The accuracy of the initial void ratio is vitally important to ensure the exact position of the normal compression line of remolded clay. In this article, a series of standard oedometer tests were performed on four different types of commercial clay in slurry state to analyze the error sources that affect the experimental accuracy of the initial void ratio. For remolded clay, it is found that the extra water at the end of the test has an unfavorable effect on the calculation accuracy. The precautions and steps for ensuring the experimental accuracy of initial void ratio in the oedometer test are detailed. According to the error propagation theory, the error estimation of five methods commonly used in the literature for calculating the initial void ratio is revealed. On this basis, a novel data fusion approach based on Bayesian inference is proposed to determine the initial void ratio of remolded clay, which fully considers the uncertainty of measurement error. The effectiveness of the proposed Bayesian data fusion approach has been validated by the data from mercury intrusion porosity testing, promoting the application of Bayesian theory in soil mechanics.

This paper first examines the evolution of pore-size distribution (PSD) in four types of reconstituted clays during one-dimensional (1D) compression, utilising mercury intrusion porosimetry. Central to this work, fractal theory is then applied to quantify the complexity of the pore structure through fractal dimensions, followed by being correlated with 1D compressibility. The key findings are as follows: (1) The 1D compressibility of the four clays exhibits significant variability, with montmorillonite demonstrating the highest compressibility and Shenzhen clay, dominated by chlorite, the lowest. This is associated with distinct pore size evolution patterns under 1D loading while also emphasising the crucial role of mesopores in macroscopic clay deformation. (2) Fractal dimensions increase with loading, reflecting the progressive refinement of the pore structure, with natural Shenzhen clay demonstrating the most pronounced change. (3) A mathematical relationship between fractal dimension and 1D compressibility is established for each clay type, providing a quantitative tool for predicting 1D compressibility based on fractal dimensions. (4) The testing procedures and methods to ensure the representativeness of pore structure analysis are elaborated, ensuring reliable PSD data. By employing fractal theory, this study provides new insights into the correlation between pore structure complexity and compressibility in reconstituted clays.

The concrete‐faced rockfill dam (CFRD) has been widely constructed worldwide, and the reliability of the concrete panels, the most important containment structure, is critical to the safety of the CFRDs and downstream communities. Several practical projects show that concrete slab cracking usually occurs during the water storage period, which can be attributed to the rapid increase of hydrostatic pressure and uneven settlement of the dam body. In this paper, a time‐dependent reliability analysis method considering the fuzziness of the failure criterion is presented to assess the slab cracking risk of the Houziyan CFRD during the water storage period. Based on the observed deformation, the material parameters are calibrated to ensure the fidelity of the numerical simulation. Based on the Drucker–Prager yield criterion and tensile strength criterion, the response surface method is utilized to construct the performance functions at different moments, and then the time‐dependent reliability analysis considering the fuzzy failure criteria is implemented for the concrete slab. The case study of the Houziyan CFRD shows that, during the initial period of water storage, the reliability index of the concrete slab decreases with the rise of the water level. With a stable water level, the probability of cracking of the concrete slab slowly decreases as the deformation of the dam body and slab tend to be coordinated. Especially, considering the fuzziness of the failure criteria, the reliability index decreased by about 2%, indicating that the proposed evaluation method is biased toward safety. The method proposed in this paper can reflect the evolution law of concrete slab reliability with the operating environment and provides a new approach to evaluate the performance of the concrete slab during the water storage period.

Long-term dam safety assessment relies on continuous monitoring data from multiple spatially distributed measurement points. However, monitoring data are affected by measurement noise, environmental disturbances, and model errors, while the spatial correlation among monitoring points is often ignored, leading to biased uncertainty estimation and unreliable prediction intervals. To address these issues, this study proposes a spatial-correlation-aware distribution-adaptive interval prediction method for dam monitoring via two-level uncertainty fusion. Measurement random noise is first separated using a filtering strategy, and its uncertainty is updated by incorporating the spatial correlation among multiple monitoring points. A regression model is then established based on the filtered monitoring data, and model prediction uncertainty is quantified from the residual distribution. The two uncertainty components are further integrated to construct distribution-adaptive asymmetric prediction intervals. The proposed method is verified using deformation monitoring data from the PB high core rockfill dam. The results show that the proposed method achieves an average PICP of 0.9898 on the training set, close to the target coverage level of 0.99, while reducing the average NMPIW by 16.9% and 13.4% compared with the symmetric interval method and the traditional 3σ method, respectively. On the validation set, the average NMPIW is further reduced by 18.4% and 26.5%, demonstrating that the proposed method can provide more compact and informative prediction intervals for refined dam safety monitoring.