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The high-performance, cleaner rejuvenation of aged SBS-modified asphalt mixtures (ASBSMAMs) has been a hotspot in asphalt research. Currently, the most popular rejuvenation method still involves hot-mix asphalt with a commonly used oil as the rejuvenator for recycling. However, high-quality, cleaner warm-mix rejuvenation technology for ASBSMAMs is still needed to enrich this field. This study considered adopting a polyurethane (PU) prepolymer and 1,4-butanediol diglycidyl ether (BUDGE) as reactive rejuvenators to achieve warm-mix reaction–rejuvenation to enhance the properties of ASBSMAMs with the use of a wax-based additive, Sasobit. A series of tests were conducted to realize this, including the viscosity–temperature correlation of the rejuvenated binders, as well as tests of the moisture-induced damage, high-temperature stability, low-temperature cracking resistance, and fatigue resistance of the rejuvenated mixtures. The results showed the following: through reaction–rejuvenation, Sasobit could reduce the viscosity of the rejuvenated SBSMA (RSBSMA) below 150 °C for warm mixing and slightly decrease the viscosity–temperature susceptibility; warm-mix reaction–rejuvenation helped to improve the resistance to water-immersion-induced damage and freeze–thaw damage in ASBSMAMs; the addition of Sasobit showed benefits in improving their resistance to permanent deformation, with the dynamic stability values exceeding 5700 pass/mm as more than 1% Sasobit was added; the flexural damage resistance of ASBSMAMs at low temperatures could be enhanced after warm-mix reaction–rejuvenation; and, under reaction–rejuvenation conditions, Sasobit did not reduce the fatigue resistance of the RSBSMAM and, conversely, at limited higher dosages, it worked more effectively. Overall, the studied warm-mix reaction–rejuvenation technology has been proven to be effective for the environmental recycling and reuse of ASBSMAMs at high quality. [ABSTRACT FROM AUTHOR]

Bridge early warning based on structural health monitoring (SHM) system is of significant importance for ensuring bridge safe operation. The temperature-induced deflection (TID) is a sensitive indicator for performance degradation of continuous rigid frame bridges, but the time-lag effect makes it challenging to predict the TID accurately. A bridge early warning method based on nonlinear modeling for the TID is proposed in this article. Firstly, the SHM data of temperature and deflection of a continuous rigid frame bridge are analyzed to examine the temperature gradient variation patterns. Kernel principal component analysis (KPCA) is used to extract principal temperature components. Then, the TID is extracted through wavelet transform, and a nonlinear modeling method for the TID considering the temperature gradient is proposed using the support vector machine (SVM). Finally, the prediction errors of the KPCA-SVM algorithm are analyzed, and the early warning thresholds are determined based on the statistical patterns of the errors. The results show that the KPCA-SVM algorithm achieves high-precision nonlinear modeling for the TID while significantly reducing the computational load. The prediction results have coefficients of determination above 0.98 and fluctuate within a small range with clear statistical patterns. Setting the early warning thresholds based on the statistical patterns of errors enables dynamic and multi-level warnings for bridge structures. [ABSTRACT FROM AUTHOR]