It is commonly known that for characteristics, such as long-distance, high-sensitivity, and full-scale monitoring, phase-sensitive optical time-domain reflectometry (Φ-OTDR) has developed rapidly in many fields, especially with the arrival of 5G. Nevertheless, there are still some problems obstructing the application for practical environments. First, the fading effect leads to some results falling into the dead zone, which cannot be demodulated effectively. Second, because of the high sensitivity, the Φ-OTDR system is easy to be interfered with by strong noise in practical environments. Third, the large volume of data caused by the fast responses require a lot of calculations. All the above problems hinder the performance of Φ-OTDR in practical applications. This paper proposes an integration method based on a complete ensemble empirical mode decomposition with adaptive noise and Kullback–Leibler divergence (CEEMDAN-KL) and an adaptive moving neighbor binary pattern (AMNBP) to enhance the performance of Φ-OTDR. CEEMDAN-KL improved the signal characteristics in low signal-to-noise ratio (SNR) conditions. AMNBP optimized the location and recognition via a high calculation efficiency. Experimental results show that the average recognition rate of four kinds of events reached 94.03% and the calculation efficiency increased by 20.0%, which show the excellent performance of Φ-OTDR regarding location and recognition in practical environments.

To achieve a rapid assessment of reinforced bridges without interrupting traffic, the method and influence parameters for evaluating prestressed CFRP (Carbon Fiber Reinforced Polymer) plate-strengthened bridges were investigated based on distributed sensing while considering the bridge–vehicle coupling. Firstly, based on the principle of distributed monitoring, the strain influence line equation for prestressed CFRP plate-strengthened bridges under moving vehicle loads was derived. An evaluation index and method for assessing strengthening effectiveness based on distributed strain monitoring was proposed. Numerical simulation experiments were conducted for verification, in which a simulation method for solving the bridge–vehicle coupling vibration response based on the displacement contact method was studied. Relevant example verifications were carried out to prove the correctness of this method. On this basis, a dynamic performance analysis of the bridge before and after strengthening under bridge–vehicle coupling was performed. Finally, a study on influencing parameters was conducted to investigate the impact of different prestress levels of CFRP plates, different vehicle speeds, and different vehicle axle loads on the evaluation method. The results demonstrate that the bridge–vehicle coupling calculation model based on the displacement contact method can effectively simulate the dynamic response of the bridge after reinforcement. The evaluation method based on distributed sensing monitoring is unaffected by the speed and axle load of the vehicle, enabling the assessment of the condition and stress state of the strengthened bridge.