Gravel road condition assessment is crucial for effective infrastructure maintenance. Visual windshield surveys, the traditional assessment method, are often inefficient and subjective, leading to inconsistencies in maintenance decisions. This paper presents the preliminary results of a study that tests different methods for assessing the condition of gravel roads and explores the possibility of integrating these assessment methods for improved effectiveness. Field experiments were conducted on a gravel road directly after maintenance and several months later, after corrugations had formed. Visual assessments and the International Roughness Index (IRI) measurements were performed using the Roadroid smartphone application. At the same time, the vehicle vibration response data was collected using an Integrated Electronics Piezo-Electric (IEPE) accelerometer. The findings demonstrate a correlation between higher IRI values, increased vehicle vibration levels and the presence of corrugations. Analysing vehicle vibration responses in different frequency sub-bands also provided insights into specific road defects, such as loose gravel and corrugations. Integrating these data sources provides a more accurate and detailed insight into the gravel road conditions that would enable targeted maintenance interventions. There is potential to incorporate artificial intelligence (AI) for automated road condition assessment and predictive maintenance, for instance, through a machine learning model. The model trained on the collected data would then assess the gravel road condition and predict future deterioration, thus optimising maintenance resource allocation and improving efficiency and cost-effectiveness in managing gravel roads.

As artificial intelligence (AI) advances across domains, its adoption in high-stakes environments such as industrial predictive maintenance requires trust, transparency, and usability. Ensuring AI-driven decisions are explainable and user-centered is essential for real-world deployment. While existing AI frameworks offer valuable principles and tools, integrating them effectively into development practices needs further development. This study evaluates two established AI frameworks, Schneiderman’s Human-Centered AI (HCAI) and DARPA’s Explainable AI (XAI), assessing their suitability for predictive maintenance from a user-centered perspective. HCAI provides valuable principles for human-AI collaboration, trust, and ethical oversight but requires more structured implementation guidance. Conversely, XAI offers robust explainability techniques but does not fully address iterative refinements, participatory design, or structured governance. To address these limitations, this paper presents the Methodological Human-Centered Agile Framework for AI-XAI Development, integrating HCAI and XAI principles into a structured methodology. Rather than treating these frameworks as separate, the proposed approach enhances their integration by combining structured governance, business understanding, data-driven insights, and Agile’s iterative refinements. This ensures AI development aligns with industrial needs, regulatory compliance, and evolving operational demands. By embedding continuous user feedback, participatory design elements, and fairness evaluations, this framework provides a systematic process for integrating explainability and human-centered principles into AI systems. Beyond predictive maintenance, it offers a scalable, governance-ready approach for embedding explainable and ethical AI into industrial systems. Future research should explore how this framework can be refined for broader applications, including its adaptability across various industries and sustained mechanisms for explainability and user trust.

Current trends in digitalization and servitization are transforming business models across all industrial sectors, including infrastructuremaintenance. This paper explores how business model innovation in the gravel road maintenance sector can capitalize on the opportunitiespresented by these trends. Data were collected in a single case study through in-depth interviews, site observations, and open dialogues andworkshops. Four different business models for gravel road maintenance services are proposed. The first two business models are variants oftraditional contracting, involving the execution of maintenance according to predefined requirement specifications for either private or publiccustomers. Business model 3 is based on functional contracting, focusing on performance-based contracts. Business model 4 introduces aplatform-based business model, where the supplier provides a platform and network for offering maintenance services. The opportunities andprerequisites for each business model are discussed, particularly in relation to digitalization needs. It is concluded that the levels of strategicthinking, degree of digitalization, types of competence needs, and collaboration typically increase across the business models. Additionally, thedigital maturity of both the service contractor and the customers is identified as a key factor for successful implementation.

The steady growth of multimodal data requires effective methods for managing and processing this type of information, including focusing on developing a system for detecting anomalies. This research mainly focuses on detecting anomalies and improving data quality in multimodal datasets. By integrating different data types, such as text, metadata, and images, this research examines the effectiveness of three fusion methods: early, late, and hybrid. Through a comprehensive analysis, the study demonstrates that late fusion significantly improves the accuracy of data processing and achieves the highest processing accuracy of 0.947, which is, on average, 6% higher than the early and hybrid fusion systems. The findings emphasize the potential of multimodal data systems to improve information systems' reliability, accuracy, and efficiency, thereby supporting more informed decision-making in dynamic environments.

This study examines the role of digital capabilities in facilitating organizational transformation within the maintenance domain, focusing on the need for transitioning from Industry 4.0 to Industry 5.0. The evolving concept of Industry 5.0 complements Industry 4.0 by shifting the focus from automation to human-machine collaboration. Industry 5.0 aims to create a more cooperative relationship between humans and machines to maximize productivity and innovation. To facilitate digital transformation, companies need to develop suitable digital capabilities. Through interviews with maintenance specialists and practitioners, we identified key challenges and facilitators to implementing emerging technologies. Key findings highlight the importance of providing high-quality education, aligning digital tools with work tasks, and offering user-friendly digital solutions. Furthermore, the findings emphasize the need for robust information sharing, cooperation, and the formalization of responsibilities between maintenance and production functions. Lastly, establishing a clear digital strategy, commitment to investments, and testing new technologies are crucial for facilitating digital transformation and promoting a culture of innovation.

This article underscores the importance of integrating machine learning analytics to enhance preventive maintenance methodologies, particularly emphasizing condition-based maintenance (CBM) within the wind energy domain. Through empirical evidence derived from wind turbine data, the paper outlines the efficacy and applicability of Machine Learning in Operations (MLOps) forpredicting the residual operational life of wind turbine bearings. While the study's principal domain is renewable energy, especially wind power, it employs a specific wind turbine dataset for exhaustivemodel testing, leading to the proposition of an innovative ensemble model tailored for high-speedwind turbine bearing prognosis. The introduced model, "The Data Interpretation Algorithm for Forecasting Time Series" (DIAFS), crafted for assessing wind turbine bearing conditions, is predicated on an adaptive polynomial model approximation. It emerges as an indispensable asset for maintenance professionals implementing CBM methodologies

Gravel roads form a significant share of the global road network, usually in sparsely populated rural areas. They are important, especially in agriculture, tourism, and forestry, connecting rural to urban areas. This systematic literature study comprises 105 reviewed publications on gravel road maintenance. Review articles on maintenance management practices, especially concerning objective condition assessment and data-driven methods (DDMs), are lacking. Therefore, this review provides a concise overview of current gravel road maintenance practices and ongoing research on objective condition assessment and DDMs for gravel road maintenance. It offers researchers in gravel road maintenance and other related fields a clear indication of where to focus their research efforts, as it suggests the direction for future research. Visual assessment methods are predominant for monitoring the condition of gravel roads, while objective methods and DDMs are not common. Research on gravel roads and their maintenance has increased in the last two decades, especially in North America and Northern Europe. Condition assessment is shifting from subjective to objective methods, utilizing knowledge from technological advancements in image processing, vibration and acoustics analysis, and so forth. There are some excellent research initiatives for objectively assessing the condition of gravel roads and DDMs, but the practical implementation is limited. Implementing objective assessment methods and DDMs generally improves the management of gravel roads with regard to decision-making, maintenance costs, safety, and the stability and comfort of the ride. Objective condition assessment and DMs have the potential to enhance maintenance practices in the maintenance of gravel roads.

The efficient maintenance of gravel roads depends on digital tools and data as well as information systems adapted for specific needs while conforming with current standards for better interconnectivity. Within gravel road maintenance, cloud-based solutions are a key alternative. This because necessary information, such as road condition data, could be shared throughout the network and provide the opportunity to coordinate maintenance activities between stakeholders. Effective standards are essential since they permit various services with the ability to work together while at the same time supporting differences that ease competition and innovation. Among several maintenance standards, the OSA-CBM and MIMOSA CRIS database schema are intended for handling information exchange and communication within and between systems supporting condition-based maintenance. This paper tests the usability of these standards for translating context-dependent information into a standardized data set. The domain studied is gravel road maintenance. Critical data was gathered and elicited from different stakeholders and represented in the form of a conceptual information model. This paper exemplifies how the conceptual model could be mapped into a logical model with respect to the CRIS information model. The main results are in the form of a conceptual and logical information model for purposes of gravel road maintenance, as well as a procedure suggested for mapping the information model following specific steps. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The gravel road network is an important function for rural residents and entrepreneurs. Traditional maintenance of gravel roads is well-functioning but provides a relatively high maintenance cost per unit length of the road, and every maintenance action as well as extraction and transport of new gravel contributes to increased climate impact and resource depletion. Today, maintenance planning is carried out periodically based on the maintenance history, which also is reflected in the economic models and procurement methods. Current maintenance plans may be enhanced and will not be a reliable basis in the future, e.g. due to climate change. Instead, real needs and conditions must be given greater consideration. Today, appropriate maintenance management systems are lacking, e.g. in order to be able to evaluate maintenance deficiencies, prioritize objects and choose the appropriate maintenance action. Moreover, the knowledge available at specific stakeholders is not shared with other actors. In this paper, an Information and Communication Technology (ICT) system for gravel road maintenance is proposed in the form of a cloud-based system covering the information needs of stakeholders in the gravel road maintenance ecosystem. Requirement specifications are given for the sub-systems intended for the maintenance executioner and the maintenance planner. The specifications are based on workshops and interviews conducted with stakeholders, where requirements were acquired e.g. in the form of User stories.

The current article highlights the specific challenges and issues of the healthcare system in Europe. In addition, the particular factors towards the digitalization of the domain are highlighted, and the emerging technologies contribute to this process because many things are becoming more feasible. Thus, information and communication technologies (ICTs), such as new sensors, machine learning, big data, and analytics, provide new opportunities and challenges in their implementation and use. Therefore, it has become crucial to understand the different kinds of ICTs, such as artificial intelligence (A.I) techniques, especially machine learning algorithms and their use in the domain of interest. Thus, the paper aims to understand the mentioned technologies and their implementation in the area of interest to comprehend their current status, their suitability, and what needs to be considered for their successful development and implementation. While at the same time taking into account several key aspects that need to be well-thought-out in the domain. Consequently, the author performs a conceptual literature review of relevant scientific articles where sensors, machine learning, data mining, statistical learning, etc., have been tested and utilized in the eHealth area, especially for fall prevention and fall risk assessment. Finally, the literature findings are discussed, and the factors to consider when applying machine learning for fall prevention and fall risk assessment are underscored. © 2022 Copyright for this paper by its authors.