City infrastructure maintenance is a complex task, often hindered by limited resources and the vastness of the networks involved. Roads, bridges, utilities, and public facilities all require regular upkeep, but traditional methods are often inefficient and prone to human error. This can lead to increased costs, unexpected failures, and prolonged downtimes, significantly impacting the quality of life for urban residents and the economic vitality of cities. AMS offers a solution to these challenges.
Autonomous Maintenance Software (AMS) offers a transformative solution by automating the monitoring, diagnosis, and repair processes of urban infrastructure. This software, with its advanced sensors, artificial intelligence (AI), and the Internet of Things (IoT), takes on the heavy burden of continuous data collection and analysis from various infrastructure components. By integrating these technologies, AMS can detect anomalies, predict potential failures, and initiate maintenance actions without human intervention. The system can be applied to a range of urban assets, from water and sewage systems to transportation networks and energy grids, providing a reassuring future for our cities.
The operational mechanism of AMS involves deploying sensors across infrastructure networks to gather real-time data on their condition. These sensors transmit information to a centralised AI-driven platform that processes the data, identifying patterns and irregularities that indicate wear, damage, or impending malfunctions. Once a potential issue is detected, the software can either alert maintenance crews or, in more advanced setups, trigger automated repair mechanisms such as robotic devices. This proactive approach ensures that maintenance is performed precisely when and where needed, reducing downtime and extending the lifespan of urban infrastructure.
As urban populations grow, the demand for infrastructure increases, making efficient maintenance even more crucial. AMS enhances cities' resilience and sustainability by preventing catastrophic failures, optimising resource allocation, and minimising disruptions. Moreover, it supports the shift towards smart cities, where technology is seamlessly integrated into urban management to improve residents' overall quality of life.
An AI company that uses smartphone data to assess road conditions, now part of Michelin DDi.
Provides AI-driven predictive maintenance for civil infrastructure like bridges and tunnels.
A robotics and R&D company serving the energy and utility industries.
A global leader in weather, environmental, and industrial measurements.
Provides watsonx.governance for managing AI risk and compliance.
IoT platform for connected operations, using AI to predict vehicle maintenance needs and improve fleet safety.
Offers the Digital Annealer, a quantum-inspired architecture specifically built to solve large-scale combinatorial optimization problems.
Article
Smart City Infrastructure Maintenance: IoT, Sensors, and Connected Public AssetsOxmaint · Feb 26, 2026
Integrated Smart City IoT and sensor networks allow public works to monitor city pulse in real-time, feeding telemetry into CMMS for automated maintenance work orders the moment anomalies are detected.
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Oxmaint AI for Railways Infrastructure MaintenanceOxmaint · Feb 21, 2026
AI defect detection and digital twin risk scoring enable automated CMMS work order generation for railway infrastructure, identifying defects like fatigue cracks months before failure.
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AssetAI® – SmartNSW case studyNSW Government · Oct 15, 2025
AssetAI uses sensors and cameras on council vehicles combined with AI analysis to streamline road maintenance and improve safety by using near-real-time data to find and fix problems faster.
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How AI could help stretch the life of industrial equipmentIBM Research · Jul 2, 2025
IBM integrates AI agents into Maximo Application Suite to enable condition-based maintenance, allowing operators to interact directly with AI for efficient asset management and moving away from fixed schedules.
Report
Railway Infrastructure Maintenance Services Market Outlook 2025-2034GlobeNewswire · Jun 24, 2025
The market for railway maintenance is growing, driven by investments in smart infrastructure solutions such as autonomous inspection systems and AI to enhance operational efficiency.
Article
TOWARDS AN AUTONOMOUS MANAGEMENT MAINTENANCE MODEL APPLIED TO A HERITAGE BUILDING: THE CASE OF HERNANDO COLÓN COLLEGE, UNIVERSIDAD DE SEVILLA, SPAINwitpress.com
Maintenance in buildings is crucial to assure a proper use during their lifespan. However, both unqualified managers and a lack of commitment to develop a maintenance management plan is a challenge to establish Total Productive Maintenance (TPM) and European Foundation for Quality Management (EFQM) models. Moreover, if a building comprises heritage special requirements, specific actions must be considered. In this context, this research proposes a maintenance methodology by unqualified managers to facilitate decision-making for qualified agents. For this purpose, process management provides the bases for an autonomous management maintenance model characterisation and its application to historic buildings. Hernando Colon College (CMHC by its abbreviation in Spanish) of the University of Seville is a superb example of a heritage building with maintenance data and track record in process management approach, which supposes a starting point to apply the proposed methodology. The model has been implemented in the CMHC since 2015. The results showed that the maintenance optimisation reduced the number of corrective maintenance actions by 13% in comparison with the preventive actions. The results also indicated that the total maintenance actions were reduced by 37%. This study demonstrates, by collecting data based on quantitative measures, that it is possible to apply an autonomous maintenance management model in a historic building in use.
Article
Self-Repairing Cities: How AI is Streamlining Maintenance Operations at Scalemedium.com
Headquartered in Singapore, H3 Dynamics’ customers span the public and private sector, facilities management firms, site operators, safety inspection and certification organizations, urban planners and architects, and regulators involved in legislating safety mandates.
Article
A Review on Technologies for Localisation and Navigation in Autonomous Railway Maintenance Systemsncbi.nlm.nih.gov
Smart maintenance is essential to achieving a safe and reliable railway, but traditional maintenance deployment is costly and heavily human-involved. Ineffective job execution or failure in preventive maintenance can lead to railway service disruption and unsafe operations. The deployment of robotic and autonomous systems was proposed to conduct these maintenance tasks with higher accuracy and reliability. In order for these systems to be capable of detecting rail flaws along millions of mileages they must register their location with higher accuracy. A prerequisite of an autonomous vehicle is its possessing a high degree of accuracy in terms of its positional awareness. This paper first reviews the importance and demands of preventive maintenance in railway networks and the related techniques. Furthermore, this paper investigates the strategies, techniques, architecture, and references used by different systems to resolve the location along the railway network. Additionally, this paper discusses the advantages and applicability of on-board-based and infrastructure-based sensing, respectively. Finally, this paper analyses the uncertainties which contribute to a vehicle’s position error and influence on positioning accuracy and reliability with corresponding technique solutions. This study therefore provides an overall direction for the development of further autonomous track-based system designs and methods to deal with the challenges faced in the railway network.
Article
Predictive and preventive maintenance in smart citiesdac.digital
Equipment failure is inevitable, especially in systems that are heavily exploited. It is imperative to develop an appropriate maintenance strategy. Companies spend an average of 80% of their time reacting to maintenance issues rather than preventing them. How can predictive and preventive maintenance help in smart cities?
Article
Autonomous Maintenance Technology Literature Reviewresearchgate.net
The objective of this report is to summarize the state-of-the-art, as well as the state-of-the-practice, of the autonomous maintenance technology development. This report is structured around the following three particular topics of interest. A brief review of mobile and slow-moving operation technology, from the academic research side, is provided in Section 2. In Section 3, we focus on reviewing the Federal and State regulations for autonomous vehicles, and on analyzing how the ATMA vehicle system aligns with these regulations. Section 4 summarizes progress from the AMT Pool Fund, with emphasis on the DOT deployment status and the sponsored projects status. The purpose of this report is to work with key stakeholders from the public and private sectors who are working on autonomous maintenance technology, and to summarize and document knowledge for the purpose of autonomous maintenance technology promotion and deployment.
