Energy sustainability and the establishment of the ‘national water network’ are all inseparable from the construction of underground engineering. Monitoring indices for the surrounding rock are vital for the safety management of underground engineering construction, which determines the actual state of the surrounding rock. The complexity of deep underground engineering construction leads to many situations that cannot be predicted and explained by existing experience. Therefore, it is necessary to identify which monitoring index best represents the surrounding rock damage. Currently, there are no advanced and convenient effectiveness evaluation schemes for surrounding rock monitoring information. To fill the technical gap, this study introduces the volume expansion rate (VER) index for surrounding rock and proposes a machine learning (ML)-based evaluation scheme for the effectiveness of monitoring indices. First, six conditions with different in situ stresses are designed, and tunnel excavation monitoring tests are conducted. Second, the surrounding rock damage determination experiments using the ML classification algorithm are performed, and the accuracy matrix and index significance scores are obtained. The evaluation results show that: (1) The multi-class logistic regression algorithm is more suitable for determining surrounding rock damage with high accuracy and more appropriate significance evaluation outcomes. (2) Under the higher in situ stress condition, the tangential stress is more sensitive to the surrounding rock damage. (3) As the in situ stress increases, the significant monitoring indices demonstrate a transition ‘from shallow to deep, from regional damage to point failure’, describing the instability of the surrounding rock and inspiring a new instability criterion for surrounding rock.

Accurate identification of building structure frequencies forms the basis for damage detection. The structural dynamic response signal, under ambient excitation, can be transformed into a superposition of multiple single-frequency exponentially damped sinusoids combined with random white noise. However, the peak power spectrum of the response signal tends to exhibit line splitting, compromising the precision of frequency identification. This study examines the accuracy characteristics of the single-frequency free damping vibration signal (SFFDVS) and derives the Cramer–Rao lower bound for the frequency estimator. It thoroughly analyzes the factors influencing the accuracy of SFFDVS frequency identification. The study reveals that the primary cause of spectral line splitting is the random delay inherent in SFFDVS. Based on the maximum likelihood method (MLM), this research introduces the MLM algorithm for SFFDVS and provides a simulation analysis. The findings indicate that the MLM estimation algorithm for frequency parameters effectively addresses spectral line splitting and offers robust noise resistance and recognition accuracy.

–Bridge–sluice systems serve road traffic and hydraulic regulation, yet the directional association of traffic–induced vibrations within the bridge deck—sluice pier—embankment system and its implications for –wall–soil interaction remain insufficiently understood. Synchronized field measurements were conducted at the Wucun control sluice using four vehicle types and multiple speeds. Transfer entropy (TE) was used to identify directional information associations rather than amplitude– or energy –transmission ratios, and measured horizontal and vertical velocity histories were converted into phase–consistent displacement inputs for a two–dimensional –wall–soil model. Vibration amplitudes varied non–monotonically with speed, indicating that vehicle mass alone did not govern the response. The crawler excavator generated vertical and horizontal bridge–deck peak–to–peak velocities of 22.549 and 26.334 mm/s, respectively. The dominant information pathway was bridge deck to sluice pier to embankment; the crawler excavator produced the strong–est transfer, with a pier–to–embankment retention coefficient of 0.81. Within the adopted two–dimensional numerical idealization, the backfill was predicted to remain predominantly elastic at the measured vibration level, with response concentrations at side–wall corners and adjacent contact zones, particularly near the upper corner of contact zone B. Local plasticity first emerged at 10 times the measured input and expanded over the 15–25–fold range. These findings support control of high–impact vehicles and targeted monitoring of side–wall—embankment connections where deformation incompatibility and seepage –control risk may concentrate.