Gravel roads are essential for economic development as they facilitate the movement of people, transportation of goods and services, and promote cultural and social development. They typically connect sparsely populated rural areas to urban centres, providing essential access for residents and entrepreneurs. Maintaining these roads to an acceptable level of service is crucial for the efficient and safe transportation of goods and services. However, substantial maintenance investmentis required, yet resources are limited. Gravel roads are prone to dust, potholes, corrugations, rutting and loose gravel. They deteriorate faster than paved roads, and their failure development is affected by traffic action and physical, geometric and climatic factors. Thus, more condition monitoring and proper road condition assessment are necessary for dynamic maintenance planning to reach efficiency and effectiveness using objective, data-driven condition assessment methods to ensure all-year-round access.

However, objective data-driven methods (DDMs) are not frequently used for gravel road condition assessment, and where they have been applied, the practical implementation is limited. Instead, visual windshield assessment and manual methods are predominant. Visual assessments are unreliable and susceptible to human judgement errors, while manual methods are time-consuming and labour-intensive. Maintenance activities are predetermined despite dynamic maintenance needs, and the planning is based on historical failure data rather than the actual road condition. This thesis establishes a data-driven approach to gravel road maintenance describing the systematic assessment of the gravel road condition and collection of the condition data to ensure efficient and effective maintenance planning. This thesis uses a design research methodology based on a literature review, concept development, interview study and field experiments.

A holistic approach is proposed for data-driven maintenance of gravel roads encompassing objective condition data collection, processing, analysing, and interpreting the findings for obtaining reliable information concerning the condition to gravel road decision support by utilising the opportunities presented by technological advancements, particularly sensor technology. Then, decision-making is primarily influenced by the objectively collected gravel road condition data rather than the evaluator’s perception or experience. The successful implementation of a data-driven approach depends on the quality of the collected data; therefore, data relevance and quality are emphasised in this thesis. The lack of data quality and relevance hinders effective data utilisation, leading to less precisionin decision-making and ineffective decisions.

Furthermore, the thesis proposes a participatory data-driven approach for unpaved road condition monitoring, allowing road users to be part of the maintenance process and providing an efficient and effective alternative for collecting road condition data and accomplishing broad coverage at minimum cost. A top-down iiapproach for data-driven gravel road condition classification is proposed to achieve an objective assessment to address the lack of readily available quality and relevant condition data. The established data-driven approach to gravel road maintenance is evaluated and verified with field experiments on three gravel roads in Växjö municipality, Southern Sweden. The research findings indicate that properly implementing a data-driven approach to gravel road maintenance would ensure efficient and effective condition assessment and classification, which are a basis for a maintenance management system of gravel roads and enable road maintainers and authorities to achieve cost-effective decision-making. 

Gravel roads play a vital role in transportation, particularly in rural and remote regions. They support essential sectors such as agriculture, forestry, tourism, and emergency services and can serve as access routes to remote natural resources or large onshore windmill farms. However, they deteriorate rapidly and rely on subjective, inconsistent and labour-intensive visual and manual condition assessment, often resulting in delayed maintenance interventions and costly maintenance actions. This dissertation addresses the need for improved condition assessment of gravel roads by integrating participatory approaches with objective data-driven methods. Overcoming the limitations of traditional assessment practices would enable broader network coverage, enhance the efficiency and effectiveness of defect detection, and support sustainable maintenance planning and informed decision-making.

The research is guided by Design Research Methodology, synthesising the six appended interrelated studies. These studies progress from problem identification and conceptual development to a case study and empirical field experiments to answer the three research questions (RQs): (1) How can objective data-driven approaches address the limitations of traditional approaches to enable efficient and effective assessment of gravel road conditions? (2) How can data-driven approaches and multi-source information enable reliable assessment of gravel road conditions and support maintenance planning and decision-making? (3) How can participatory and adaptive gravel road condition data collection enable sustainable condition assessment to support predictive and data-driven maintenance planning and decision-making?

Vehicle vibration response data (VVR) collected using Integrated Electronics Piezo-Electric accelerometers and smartphone sensors are analysed using statistical, spectral, and subband filtering techniques. The International Roughness Index (IRI) measurements, as well as videos and images of the gravel road condition, are also collected from Roadroid, an Android-based smartphone application, to complement the VVR and verify the actual defects on the gravel roads. A rule-based classification approach is developed and empirically validated using the collected field experiment data, enabling the detection of surface defects such as potholes, corrugations, and loose gravel. Multi-sensor integration and the sequential probability ratio test further enhance the robustness of the objective fault detection and condition classification for gravel roads. The findings from the Roadroid IRI measurements led to the refining of IRI thresholds for a four-class condition classification system.

The research also proposes a participatory and adaptive approach to collecting condition data. By involving regular gravel road users in collecting gravel road condition data, the proposed participatory data-driven approach to assessing gravel road conditions would increase the spatial coverage of the assessed gravel roads, which are mainly located in remote areas. At the same time, adaptive sampling optimises the data that is captured and stored without compromising accuracy, thus enabling more reliable, objective and sustainable gravel road condition assessments that would support predictive maintenance planning.

In conclusion, the dissertation demonstrates how objective, data-driven methods and the participatory approach can complement and gradually replace traditional subjective assessments, providing gravel road associations, municipalities, and road agencies with condition assessment approaches that enable broader assessment coverage, are efficient and effective, and are socially inclusive. By combining multi-source sensor data with stakeholder engagement, the research lays the foundation for sustainable, predictive, proactive and resilient gravel road management systems. It ultimately contributes to theory, method and practice in gravel road asset management.

Grusvägar spelar en avgörande roll i transportsystemet, särskilt i landsbygds och glesbygdsområden. De stödjer viktiga samhällssektorer såsom jordbruk, skogsbruk, turism och räddningstjänst och kan även fungera som tillfartsvägar till avlägsna naturresurser eller stora landbaserade vindkraftsparker. Samtidigt försämras grusvägar snabbt och deras tillstånd bedöms i stor utsträckning genom subjektiva, inkonsekventa och arbetsintensiva visuella och manuella metoder, vilket ofta leder till fördröjda underhållsinsatser och kostsamma åtgärder. Mot denna bakgrund adresserar denna avhandling behovet av förbättrad tillståndsbedömning av grusvägar genom att integrera deltagarbaserade angreppssätt med objektiva, datadrivna metoder. Genom att hantera begränsningarna i traditionella bedömningsmetoder möjliggörs en bredare vägnätverkstäckning, ökad effektivitet och precision i skadedetektering samt stöd för hållbar underhållsplanering och välgrundade beslut.

Forskningen vägleds av Design Research Methodology och syntetiserar de sexstudierna som ingår i avhandlingen. Studiernaomfattar problemidentifiering och konceptuell utveckling till fallstudier och empiriska fältexperiment för att besvara tre forskningsfrågor (RQ): (1) Hur kan objektiva, datadrivna angreppssätt hantera begränsningarna i traditionella metoder för att möjliggöra en effektiv och ändamålsenlig bedömning av grusvägars tillstånd? (2) Hur kan datadrivna metoder och information från flera källor möjliggöra en tillförlitlig tillståndsbedömning av grusvägar och stödja underhållsplanering och beslutsfattande? (3) Hur kan deltagarbaserad och adaptiv insamling av data om grusvägars tillstånd möjliggöra en hållbar tillståndsbedömning som stödjer prediktiv och datadriven underhållsplanering och beslutsfattande?

Data om fordonets vibrationsrespons (VVR), insamlade med hjälp av piezoelektriska accelerometrar och smarttelefonsensorer, analyseras med hjälp av statistiska metoder, spektralanalys och subbandsfiltreringstekniker. International Roughness Index (IRI)-värdensamt videor och bilder av grusvägarnas tillstånd samlas också in med Roadroid, en Android-baserad smarttelefonapplikation, för att komplettera VVR-data och verifiera faktiska skador på grusvägarna. En regelbaserad klassificeringsmetod utvecklas och valideras empiriskt med hjälp av insamlade fältexperimentdata, vilket möjliggör detektion av ytdefekter såsom potthål, korrugeringar och löst grus. Multisensorintegration samt användning av Sequential Probability Ratio Test stärker ytterligare robustheten i den objektiva skadedetekteringen och tillståndsklassificeringen för grusvägar. Resultaten från Roadroid-baserade IRI-mätningar användes för att förfina IRI-tröskelvärdena i ett fyrklassigt tillståndsklassificeringssystem.

Forskningen föreslår även ett deltagarbaserat och adaptivt angreppssätt för insamling av tillståndsdata. Genom att involvera regelbundna användare av grusvägar i datainsamlingen kan den föreslagna deltagarbaserade, datadrivna metoden öka den geografiska täckningen av bedömda grusvägar, vilka huvudsakligen är belägna i avlägsna områden. Samtidigt optimerar adaptiv provtagning den mängd data som samlas in och lagras utan att kompromissa med noggrannheten, vilket möjliggör mer tillförlitliga, objektiva och hållbara tillståndsbedömningar som kan stödja prediktiv underhållsplanering.

Sammanfattningsvis visar avhandlingen hur objektiva, datadrivna metoder och deltagarbaserade angreppssätt kan komplettera och gradvis ersätta traditionella subjektiva bedömningar. Detta ger vägsföreningar, kommuner och vägförvaltande myndigheter tillgång till för tillståndsbedömning som möjliggör bredare täckning, är effektiva och ändamålsenliga samt socialt inkluderande. Genom att kombinera multisensordata med intressentengagemang lägger forskningen grunden för hållbara, prediktiva, proaktiva och robusta system för förvaltning av grusvägar och bidrar därmed till teori, metod och praktik inom förvaltning av grusvägsinfrastruktur.