As cities grow in size, traffic also increases, thus making the population more exposed to road noise and traffic accidents. It is therefore important to study and understand which properties of the pavement influence its acoustic impact and skid resistance performance. The pavement texture plays a major role in generating noise and friction, and it can be engineered in order to control both of them at the same time. The phenomena regulating skid resistance are well understood today. The same applies for noise generation and propagation; the literature contains methods of designing the pavement surface layer to achieve consistent results. Several types of solutions can be found for asphalt mixtures, most of them derived from decades of studies and research. They use different approaches to be effective for noise and friction, but all have in common the control of the surface’s macro and microtexture. Finally, some considerations are made regarding novel paving solutions with artificial aggregates instead of natural ones to address noise and skid resistance.

Cold bitumen emulsions (CBE) are currently used worldwide for both non-structural (cold surface treatments) and structural (cold bituminous mixtures) paving applications. Among non-structural applications, bituminous slurry surfacings (BSS) allow to improve surface properties and extend the pavement’s life. However, selection of constituents, mix design, application and quality control procedures for BSS are mostly based on local experience and empirical tests. Thus, the use of BSS is associated to higher technical risks with respect to the use of traditional hot-mix technologies. In this context the RILEM Technical Committee 280-CBE TG2 “Multiphase characterisation of cold bitumen emulsion materials” aims to collect worldwide theoretical knowledge and experiences on BSS and to inspire new standards, specifications and guidelines encompassing the wide set of existing practices. This paper summarizes the first part of the literature review carried out as part of the TC workplan and focuses on the definition, the description and the application method for BSS. National standards, construction specifications and best practice documents from North America, South America, Europe and Australia are analysed and compared.

The use of waste materials in road construction is becoming widely spread due to economic and environmental needs. Construction and demolition waste materials and mining residues have been studied for a long time. However, the use of fine materials, mainly from mine tailing and mining residue, is still complex, as they can be used as inert materials into the mix or can become a reactive agent in geopolymer mixes. In the present paper, an experimental application of basalt powder is proposed in the geopolymerisation reaction to produce artificial aggregates. In order to understand the input and output variables’ interactions used in the mix design, a statistical method called Design of Experiments was applied. With this design approach, it was possible to optimize the mix design of the experimental geopolymer mortars. The study evaluated several mixes with respect to their workability, compressive strength, and success rate of aggregates production. Finally, a model for predicting compressive strength is proposed and evaluated.

The road surface texture is responsible for controlling several quality/safety road indicators, such as friction, noise, and fuel consumption. Road texture can be classified into different wavelengths, and it is dependent on the material used in the paving solution. With the aim of evaluating and characterizing the surface texture of a microsurfacing road pavement, six microsurfacing samples were made in the laboratory with both traditional materials (basaltic aggregates and bituminous emulsion) and with innovative materials from recycling procedures (crumb rubber (CR) and artificial engineered aggregate (AEA)). The characterization was performed through the use of a conoscopic holography profilometer with high precision and post-processing of the profiles detected through consolidated algorithms (ISO standards). We found that the aggregate type plays a very important role in the pavement texture. The binder agent seems to be highly important, but more studies regarding this are necessary. The use of crumb rubber as an aggregate proved to be feasible, and the texture parameters that were obtained were in accordance with the benchmark ones. In addition, the study shows that the use of artificial engineered aggregates does not impair the surface texture. Finally, the use of the texture parameters defined by the ISO standards, together with a statistical analysis, could be useful for defining the surface texture characteristics of microsurfacing.

The widespread use of natural aggregates is one of the main causes of the depletion of natural resources, as aggregates are constituents of several construction materials. Alternatively, it is, today, proven to be feasible to use mining tailings, either natural or recycled materials, to produce artificial aggregates through specific processes. A possible way to produce artificial aggregate is through the alkali activation of the powdered material in a process called geopolymerization. This study proposes to use a basalt powder and two different metakaolins as precursors for the production of an alkali-activated artificial aggregate, with a specific shape and size achieved by using 3D-printed molds. The experimental aggregates were evaluated using traditional tests for natural aggregates, such as resistance to compression, specific density and resistance to abrasion and fragmentation. Furthermore, the material was chemically analyzed in order to evaluate the geopolymerization process promoted by the two adopted metakaolins. The physical tests showed that artificial aggregates do not perform well in terms of resistance to wear and fragmentation, which can be improved. However, they revealed promising results in terms of skid, polishing and micro-texture.

As global infrastructure ages under fiscal constraints, traditional Pavement Management Systems often rely on sequential workflows that ignore the dynamic impact of interventions on future deterioration. This paper introduces a data-driven framework integrating a recursive multi-year forecasting engine with network-level Genetic Algorithm (GA) optimization. Unlike static models, this approach establishes a dynamic feedback loop where maintenance decisions at year t update the pavement state inputs for the prediction model at year $$t+1$$ . The framework couples a Light Gradient Boosting Machine (LightGBM), with hyperparameters tuned via Bayesian optimization (Optuna), trained on Long-Term Pavement Performance (LTPP) data ( $$R^2=0.88$$ , $$RMSE=0.19$$ ) with a GA optimization module for resource allocation. Using a section-based splitting strategy to prevent data leakage, results show this recursive integration outperforms reactive strategies, improving network quality by 3% while reducing maintenance costs by 22%. This framework demonstrates the potential of recursive data-driven models to optimize life-cycle costs and reliability in modern pavement management.