Rutting, a prevalent failure mode in flexible pavements, largely stems from subgrade issues. Despite this, there is a lack of standard protocols to evaluate subgrade rutting or permanent deformation (PD). This study attempted to characterise PD in subgrades, focusing on a poorly graded sand and two silty sands. Moisture contents above and below optimum levels were considered to account for seasonal variations. The research involved adapting a test to assess the PD by determining typical stresses on the subgrade. Moreover, given these soils’ unsaturated state and medium- to fine-grained nature, suction is an important factor. Suction-controlled multi-stage repeated load triaxial tests were conducted, and the results were fitted by a PD model modified to account for suction. The characterisation was compared with the subgrade strain criterion used in pavement design solutions. Results indicated discrepancies between the PD characterisation and strain criteria predictions, with the silty sands performing better than the poorly graded sand, consistent with the shakedown theory.

This study investigates the influence of moisture and suction on the resilient modulus (MR) of subgrade soils. The research employs suction-controlled Repeated Load Triaxial (RLT) tests on three sandy materials with varying fines content. The soil water retention curves (SWRC) for the three materials were obtained and allowed expediting suction equilibrium outside the triaxial chamber by controlling the water loss and thus inferring the yielded suction with the SWRC parameters. The results show that MR increases with lower moisture content and higher suction. Two stress-based models and two moisture-based models are evaluated for predicting MR. The findings indicate that stress-suction models provide a good fit for the silty sands, but only the model where suction is an independent variable suits all tested materials. Additionally, a proposed suction-based model demonstrates promising results. Overall, the study highlights the importance of considering both moisture and suction for accurate MR characterisation of subgrade soils.

Machine learning (ML) models are increasingly getting attention in predicting pavement maintenance methods to improve decision-making. This study investigates the use of ML at the municipal level to predict the street pavement condition index (PCI) rating over a 4-year span. Several supervised learning models, namely linear regression (LR), random forest (RF), and neural network (NN), were applied to the visually assessed pavement condition data of Skellefteå municipality, Sweden. Pavement distress, pavement age, and traffic data were used in several combinations to evaluate and compare the performance of the models. The RF model was based on paired variables of pavement age and pavement distress data. The results were comparatively accurate with R2=0.59 and Spearman's coefficient=0.74 for residential streets in the model testing stage. Similarly, for main, collector, and industrial (MCI) streets, the RF model, based on pavement age and traffic variables, performed best with R2=0.79 and Spearman's coefficient=0.88 during the model testing stage. The importance of input variables varies with the level of the model's sophistication and pavement performance goal; however, pavement age is the dominant variable. The prediction models can be useful in effectively managing street networks among municipalities, even those with scarce resources.

One step towards reducing construction and industrial wastes (CIW) is to enhance their utilisation in road construction. Prior to their implementation, it is essential to evaluate their mechanical properties to ensure suitability and establish design guidelines. With this objective, four CIW materials were tested using repeated load triaxial (RLT) equipment to evaluate the materials’ functional properties, namely stiffness (Mr) and permanent deformation (PD) characteristics. In addition, full-scale accelerated pavement tests (APTs) were conducted where the four CIW materials were used as the subbase layer instead of traditional materials. Thereafter, a mechanistic–empirical (ME) pavement design and performance prediction method was used to predict their APT performances based on the RLT testing parameters. It was found that the ME performance method can adequately predict the CIW´s rutting development. The approach presented here, therefore, can facilitate the development of design guidelines and the increased utilisation of CIW-based materials in road construction projects.

Assuring sufficient structural stability of cycle paths is important to avoid distress. Being thin and narrow structures, they are highly susceptible to edge cracking by passings of heavy vehicles. Two important factors that affect distress are lack of lateral support at the edge and increased moisture content in the unbound granular materials. Three full-scale instrumented cycle path structures with different structural design were constructed in a concrete pit at a test facility. Response measurements were conducted with a falling weight deflectometer, a light weight deflectometer, and light, standard and heavy maintenance vehicles at three different groundwater table scenarios. The results confirm reduced load-bearing capacity close to the edge, and at an increased groundwater table. In general, larger transverse tensile strains can also be expected in the asphalt layer close to the edge.

Determining resilient modulus (MR) for subgrades is crucial in advancing Mechanistic-Empirical (M-E) pavement design. This study models the responses of a poorly graded sand and two silty sands through Suction-controlled Repeated Load Triaxial tests (RLT), factoring in moisture content and equivalent suction levels based on Soil Water Retention Curves (SWRC). Using a predictive model, the authors calculate the subgrade’s MR for a typical pavement cross-section in Sweden, incorporating layer parameters for critical seasons and climatic zones defined by the Swedish Transport Administration (STA). Results highlight the materials' sensitivity to moisture-suction and their stress dependency. Notably, the predicted MR for silty sands exceeded STA-recommended values across seasons and climatic zones. The poorly graded sand aligns well when the coefficient of earth pressure at rest k0 equals 1, except under wet conditions, in such case STA-recommended values are optimistic. Comparisons with existing data support the findings, particularly for the silty sands. In summary, this research sheds light on three subgrades and offers a reproducible method to expand the database of subgrade materials. Furthermore, if offer insights for enhancing M-E pavement design, considering different climatic conditions and materials.

Street pavements are subject to various types of distress which necessitate a cost-effective management approach. This paper presents the outcomes of a survey focusing on street pavement maintenance and the utilization of machine learning (ML) pavement performance models on a 320 km municipal street network in Skellefteå municipality, Sweden. The findings reveal that the most common types of distress on Swedish streets include potholes, surface unevenness and alligator cracking, while prevalent causes of these distress are pavement ageing, heavy traffic and pavement patches. The windshield method of assessment of street pavement is prevalent, but the use of pavement management systems (PMS) is limited and pavement performance models are rarely employed. The case study reveals that Random Forest (RF) models developed for non-residential streets perform better than residential street models. RF models based on the variables age (A) and traffic (T) emerged as the best models, with 84% prediction accuracy. However, the R-squared value for the RF model applied to residential streets was 0.53, slightly surpassing the values for all models applied to non-residential streets (0.31, 0.50, 0.49). Further evaluation of models is suggested by using additional data.

Cyclists' riding comfort, related to pavement texture and unevenness, has not been thourougly investigated, partly due to the lack of condition assessment methods specifically adapted to the speed and space limits on cycle paths. Metrics that better describe the perceived comfort of cyclists, rather than that of car users, are needed. In this paper a novel method, the Bicycle Measurement Trailer (BMT), is proposed to bridge this gap. Eight different cycle path surface types have been assessed with regards to pavement texture and for four of these surfaces the longitudinal evenness was assessed. The accuracy and repeatability of the BMT were evaluated. Finally, five different metrics (Dynamic Comfort Index, Evenness Coefficient, 0.5 m Straight Edge, International Roughness Index and Root Mean Square), were calculated from the collected data and assessed. The main findings suggest that the BMT has a high accuracy at normal and high cycling speeds and a high level of repeatability at normal cycling speed. The surfaces could be ranked according to texture, and the evenness was successfully analysed. In conclusion, the BMT could be a valuable tool to assess the cycle path surface condition in relation to bicycle riding comfort.

The municipal street network acts as a multifunctional asset by providing people, vehicles and public services with a well-functioning infrastructure. To keep it in good condition, optimal maintenance measures are required which would result in an efficient use of taxpayers' money. This paper investigates the street network deterioration processes and the management practices that the municipal administrations have applied in Sweden. The study is based on a survey with Swedish municipalities using questionnaires and complementary interviews. The answers provide insight into a wide range of common pavement distresses and deterioration factors, along with pavement management practices. The study identifies that potholes, surface unevenness and alligator cracking are the most cited challenges, while pavement ageing, heavy traffic and patches are the most noted causes. Similarly, the cold climate and population density are influential factors in pavement deterioration. Allocation of the maintenance and rehabilitation and reconstruction budget is higher in the northern part of the country as well as in densely populated municipalities. Condition data collection and use of commercial Pavement Management Systems (PMS) are limited. Addressing the challenges effectively may be possible through the enhancement of the budget, feasible/clear guidelines from municipal councils/politicians, and reducing the gap between street network administrations and utility service providers.

There is a need to move society in a sustainable direction. One way to contribute to thismove is to change to more sustainable transport modes, such as cycling. To increase cycling, theinfrastructure is important, and good quality cycle paths are needed. However, little is known aboutthe degradation of cycle paths. This paper aims to investigate what modes of pavement distressare found on municipal cycle paths in Sweden, and what probable mechanisms lie behind suchdistress; these are determined based on questions from a state-of-practice survey, interviews, and aliterature review. The main findings are that the most commonly stated distress modes are surfaceunevenness followed by longitudinal cracks, and the most commonly stated causes of distress areageing, followed by structural interventions, and roots and vegetation. The results also show that forseveral distress modes, there are probable connections with climatic factors such as temperature andmoisture, as well as with the population size of the urban areas. Objective data are needed regardingtraffic load and the climatic factors that affect cycle paths, along with information on their structuraldesign, to better understand their degradation.