This study presents a comprehensive method for detecting debonding defects in concrete-filled steel tube (CFST) structures using wave propagation analysis with externally attached piezoelectric ceramic sensors. Experimental tests and numerical simulations were conducted to evaluate the sensitivity and accuracy of two measurement techniques—the flat and oblique measurement methods—in detecting debonding defects of varying lengths and heights. The results demonstrate that the flat measurement method excels in detecting debonding height, while the oblique method is more effective for detecting debonding length. A normalized judgment index (DI) was introduced to quantify the correlation between debonding characteristics and the detected signal amplitude, revealing the significant influence of sensor spacing on detection accuracy. Furthermore, a mathematical model based on wavelet packet energy analysis was developed to establish a linear relationship between wavelet packet energy and debonding size. This model offers a scientific foundation for the quantitative detection of debonding defects and provides a new approach to the health monitoring of CFST structures. The integrated use of both measurement techniques enhances detection precision, enabling both qualitative and quantitative defect analysis, which can significantly guide the maintenance and repair of CFST structures.

Accurate determination of constitutive model parameters is crucial for reliable numerical simulation in deep foundation pit engineering. This study presents an inverse analysis method using Multioutput Least-Squares Support Vector Regression (MLSSVR) optimized by the Gray Wolf Optimization (GWO) algorithm to invert key parameters of the Hardening Soil (HS) model. A case study on a foundation pit in the dredger fill stratum of Xiamen Railway integrates finite element simulation with machine learning. The proposed GWO-MLSSVR model demonstrates high predictive accuracy, with lateral displacement predictions closely matching field monitoring data and relative errors within 5% at various depths of measurement point. Compared to traditional inversion methods and MLSSVR models optimized by other algorithms, this approach significantly reduces prediction errors. Additionally, the influence of construction stages, input layer nodes, and training sample size on inversion performance is investigated. This method provides a practical and efficient solution for accurately obtaining soil parameters under complex soil conditions, thereby enhancing the reliability of geotechnical numerical simulations and offering valuable guidance for foundation pit design and safety assessment.

Thousands of coastal reinforced concrete structures using HRB400 bars have served for over three decades in China. Their reinforcement simultaneously endures chloride corrosion and seismic action, yet studies on performance degradation remain limited. This paper investigates the low-cycle fatigue (LCF) behavior of HRB400 bars under various strain amplitudes, systematically analyzing corrosion morphology, cyclic stress–strain response, fatigue life, and underlying mechanisms. Corrosion is induced by an adjusted accelerated method that replicates field conditions. Observations reveal that corrosion pits act as primary crack initiation sites. Crack paths and fracture surfaces progressively follow the local pit geometry as strain and corrosion grow. The detrimental effect of corrosion on LCF life is more pronounced for smaller bars. At a γ of around 8%, 20 mm bars lose 60.7% of the half cycles to failure at ε = ±1.5%, but only 37.5% at ε = ±5.0%. Predictive corrosion-inclusive strain amplitude (ε_a)–fatigue life models are proposed, yielding R² = 0.952 (16 mm) and 0.928 (20 mm). A unified LCF predictive model, calibrated on a database of 310 corroded/uncorroded bar tests, is established. The final model comprehensively considers the characteristics of rebars, seismic action, and corrosion damage, improving the conventional relationship between LCF life and seismic loading. This work contributes to the understanding of the fatigue behavior of HRB400 bars and provides support for time-dependent seismic reliability analysis of aging reinforced concrete structures in corrosive environments.