Strain-based condition evaluation has garnered as a crucial method for the structural health monitoring (SHM) of large-scale engineering structures. The use of traditional wired strain sensors becomes tedious and time-consuming due to their complex wiring operation, more workload, and instrumentation cost to collect sufficient data for condition state evaluation, especially for large-scale engineering structures. The advent of wireless and passive RFID technologies with high efficiency and inexpensive hardware equipment has brought a new era of next-generation intelligent strain monitoring systems for engineering structures. Thus, this study systematically summarizes the recent research progress of cutting-edge RFID strain sensing technologies. Firstly, this study introduces the importance of structural health monitoring and strain sensing. Then, RFID technology is demonstrated including RFID technology’s basic working principle and system component composition. Further, the design and application of various kinds of RFID strain sensors in SHM are presented including passive RFID strain sensing technology, active RFID strain sensing technology, semi-passive RFID strain sensing technology, Ultra High-frequency RFID strain sensing technology, chipless RFID strain sensing technology, and wireless strain sensing based on multi-sensory RFID system, etc., expounding their advantages, disadvantages, and application status. To the authors’ knowledge, the study initially provides a systematic comprehensive review of a suite of RFID strain sensing technology that has been developed in recent years within the context of structural health monitoring.

Airport pavements are widely constructed as airport runways, taxiways, and aprons. The airport traffic should be considered during the design stage of airport pavements before the construction. To protect airport pavements from negligent overload, a pavement strength rating system and an aircraft load classification system are adopted. Under the rating systems developed by the International Civil Aviation Organization (ICAO), Aircraft Classification Number (ACN) and Pavement Classification Number (PCN) are calculated to assess the aircraft loads and the load-carrying capacity of the airport pavements for unrestricted operations. With the increase in the air transport demand, increasing attention on the continuous evaluation of airport pavement conditions is introduced to airports across the globe. The load-carrying capacity can be evaluated to assess airport pavement conditions by calculating the value of PCN continuously during the service cycle of pavements. The value of PCN can be estimated by testing the response of stationary dynamic loads, adopting a Falling Weight Deflectometer (FWD) or a Heavy Weight Deflectometer (HWD) device that simulates the stress generated by aircraft movements. By evaluating the PCN continuously in Airport Pavement Management System (APMS), cyclic determinations of the load-carrying capacity can be conducted to support the maintenance and rehabilitation (M&R) decision-makers. This review summarizes the methodology for determining the PCN and the tests conducted for evaluating the strength of airport pavement presented in the published literature. Further, the review highlights the benefits of determining PCNs by adopting the FWD or HWD field data with more precise results through mechanistic procedures.

The resilient modulus (M_r) and permanent deformation of subgrade soils are key indicators for assessing pavement performance under repeated traffic loads. Although numerous studies have confirmed their importance in pavement design and performance prediction, a systematic review of empirical relationships and scientific knowledge is lacking, resulting in insufficient integration and application of current findings. To address these issues, this study systematically reviews laboratory and field-testing methods based on over 200 published papers, summarizes common empirical equations, and focuses on the feasibility and advantages of integrating AI to predict M_r. Meanwhile, by examining the main factors that influence M_r and permanent deformation, this study synthesizes and evaluates existing research to identify potential gaps. Findings indicate that laboratory and field tests effectively capture the mechanical behavior of pavement materials, and incorporating AI technology in modulus prediction enhances accuracy and efficiency while managing complex influencing factors. However, existing empirical equations have not been fully integrated with emerging technologies for validation and optimization, and some predictive models remain limited in terms of applicability and generalizability. This review highlights the need to refine empirical relationships for the resilient modulus using stochastic methods and AI techniques, thereby facilitating a more comprehensive integration of the latest testing technologies and computational tools. This research is of great significance for advancing sustainable pavement design, optimizing maintenance strategies, and guiding future research directions.

The extensive application of natural unbound granular materials (UGMs) motivates studies into the mechanical properties of alternatives such as processed crushed rocks employed commonly as base or subbase layers. The rutting and settlement generated in base and subbase layers is widely restricted in many specifications and standards. In this research, the dynamic behavior including the resilient modulus (${\mathrm{M}}_{\mathrm{r}}$) and the plastic strain (ε${\mathsf{\epsilon }}_{\mathrm{p}}$) of the crushed rocks collected from Queensland in Australia will be tested by a series of repeated load triaxial test (RLT) tests to investigate the behavior of UGMs under the fluctuation of the degree of saturation (DOS) (59%–100%). In particular, the RLT specimens were prepared in the laboratory through proper gradation under optimum moisture content (OMC) and 100% standard proctor maximum dry unit weight. Results from the RLT tests showed that UGM specimens soaked at higher DOS generated lower resilient modulus and weaker resistance to heavy traffic volumes with significant accumulation of plastic strain. The ${\mathrm{M}}_{\mathrm{r}}$ and ε${\mathsf{\epsilon }}_{\mathrm{p}}$ of the tested aggregates under different cyclic deviator stresses of 425 kPa and 625 kPa approximately linearly decreased and approximately linearly increased as the DOS increased with a certain number of cycles up to 50,000, respectively.