Journal:Data and information systems management for urban water infrastructure condition assessment

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Full article title Data and information systems management for urban water infrastructure condition assessment
Journal Frontiers in Water
Author(s) Carriço, Nelson; Ferreira, Bruno
Author affiliation(s) Polytechnic Institute of Setúbal
Primary contact Email: nelson dot carrico at estbarreiro dot ips dot pt
Editors Vladeanu, Greta
Year published 2021
Volume and issue 3
Article # 670550
DOI 10.3389/frwa.2021.670550
ISSN 2624-9375
Distribution license Creative Commons Attribution 4.0 International
Download (PDF)


Most of the urban water infrastructure around the world was built several decades ago and nowadays they are deteriorated. As such, the assets that constitute these infrastructures need to be updated or replaced. Since most of the assets are buried, water utilities face the challenge of deciding where, when, and how to update or replace those assets. Condition assessment is a vital component of any planned update and replacement activities and is mostly based on the data collected from the managed networks. This collected data needs to be organized and managed in order to be transformed into useful information. Nonetheless, the large amount of assets and data involved makes data and information management a challenging task for water utilities, especially those with as lower digital maturity level. This paper highlights the importance of data and information systems' management for urban water infrastructure condition assessment based on the authors' experiences.

Keywords: condition assessment, data, information systems, management, urban water infrastructure


Urban water infrastructures are constituted by a large variety of physical assets (e.g., tanks, pumps, pipes). These assets deteriorate due to their natural aging and non-controlled processes (e.g., pipe's poor production quality, external actions such as excavation) and, as a result, need to be updated or replaced (i.e., "rehabilitated) to improve the assets' condition to its “as-new” condition if practicable.[1][2] By extension, it is vital to then assess the physical condition and functionality of urban water infrastructures in order to estimate the remaining service life and asset value.[3] Since urban water infrastructure assets are mostly buried, the assessment of their condition must be based on reliable pre-acquired information.

Over the past few decades, water utilities have made significant investments in implementing different information systems to address the increasing complexity of the daily control, operation, management, and planning of their systems.[4] Data is usually collected, stored, managed, and analyzed using various information systems, which are often dispersed across different divisions of a water utility. In particular, the activity of condition assessment is data intensive and uses a plethora of information systems.[5]

This paper highlights the importance of data and information systems management for urban water infrastructure condition assessment based on the authors' experiences obtained from some Portuguese R&D projects. These projects aimed to develop a platform to assist small- and medium-sized water utilities with their low digitalization maturity level to better integrate data from their different existing information systems to assess the condition of their water distribution systems.

Urban water infrastructure condition assessment

"Condition assessment" may be defined as the identification of the likelihood that an asset will continue to perform its required function[6] and is an essential part of any urban water infrastructure asset management (IAM) processes and methodologies.[2][7][8][9][10][11][12][13][14] Further, ISO 55000 defines "asset management" as a coordinated activity carried by an organization to realize value from its assets involving a balancing of costs, risks, opportunities, and performance benefits.[15] As such, we can say that condition assessment may involve risk, performance, and cost methodologies.

The most common risk management frameworks used are in accordance with the reference standards AS/NZS 4360:2004[16] and ISO 31000:2018.[17] The frameworks address topics such as:

  • risk assessment, the processes of risk identification, risk analysis, and risk evaluation[18];
  • performance assessment system, a set of data, calculations, performance metrics, and contextual information that allow the evaluation and reporting of the performance of a single asset, a whole infrastructure, a provided service, or a utility[19][7]; and
  • cost assessment, which evaluates the deterioration of an infrastructure and the quantification of the investment needed for its rehabilitation, as well as the total cost for comparison of different alternatives of rehabilitation actions.

In 2020 research published by Carriço et al.[20], five Portuguese water utilities defined a set of 16 performance indicators (Table 1) aimed towards the assessment and prioritization of water supply systems (WSS) or district metering areas (DMA) for rehabilitation. These performance indicators were regarded as having the utmost importance and were implemented in a platform allowing its calculation after the integration of the required data.[20]

Tab1 Carriço FrontWater2021 3.jpg

Table 1. Set of performance indicators to assess and prioritize water supply systems (WSS) or district metering areas (DMA).

The calculation of these performance indicators relies in a deep knowledge of managed systems and their respective assets. This knowledge is provided by data that is collected and stored in different databases, generally spread across various departments or divisions from the water utility. For instance, pipe failures may come from a service work order register, whilst real water losses in network may come from a water balance calculation. Therefore, the infrastructure asset manager has a challenging task every time he needs to assess their systems since data must be collected from different stakeholders in a coordinated manner.[20]

Data requirements for condition assessment

The basic data needed to assess an urban water infrastructure asset's condition may be categorized into four main groups:

  1. Cadastral mapping: Detailed information of the system's different assets, including identification, type, location, dimensions, shape, material, etc.
  2. Functional and physical condition: Studies about hydraulic behaviors, such as mathematical modeling and hydraulic measurements and performance assessment reports (e.g., containing assessment period, performance indicators used, and targets considered), etc.
  3. Operational and maintenance condition: Documention that includes data on asset maintenance activities (e.g., dates, locations), failure reports, data complaints (e.g., low pressure), etc.
  4. Billing and account information: Data concerning network-related maintenance and operational costs, energy and personnel costs, amortizations and interest, revenues, etc.

In this regard, data collection thus is an activity that significantly consumes human, technological, and financial resources. However, important data is often neither collected nor recorded, or the available data does not respond to the needs of or does not fulfill the requirements for the different stakeholders. In some cases, water utility technicians are required to collect too much data, and as a result some data is simply not captured and registered due to personnel's lack of time. For example, the authors have analyzed the service work orders of a few Portuguese water utilities and found that they usually have up to 200 possible data fields regarding each work order. Nonetheless, most of these data fields (i.e., around 90%) were simply null as they were left blank by personnel. For that reason, the water utility should firstly identify the necessary information requirements to fulfill its objectives (in which one is an asset's condition assessment) in order to rationalize the use of resources and maximize data collection effectiveness.

These data are then stored and used in the water utility's activities using one or more information systems such as:

Figure 1 depicts a possible scheme for the data requirements and information fluxes for condition assessment.

Fig1 Carriço FrontWater2021 3.jpg

Figure 1. Data requirements and information fluxes for condition assessment.

As the large amount of assets and data involved makes data and information management a challenging task, new tools and processes are often needed to collect, gather, manage, analyze, and use asset data. Creating and using these tools can stimulate and improve knowledge and decision making within organizations, ultimately improving utilities' information management.[21]

Data integration and interoperability

The assessment of an asset's condition is carried out by pooling data and information from the different existing information systems at the utility. A way to pool such data and information is through the use of systems integration and/or interoperability. Data integration refers to the connection of different information systems so that data from one system can be accessed by another. As such, data integration usually involves a third software solution called middleware that translates the data and associated communication methods, making it readable for the receiving system. Data integration is important to enabling cross-functional processes as well as providing quality analysis of asset condition assessment. Interoperability refers to the ability of an information system to connect and communicate with another information system, even if they were created by different developers. When systems are interoperable, they can not only share information, but they also can interpret the data received and present it in its original form. Interoperability not only brings together various systems but also contributes to functional and organizational integration.[5] Figure 2 exemplifies data interoperability and integration in the context of condition assessment.

Fig2 Carriço FrontWater2021 3.jpg

Figure 2. Data integration and interoperability in the context of condition assessment.

Water utilities with a greater digitalization maturity level may have the required resources to develop their own data integration and interoperability solutions. These solutions integrate data gathered from different sources (e.g., sensors, GIS, equipment automation signals), as well as results from hydraulic simulation models, aiming ultimately at predictive models of network hydraulic behavior and decision support tools.[22] Some water utilities choose to integrate data through a GIS-driven solution, in which it is possible to easily access data related to infrastructures, fleets, water demands, billing, and more.[23][24] Nonetheless, the use of these solutions is restricted to the utility itself, and most water utilities do not have the necessary resources to develop their own solutions. Different commercial software (e.g., Baseform, WaterSmart) claim to provide comprehensive, actionable analytics to assist water utilities with specific efficiency issues and key business outcomes.[20] However, the cost of such solutions is usually a barrier to many water utilities.

Data and information management barriers and recommendations

The information management efficiency of water utilities often falls short of desired results. This is particularly prominent in municipalities since management is usually structured according to local government models. Due to lack of technical capacity and human resources, it is not usually possible to generate the critical information through daily routines (e.g., well-planned and organized service orders that can ensure useful, good-quality information). The most frequent problems in information management found by the authors in Portuguese water utilities include:

  • significant diversity among existing information systems;
  • lack of information system integration and/or interoperability;
  • existence of several legacy systems that require updating or replacement;
  • strong competition among information management systems;
  • lack of strategic vision towards the technological environment of the water utility;
  • limited and irregular use of information systems by utility workers;
  • poor data quality, including lack of consistency, duplication, and outdatedness;
  • lack of support and recognition from the management of the water utility for the importance of information management;
  • limited resources to deploy, manage, or improve information systems;
  • difficulty in altering workers' processes and workflows; and
  • lack of coordination or communication among the various users of information within the utility.

However, there are several practices and solutions that may reduce or limit those problems. First, data stored in the various information systems could be standardized in order to make them more compatible with each other. Additionally, utilities could improve the quality of collected data by periodically reviewing the data model, eliminating duplicated data, and cleaning out the outdated data. Other potential solutions include the optimization of information processes and workflows; guaranteeing the protection and security of data using information systems in accordance with the General Data Protection Regulation (GDPR) and other data protection regulations; and creating a data recovery strategy, as it is common to both make mistakes when entering data and to delete data inadvertently.

Future trends

The fourth industrial revolution has brought more sophisticated solutions to urban water infrastructure asset management. For example, the internet of things (IoT) provides opportunities for water utilities to have smart water metering systems that measure and monitor their networks in real-time. Artificial intelligence (AI) solutions give the water industry the power to leverage collected data to create more effective and intelligent water management decisions and operations. Augmented and virtual realities (AR/AV) may be able to assist water utilities in their daily operations, management activities, and training efforts by providing better ways of data and information visualization. Additionally, "digital twins" will make it possible to build what-if scenarios and incur suitable outcomes throughout an asset's lifecycle, allowing better planning and training activities.

However, these promising technologies are not ready-to-use by most water utilities, especially in those with lower digitalization maturity levels. Notwithstanding the fact that a large sum of capital investment is needed to implement any such technology, these water utilities face some barriers that need to be firstly solved. Such barriers often include the lack of integration and interoperability of existing information systems, resistance of human resources to change their cultural mindset and work habits, lack of infrastructure (e.g., access to wi-fi, power source), and weak cybersecurity policy, amongst others.

Final remarks

The large amount of assets and data involved, coupled with the lack of human and financial resources, makes data and information management a challenging task for water utilities. This challenge is greater in those utilities with a lower digital maturity level. New tools and processes in information systems are being developed, motivated by the growing need to make water utilities more resilient and flexible, with greater transparency and rationality in the decision-making process, as well as to respond to challenges more quickly and efficiently. The development and use of these tools can be a stimulative process to water utilities, further improving the knowledge within the organization and decision-making processes.

However, many small and medium size utilities worldwide are yet resistant to progress to the digital era initiated by the third industrial revolution, which in turn brings difficulties in moving towards the automation era initiated by the fourth industrial revolution. These utilities should start to investigate the data they collect and to rethink existing data models. These models should be simplified and standardized between them in order to avoid data duplication and to improve data quality.


The authors acknowledge the Fundação para a Ciência e a Tecnologia for supporting this research, the project's team, and participating water utilities for their contribution.

Author contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.


This paper was developed under the WISDom project, supported by the Fundação para a Ciência e a Tecnologia (Grant Number: DSAIPA/DS/0089/2018) through the Data Science and Artificial Intelligence in Public Administration Programme.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


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This presentation is faithful to the original, with only a few minor changes to presentation, though grammar and word usage was substantially updated for improved readability. In some cases important information was missing from the references, and that information was added. The original article lists references alphabetically, but this version—by design—lists them in order of appearance.