/Germany), b wastewater infrastructure to be augmented, c tablet with antenna

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Using vGIS, all incorporated infrastructure elements could be visualised in 2D or 3D mode including cross sections and bird's eye view. Further, the AR app offered several virtual tools, e.g.: (a) calibration to synchronise the position and perspective of the user with the georeferenced data, (b) manual adjustments to align the direction and elevation of the visualised model with the physical environment by camera modus or waking on a virtual line, (c) measurement of physical distances by digitally marking two objects or walking between two positions, (d) data collection to digitally add objects such as manholes to a data library, (e) field reporting by adding text, screenshots or other pictures, (f) virtual excavation to enhance the perspective of complex elements and (g) record as well as livestream the user’s field visit with others (see Fig. 16.3).

Fig. 16.3
Four 3-d illustrations of a digital model with pipelines on a paved area. a. Position of user on a dial with respect to the layout. b. Alignment of a joint between the supports. c. Marking of start and end points in the area. d. A man-hole with distance labeled 2.67 meters.

a, b Augmentation of wastewater infrastructure, c field reporting, d virtual excavation

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2.2 Scope and Methodology

The study was conducted in summer 2022. In total 18 persons participated, 83% (14) of which identified as male, 17% (4) identified as female. All participants (N = 18) had a professional back ground in infrastructure planning and were at the time of the study working in the field, teaching, or attending extended studies in that area. The average age of the participants was 37.4 years, differentiated by age groups 5 (28%) participants indicated an age of 20–29 years, 7 (39%) 30–39 years, 3 (17%) 40–49 years, 2 (11%) 50–59 years, and 1 (6%) were older than 60 years (total range: 27–61 years). While the majority of the participants (11, 61%) were holding a master’s degree, 4 (22%) had a bachelor’s degree, one (6%) accomplished a practical training program and two (11%) a doctorate. All participants had previously been in contact with the faculty of civil engineering at the Bauhaus-Universität Weimar as part of their working or study routine. The participants had been invited via e-mail and telephone calls.

The study consisted of three phases: (a) introduction phase (b) exploration phase, and (c) evaluation phase.

Introduction phase: In the introduction phase, the researchers met each participant individually at Herderplatz and provided an introduction to the task, the technical set-up (tablet, antenna, AR app) and the location. When the AR app was introduced to the participants, the researchers ensured that all available functions were presented. At the end, the equipment was handed over to the participants. In average this phase lasted 20 min.

Exploration phase: In the following exploration phase, the participants were asked to interact with the AR technology. As previously mentioned, location-based AR technology is not yet a standard medium in infrastructure planning, therefore resulting in limited experience amongst technical infrastructure professionals. Therefore, participants were asked to familiarise themselves with the AR scenario and explore the wastewater infrastructure at the entire study area. The detailed tasks participants were asked to perform during the exploration phase are presented in Table 16.3.

Table 16.3 Tasks of the exploration phase
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During the exploration phase, the researchers accompanied the participants in case of technical challenges or questions. A time limit of that phase was not predefined in order to let the participants explore the AR scenario with caution; thus, preventing stress-related mistakes which could distort the study’s results. In average, this phase took 40 min. When the participants indicated that they had completed the exploration of the AR scenario they were asked to join the evaluation phase.

Evaluation phase: The evaluation aimed to deliver findings regarding three focus points: (1) the significance of location-based AR technology in infrastructure planning (see Sect. 16.3.1), (2) the participants’ experiences with the AR app (see Sect. 16.3.2), and (3) technical aspects of the AR scenario (see Sect. 16.3.3). Methodologically, each focus point was addressed by a mixed methods approach (Kelle 2014) consisting of the following quantitative and qualitative tools:

  • Questionnaire: Besides demographics, experiences in infrastructure planning and AR, a questionnaire was used to collect results on all three focus points. For the significance of AR technology in infrastructure planning and the participants’ experience with the two 10-point Likert scales were used. The usability was measured by the Post-Study System Usability Questionnaire (PSSUQ) (Lewis 1995). Due to the small sample size, descriptive statistics only were applied for analysis.

  • Semi-structured interview: To deepen and discuss the three focus points qualitatively, a semi-structured interview (Gläser and Laudel 2010) was conducted with each participant. The interviews included questions regarding the (dis-)advantages, practical fields, and scenarios for the usage of AR apps, problems with the AR app, as well as expectations and acceptance of AR technology in daily work routine. The participants were interviewed after finishing the questionnaire. The conducted interviews were analysed according to the Qualitative Content Analysis (QCA) method (Gläser and Laudel 2010).

  • Open discussion: To receive further feedback an open discussion was held in which each participant could raise relevant points they wanted to share with the researchers (Gläser and Laudel 2010). In total, the evaluation phase lasted on average 15 min. The addressed points were embedded in the QCA of the interviews.

The design of the evaluation phase with the three methodological approaches was validated by test-runs conducted with two students from the faculty of civil engineering who had gained practical work experience in the field of wastewater infrastructure beforehand.

3 Results

To scientifically contextualise the results, the background regarding both experience in infrastructure planning and digital tools were analysed as shown in Fig. 16.4. In detail, participants were asked to rate their professional experience in the planning of wastewater infrastructures on a scale of 1 (no experience) to 10 (highly experienced) as well as their experience with AR. The average self-assessed experiences for technical infrastructure planning were rated 5.6 on average (SD = 2.45), while the average AR experience was rated 2.8 (SD = 1.90). On a scale of 1 (‘I am not familiar with’) to 10 (‘I am a digital native’) the participants were requested to rate the level of familiarity with digital tools. The average self-assessed familiarity with digital tools was 8.1 on average (SD = 1.75). Further, the participants were also asked to rate their level of interest before and after exploring the AR scenario. Using the same scale, the indicated interest before the experienced AR scenario was 5.9 on average (SD = 2.6), while the interest after using the AR app had increased to 7.4 (SD = 2.71).

Fig. 16.4
A horizontal bar graph with error bars of ratings. Experience in wastewater infrastructure planning 5.6 with the maximum error of approximately 8. Experience in augmented reality 2.8. Familiarity with digital tools 8.1. Interest in A R before using the A R scenario 5.9. Interest in A R after using the A R scenario 7.4.

Self-assessment of the participants (N = 18)

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The following results are obtained from the individual questionnaire, the semi- structured interviews and the open discussion. The results are structured by the researched focus points. In each section the quantitative results are presented first, followed by the qualitative findings.

3.1 Significance for Wastewater Infrastructure Planning

As early as in the 1980s Bowen (1986) and Young (1984) observed, that the potential to enhance workflows by new technologies is regularly impeded by users’ unwillingness to accept and use available systems. Thus, even if developers design technologies prompting to change work routines, users are the crucial factor regarding their implementation into a running system. Therefore, the significance of the AR technology in wastewater infrastructure planning was analysed by a questionnaire. Further, advantages and disadvantages as well as the potential of AR technology for the different planning phases and operational activities were explored by interviews.

3.1.1 Usefulness

For measuring the usefulness, participants were asked in how far they ‘disagree’ (referring to value 1 on a 10-point Likert scale) or ‘agree’ (value 10) to various statements (see Fig. 16.5). The results show, that all items in which participants could indicate an experienced benefit for the planning process received high mean values. Analysing the results precisely, two groups showing similar mean values are to be identified. The values of the first group (a: 9.2–h: 7.9) indicate most of the participants have experienced a highly positive impression and see AR as a technology that can enhance planning processes by, e.g., reducing mistakes and misunderstandings, generating a similar spatial–technical understanding among different stakeholders involved in a project and supporting coordination and communication. The second group consists of one statement reflecting in how far planning partners of the participants would appreciate AR technology on their working routine. For this statement, the participants seemed to have minor doubts (i: 6.8). The third group, referring to the preference of conventional planning tools, achieved significant low values (j: 3.8–l: 1.8). In reference to the first group, this indicates that the participants strongly support the idea to enrich their current available planning instruments by AR technology. Deriving from the standard deviations, one conclusion can be drawn. In particular, the wide standard deviation of items ‘f–g’ indicates that some participants had not been convinced of the advantages of the AR technology. This might correlate with the large standard deviation of items ‘j–k’ implying that some participants do not experiences the need to extend their current repertoire of planning tools.

Fig. 16.5
A horizontal bar graph with error bars of the usefulness of A R technology with ratings for 12 statements. A R can reduce mistakes and enhance understandings during planning has the highest of 9.2. I don't see any benefit of A R for infrastructure planning has the lowest of 1.8.

Usefulness of AR technology in infrastructure planning (10-point Likert scale (‘disagree’ for 1, ‘agree’ for 10), N = 18)

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3.1.2 Advantages and Disadvantages

To investigate the perceived usefulness of AR in infrastructure planning more comprehensively, the authors were seeking to learn more about its practical potential as well as constraints. In detail, the participants were asked which advantages and disadvantages they expect by using of AR app in the field of technical infrastructure planning. Based on the conducted interviews, 63 statements were extracted in which advantages were mentioned. In total 37 statements were included that referred to disadvantages. The extracted statements were clustered by topic and summarised in Table 16.4. Results show that participants expect benefits within a wide range, particularly anticipating advantages regarding the understanding of planning situations, process management, and collaboration among stakeholders involved in a project. The acknowledged disadvantages were confined social restraints and technical barriers.

Table 16.4 Advantages and disadvantages of AR in wastewater infrastructure planning
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3.1.3 Planning Phases and Operational Activities

The development of technical infrastructures, follows a clear sequence of phases. Hamdi and Goethert (1997) structure a planning process in the following six phases: (a) initiation, (b) planning, (c) design, (d) implementation, (e) maintenance and (f) evaluation. Based on that structure the interviewed experts were asked to name the planning phase(s) in which they see potential for the AR technology. With the option to name several planning phases, the majority opted to use AR particularly at the beginning of a planning process. In detail, 72% would apply it during the concept development phase, while 78% deem it helpful during the planning phase and 50% in the design phase. Half of the participants (50%) stated to see its benefits in the implementation phase and nearly every fourth would use during maintenance (28%) as well as for evaluative purposes (28%). Further, the participants were asked to describe operational activities they would use the presented AR technology for. In total, 83 statements were given which were summarised and are presented as follows:

  • Identification and management of planning conflicts (e.g., clashes between existing and proposed infrastructure elements).

  • Design and identification of suitable proposals (e.g., by participation of different stakeholders, hybrid on-site meetings, verification of proposed changed on-site).

  • Locating and mapping of environmental features on-site (e.g., existing infrastructures, trees, roads), risks of natural hazards (e.g., risks of flooding), administrative aspects (e.g., plot boundaries), topography.

  • Transferring project related knowledge from planners and engineers to operators.

  • Retrieving object-related information quickly on-site.

  • Verifying, updating and upgrading existing plans by mapping existing environmental features and adding new information to database.

  • Supporting logistical aspects on-site.

  • Aligning construction methods and materials.

  • Lowering barriers of project related understanding, participation and conflict negotiation through supporting visualisations.

  • Supervising of construction and project framework (e.g., working routines, project timeline and progress, quality of work, stakeholder involvement).

  • Documentation of progresses (e.g., in planning and building) and final results.

3.2 User Experience

Based on the interaction with technical systems (Norman 1998), user experiences may be understood as a decisive factor determining in how far users agree to use an app (Wolf et al. 2021). Thus, analysing the user experience is a crucial aspect within the scope of implementing new technologies. As AR has barely been used in the field of infrastructure planning, the user experienced was analysed by focusing on the usability of the AR app using a standardised questionnaire. Further, the user interface, the willingness to adapt to AR technologies as well as participant’s assumptions regarding its future occurrence in the planning sector were assessed by semi-structured interviews.

3.2.1 Usability

The Post-Study System Usability Questionnaire (PSSUQ) is a tool to investigate user satisfaction based on 19 items that are known to ‘influence user perception of usability’ (Lewis 1995). The PSSUQ uses three subscales to assess user satisfaction, namely, System Usefulness, Information Quality and Interface Quality. Another item refers to the Overall Satisfaction. A low score on an item indicates higher satisfaction. For usage, the PSSUQ was alert slightly by exchanging the term ‘system’ with ‘AR prototype’. The results (see Fig. 16.6) show that in all subscales positive feedback of the participants was achieved. That means, the participants detected the usefulness for the AR app for infrastructure planning, valued the quality of the presented information as well as the interface and were satisfied with the AR app in general. The significant level of the standard deviation detected in the subscale Information Quality might be caused by the different kind of data each participant requires during their daily working routine which might have been available or not available in the AR scenario.

Fig. 16.6
A horizontal bar graph with error bars of ratings for usability. System usability 2.6. Information quality 2.2. Interface quality 2.6. Overall satisfaction 2.5. Respective estimated maximum error values are 3.3, 2.9, 3.2, and 3.1.

Usability of the AR app (subscale values of PSSUQ (7-point scale (‘Strongly agree’ for 1, ‘Strongly disagree’ for 7) (Lewis 1995), n = 18)

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3.2.2 User Interface

When asked about the user-interface (Table 16.5), the majority (16 participants) reported a positive experience by mentioning its user-friendly and intuitive design. In example it was stated that the design of functions ‘is sufficient and not overwhelming’ and app ‘needs a bit of practice (…) which is not a problem’. Adding to the positive experiences, five participants considered the presented visualisation of the menu and the augmentations as appealing, stating, e.g., ‘it doesn't need a photo-realistic representation, that would be too finely detailed and thus confusing’. A slow and malfunctioning interface was experienced by five participants. As malfunctions an incorrect positioning of augmentations, lacking and not responding menu functions, and language issues were considered. For three participants, the displayed amount of information and visualisations were confusing. Further, statements regarding the high sensitivity of the interface, suggestions for further improvements of the interface (e.g., using a pen or AR classes) and the ‘annoying weight of the antenna’ were given. In total, 44 statements had been extracted and analysed.

Table 16.5 Interface-related statements
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3.2.3 Digital Ecosystem

‘Digital ecosystems refer to complex and interdependent systems and their underlying infrastructures by which all constituents interact and exhibit as a whole self-organising, scalable and sustainable behaviours’ (Li et al. 2012). As networks they interconnect digital technologies, platforms and services to create added value for companies and consumers (Briscoe 2009). In this context, the participants were asked to which extent they would prompt changes to their currently used digital ecosystem to accommodate the implementation of the AR app. In detail, they were asked regarding changes to their software, hardware and main platform. The option to use AR technology as an add-on to their existing technological set-up was offered as well.

In general, a trend towards the integration of AR technology into the current technological environment is observed (see Fig. 16.7). The majority declined changes to their currently used digital ecosystem. Only every tenth (two participants) would make changes to running software, the rates of willingness for switching hardware components (1 participant) and the main platform (0 participant) are lower. Referring to the comments of the participants their declining positions are based on, e.g., technical problems experienced with the app, expected inadequateness coming with implementing new information technologies and the financial limitations. Notably, there is a large group of participants who did not decline changes to their current technological set-up but seemed to hesitate (a-c). As causes participants mentioned, e.g., the unclear value of the technology in smaller projects, the required effort to train users and the need to alter established working routines and work flows. In contrast, 67% would already implement the AR technology based on their experiences with the developed AR scenario in their current set-up as an add-on to enhance the currently used planning instruments.

Fig. 16.7
A grouped bar graph with yes, no, and maybe % for willingness to make changes in digital ecosystems to accommodate A R technology. a. Change of software 11, 56, and 33. b. Change of hardware 6, 56, and 39. c. Change of main platform 0, 61, and 39. d. Extending current digital habitat 67, 6, and 28.

Willingness to make changes in digital ecosystems to accommodate AR technology (n = 18)

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Further, the participants were asked to assume how far AR technology will be becoming a common tool in infrastructure planning in the next years. For 56% of the participants, its usage is imminent and inevitable. Within this group, mentioned determining factors—apart from the advantages to planning—were the alternation of generations in the private and public sectors including the presumed or experienced high affinity of younger professionals with digital tools, the general ongoing trend towards digital process management in the society and particularly in the architecture, engineering, construction and operation (AECO) industry, e.g., through application of the Building Information Modelling (BIM) method and usage of digital twins as well as disadvantages of conventional planning tools. The remaining 44% did not exclude a wide implementation of AR technology in the planning sector but attached their assumptions to several conditions. Emphasised were further developments to reach higher accuracy, reliability, and user-friendliness as well as more consumer-friendly prices to guarantee access for a broad range of planning stakeholders. One participant expressed concern regarding potentially new legal requirements hampering the daily use of AR technology in Germany, expecting less obstacles impeding this process in other European countries. Not a single participant doubted the breakthrough of AR technology in infrastructure planning.

3.3 Technical Aspects

Technical issues can have a strong impact on how users perceive a technical system or an application (Davis 1989; Park 2009). Therefore, the users’ satisfaction with crucial technical characteristics of the AR scenario were explored through a questionnaire. Additional valuable features to support the planning of infrastructures were collected via interviews.

3.3.1 Technical Characteristics

For measuring technological characteristics, participants were asked to rate six prominent technical aspects (1: ‘Very poor’, 6: ‘Very good’, see Fig. 16.8). In general, an acceptable to good level of all technical aspects was observed. Analysing the results precisely, it shows that especially the compatibility of the AR app with other software, formats, and digital platforms which might be used for infrastructure planning (e.g., Autodesk Build, Esri ArcGIS, DWA, IFC, Bentley iTwin), as well as the quality of the visualisation reached high scores (a: 7.7, b: 7.6). Focusing on factors of operation and handling of the used hardware (attaching and connecting the tablet with the external antenna, starting and working with them) and the precision of the GPS signals received slightly lower values (c: 6.7, d: 6.1). The size and the weight of the used tablet and the connected external antenna and the temporarily low GPS coverage when participants were standing close to buildings might be the reasons. While the complexity of the AR layout referring to the selection of descriptive information of the augmented wastewater infrastructure and its arrangement in the default visualisation mode achieved a medium value (e: 5.4), the accuracy of the augmentations received the lowest value (f: 4.2). Presumably, the temporarily low GPS coverage caused inaccurate positioning of the augmented wastewater infrastructure.

Fig. 16.8
A horizontal bar graph with error bars of technical characteristics of the A R app. Software compatibility 7.7. Quality of visualizatons 7.6. Handling of hardware 6.7. G P S precision 6.1. Complexity of A R layout 5.4. Accuracy of augmentations 4.2.

Technical characteristics of the AR app (10-point Likert scale (‘Very poor’ for 1, ‘Very good’ for 10), n = 18)

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3.3.2 Additional Features

As all the participants are actively working in the field of technical infrastructure planning, the researchers assumed that each participant requires particular tools in their daily work routine. Thus, they were requested to describe features an AR app should encompass to support their work. In total 24 statements referring to additional features were retrieved. They were categorised and are summarised in Table 16.6. The majority of participants referred to additional access to information, such as digital libraries to retrieve objects, embed the augmentation in digital twins of cities, visualise real-time data and, e.g., having support in calculation prices for planning options directly on-site. Six statements refer to the category of functions. Two participants requested the option to add freehand sketches to the augmentation on-site as well as a function which identifies planning clashes that might occur among proposed and existing infrastructures by automatically. Four statements addressed the presented visualisations. E.g., a more selective approach of presenting object-related information to improve the visual clarity was mentioned two times. Further, three participants wished for an offline version for working in areas with limited internet connection.

Table 16.6 Additional features to support infrastructure planning with AR technology
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4 Discussion

This chapter provides an insight into the perceived value of AR in wastewater infrastructure planning. Based on exploring a design AR scenario, the participants expressed a high significance for AR in wastewater infrastructure planning, which offers a high transferability to the sector of technical infrastructure development in general. The measured user experiences and technical aspects revealed some aspects to be improved in order to be used by practitioners in their working routine. However, three factors must be considered in the light of the achieved results. Firstly, the participants showed a high rate of being familiar with digital tools but expressed only low experiences with AR technology. This indicates that AR is not an established technology in the wastewater sector yet. Secondly, it is the ‘novelty effect’, defined by Tsay et al. (2020) as ‘the human tendency for heightened engagement and/or performance when encountering the introduction of a novel phenomenon, such as the introduction of a new technology’. With time, those positive dynamics fade (Tsay et al. 2020). Thus, participants might have rated the AR scenario in an extraordinary positive light. However, it can be assumed that this especially effects quantitative measurements in general and qualitative analysis referring to the experiences with the AR scenario. Results, addressing the superordinate context such as (dis-)advantages of AR for infrastructure planning and mentioned additionally features might be less effected. Thirdly, the effect of the mental effort experienced by the participants needs to be discussed. As a part of the cognitive load theory, ‘mental effort’ can be defined as the artefact between the ‘characteristics of a target task and the subject’s available information-processing capacity and […] the fidelity of the information-processing operations actually performed, as reflected in task performance’ (Shenhav et al. 2017). A previous study within the scope of the presented AR scenario as a learning tool indicates a high workload among students (Das et al. 2024). Therefore, a high mental effort among the participating practitioners can be assumed. Technical flaws requiring, e.g., to recalibrate and reposition the augmented wastewater infrastructure at the study area during the exploration phase caused supposedly further distraction. Here, it is to be noted that the information of building shapes and a digital terrain model were not included in the study as pre-tests had shown that they were prone to cause visible inaccuracies.

A limitation of the present study is the limited number of participants. While with 18 participants comprehensive qualitative results were achieved, the quantitative results require to be validated within the scope of a larger study. However, referring to the limited research conducted in the field of AR and infrastructure planning, the study presents valuable results to gain a deeper understanding of the value of AR. Further, the study is based on an available commercial app. While this approach proved to be sufficient to evaluate the perceived value of AR among practitioners, a self-contained prototypical development allows to design an app particularly addressing the needs of stakeholders in the wastewater sector. The high dependency on reliable hard- and software components as well as network coverage of internet and GPS data might challenge the general use of AR especially in rural areas, but did not show to be interfering our study.

Furthermore, the implementation process of the AR scenario needs to be discussed. The development was based on a close collaboration of an interdisciplinary team. While the researchers were compiling the AR scenario for the study, the responsible urban municipality had to provide the data as they are not publicity available. Involved planning offices supported the researchers to prepare and incorporate data into the AR app. The broad range of co-authors as well institutions mentioned in the acknowledgements reflects that. Additionally, the implementation required a collaboration with the app’s developers providing support for improving the functionality and the user’s experience. Hence, it can be argued—given, that the selected app is one of the leading in its field—that AR is not a mature technology yet. However, with respect to conducted previous studies (Söbke et al. 2018), the researchers observed a significant progress in terms of the implementation of AR scenarios, user experience as well as technical features and functions. Therefore, it may be just a matter of time before these applications will be marketable and be widely used for infrastructure planning.

5 Conclusions

The recent years brought the rise of metaverse(s) and AR as one of its key technologies in urban planning. While in some areas such as the early participation of the public, have been intensively explored, in the field of technical infrastructure development and particularly with the focus on wastewater infrastructure planning the metaverse is still in its initial stage. So far, only few apps exist mainly addressing aspects of construction and of maintenance. The conducted study seeks to foster the implementation of AR in the wastewater sector leading to better planning results. Based on a designed AR scenario, a mixed-methods approach was used consisting of quantitative and qualitative tools to evaluate the practical value of AR for wastewater infrastructure planning. Emphasising three focus points—significance for wastewater infrastructure planning, user experience and technical aspects—the general results show a substantial value of AR bearing multiple advantages to enhance planning processes. Nevertheless, some areas, such as the user interface as well as the technical features and functions, offer potential for improvements. Within the scope of the rapid technical development, it can be expected that the experienced weaknesses will be overcome, and additional valuable affordances will emerge in the coming years. This will transform metaverse(s) and particularly AR from a term appearing in literature initially in the early 1990s (Milgram et al. 1995) into a standard technology for urban planners.