The granular bases commonly used in the construction of road infrastructure projects often require a high consumption of raw materials. The potential utilization of recycled materials, specifically Reclaimed Asphalt Pavement (RAP) derived from road asphalt pavement demolition, emerges as a promising sustainable advantage for infrastructure projects, considering its potential environmental and cost benefits in other layers of the structure. In this context, this research proposes a feasibility study on the use of RAP as a granular base layer, supported by an advanced laboratory analysis that includes a range of tests simulating the in-service conditions as well as a full-scale demonstration of the material behavior under static and dynamic loads. Various design variables, such as different gradations and binder content, are considered. The results demonstrate that, despite having discontinuous gradation and smaller aggregate sizes than those commonly applied in natural base layers, the evaluated recycled materials exhibit a higher load-bearing capacity and resistance to permanent deformation than the reference materials commonly used as granular bases. Notable enhancements of up to 30% in elastic modulus, coupled with reductions of around 20% in permanent deformations, have been documented using the asphalt cement potential in the old pavement.

Embedding sensors in rail pads allows for direct monitoring of train–track interaction, which is essential for preventive maintenance and sustainable management of railway infrastructure. Nonetheless, given the critical role that rail pads play in enhancing railway track performance and durability, it is crucial to define the optimal configuration of the sensorized pads (InterActive Pads) that ensures both mechanical reliability and functional accuracy. Also, before its widespread application, it is mandatory to provide calibration and modelling to allow for preventive maintenance, improving sustainable management. Thus, this research optimizes the design of rail pads with embedded piezoelectric sensors while validating its performance and developing calibration models to enable the implementation of preventive measures for railroad tracks. Laboratory tests identified the optimal sensor position at the rail pad extremity, featuring a half-embedded design with a gap beneath to ensure mechanical resistance and durability. Large-scale testing further facilitated the development of a calibration model that enhances diagnostic accuracy and supports proactive and sustainable maintenance strategies. The findings demonstrate a strong correlation between sensor signals and train-induced forces, allowing predictions of long-term track performance. This predictive capability enables more effective maintenance, reducing costs and improving safety. By providing a sustainable solution for railway management, this research lays the groundwork for future implementation on real tracks, offering a robust framework for proactive, data-driven maintenance strategies.

The development of cost-effective and reliable railway monitoring technologies is crucial for the maintenance of modern infrastructure. Embedding sensors into rail pads has emerged as a promising approach for monitoring wheel–track interactions, but the successful implementation of these systems requires a robust framework for signal data acquisition and analysis. This study validates a custom-designed External Signal Acquisition Device (ESAD) for use with smart rail pads, comparing its performance against a high-precision commercial analog module. While the commercial module delivers exceptional accuracy, its high cost, bulky size, and complex installation requirements limit its practicality for large-scale railway applications. Laboratory-scale and full-scale experiments simulating real-world railway conditions demonstrated that the custom ESAD performs comparably to the commercial module. During simulated train passages, the ESAD showed reduced signal dispersion as load and train speed increased, confirming its ability to provide reliable calibration data. Moreover, the device maintained over 95% reliability in analyzing load-to-signal linearity, ensuring consistent and dependable performance in both laboratory and field settings. However, the ESAD does have limitations, including slightly lower resolution for low frequencies and potential sensitivity to extreme environmental conditions, which may affect its performance in specific scenarios. These findings highlight the ESAD’s potential to strike a balance between cost and functionality, making it a viable solution for widespread railway monitoring applications. This research contributes to the advancement of affordable and efficient railway monitoring technologies, fostering the adoption of preventive maintenance practices and enhancing overall infrastructure performance.

The railway track system is a complex assembly of rails, sleepers, and fastenings designed to ensure operational stability and safety. Within this framework, rail pads play a critical role in load transfer, vibration attenuation, and noise control. This study provides a comprehensive bibliometric analysis of research on railway components published between 2015 and 2024, based on 288 documents retrieved from Scopus, Elicit, and Web of Science. Publication trends reveal a steady increase in research output over the study period, primarily driven by Spain and China. Keyword co-occurrence analysis yielded 51 keywords organized into seven thematic clusters, with the highest frequency terms being “rail pad”, “noise”, “dynamic property”, and “MTHDRP”. The analysis highlights a significant focus on materials such as TPEs, EPDM, and EVA, with static preload and stiffness identified as the most scrutinized performance factors. Findings indicate a clear thematic shift from traditional field testing toward advanced material science and sensor-integrated monitoring technologies. Ultimately, this review outlines future research trajectories emphasizing sustainability, smart sensor integration, and predictive maintenance. By synthesizing a decade of academic contributions, this study serves as a strategic roadmap for optimizing the long-term durability and efficiency of modern railway infrastructure.

Railway managers face increasing pressure to reduce downtime, emissions, and operational costs. In response, the development of new technologies to facilitate the study of predictive maintenance and to evaluate its impact on the economy and society is becoming increasingly essential, thus, the present study is carried out. This study introduces a three-stage approach for enabling predictive maintenance using a sensor-integrated intelligent rail pad. In the first stage, the monitoring system’s mechanical performance and long-term durability were validated through standardized impact attenuation and fatigue tests, which is essential to guarantee the efficiency of the predictive system. The second stage involved creating high-precision models from sensor signals estimating axle loads and predicting ballast settlement. In the third stage, a comprehensive life cycle assessment (LCA) and life cycle cost analysis (LCCA) were performed to compare the benefits of predictive versus corrective maintenance strategies. The experimental tests confirmed compliance of the intelligent rail pads with regulatory standards and demonstrated the system’s suitability for continuous monitoring. Over a 50-year period, results indicate that predictive maintenance can reduce settlement by up to 14%, decrease ballast renewals by 13%, and halve energy use, emissions, and maintenance costs. These findings suggest that the intelligent rail pad has the potential to serve as a scalable solution for enhancing the efficiency, sustainability, and economic performance of railway infrastructure.

The granular sub-ballast layer commonly used in the construction of modern railway tracks, but its use involves a significant consumption of natural aggregates, which, moreover, must meet certain requirements that are sometimes unavailable. Therefore, the possibility of using recycled aggregates arises to promote sustainable development. In this context, this research focuses on assessing the feasibility of using RAP (Reclaimed Asphalt Pavement) as a sub-ballast layer in railway tracks, these recycled materials can contribute to increased load-bearing capacity and waterproofing of the lower layers due to the presence of adhered bitumen, studying the optimal application process to achieve a layer with high mechanical performance. It presents a laboratory investigation on the influence of different design factors on the mechanical performance of the layer, evaluating the effects of RAP characteristics, and the impact of the compaction procedure. Although these recycled materials primarily presented challenges in terms of size, particle distribution, and strength, which differ from the aggregates commonly used in similar high-demand layers, laboratory tests simulating expected service conditions for these materials in railway tracks have shown the viability and suitability of using recycled asphalt materials to achieve more sustainable railway tracks. Under compaction conditions at temperatures commonly achieved in construction (above 40 ºC) and considering its particle size distribution, it is possible to create a layer with high performance in terms of strength and durability.