Timely identification of accelerating precursors and performing reliable time-to-failure analysis are the key components in the management of slope failure risks. This study focuses on rock slope failures and proposes a framework for online identification of accelerating precursors and dynamic probabilistic prediction of failure time grounded in Bayesian inference. By integrating the Bayesian online change-point detection (BOCD) method with a typical dimensionless trend (TDT) model, the BOCD-TDT algorithm is first developed for online identification of acceleration events and their corresponding onset of acceleration (OA). Subsequently, a Bayesian approach is employed to estimate the parameters of the inverse velocity (INV) method, enabling the dynamic probabilistic prediction of slope failure time while quantifying observational and model uncertainties across different accelerating deformation stages. Building on this, the influence of starting point (SP) selection, trend update (TU), and multi-data fusion on prediction reliability is evaluated, and a novel decision criterion for impending slope failure is proposed. The feasibility of the proposed methods is then validated using 73 rock slope failure cases. Results show that using INV data, the BOCD-TDT algorithm can reliably identify acceleration events and the corresponding OA. In time-to-failure analysis, the reliability of dynamic failure predictions can be enhanced by incorporating both observational and model uncertainties corresponding to the deformation stages into the Bayesian prediction model, along with TU detection and multi-data fusion. The proposed failure probability criterion provides valuable guidance for the identification of impending failure and the establishment of ultimate alert thresholds.

Incineration bottom ash (IBA) holds attractive potential as a construction material, yet its shear behavior under cyclic loading remains insufficiently understood. This study comprehensively characterizes the monotonic and cyclic simple shear behavior of Singapore-derived IBA under constant volume conditions, with particular emphasis on its reuse potential in dynamic load-bearing applications. Key findings reveal that: (1) The material exhibits marked strain-hardening characteristics, demonstrating a density-dependent friction angle increment from 38.3° (loose state) to 42.5° (dense state). (2) Mechanical performance shows strong dependence on Si-Ca-Fe/Al ternary chemical composition and particle gradation characteristics. (3) Distinct failure modes emerge under different loading conditions - liquefaction dominates under unidirectional cyclic simple shear (UDCSS) conditions at low cyclic stress ratios (CSRs) and confining pressures, while bidirectional cyclic simple shear (BDCSS) loading induces cyclic mobility failure at elevated CSR levels, with corresponding cyclic resistance ratios (CRRs) showing a 30 % reduction in BDCSS compared to UDCSS configurations. (4) Pore pressure ratio (R_u) evolution follows a triphasic pattern: liquefaction failures exhibit rapid R_u acceleration in initial and tertiary phases (terminal R_u > 0.9), contrasting with cyclic mobility failures characterized by decaying R_u growth rates and lower terminal R_u values. (5) Notably, the established correlation between CRR and normalized shear wave velocity (V_s1) aligns closely with that of sand-gravel mixture with 5 % fines, which demonstrates the comparable cyclic load-bearing capacity of IBA to that of conventional construction materials. The study highlights the effect of load direction, particle size, and mineralogy in design applications and supports IBA's suitability for reuse in infrastructure subjected to dynamic loads.

Stratigraphic interface characterization and strength parameter assessment of geomaterials constitute fundamental research priorities in geological and geotechnical engineering. While measurement while drilling (MWD) and drilling process monitoring (DPM) have emerged as critical techniques for acquiring real-time drilling parameters, inherent limitations in data interpretation persist. The critical challenge of random fluctuations in MWD-derived penetration rate measurements exhibits poor correlation with the stratified homogeneity characteristics of geological formations. Such discrepancies undermine the reliability of stratigraphic classification and mechanical property analysis. Through systematic comparison of MWD and DPM datasets combined with quantitative parameter evaluation, this investigation reveals significant methodological distinctions in data acquisition accuracy. Machine learning-enhanced analysis employing Support Vector Machine (SVM) algorithms demonstrates that DPM-derived parameters provide superior stratigraphic identification capabilities. Our findings indicate that DPM implementations achieve 20.57 % and 38.01 % higher resolution in interface detection along two drill-holes compared to the conventional MWD approaches. This improvement allows for better prediction of stratigraphic profiles and more precise guidance in subsequent geological and geotechnical engineering practices.

Effective sealing of geological fractures is essential for subsurface stability and mitigating environmental risks such as groundwater contamination and inefficient CO₂ sequestration. Enzymatically Induced Carbonate Precipitation (EICP) offers a promising bio-mediated approach due to its ability to fill and seal fractures. However, real-time precipitation patterns and clogging behavior of EICP under varying fracture and flow conditions remain poorly understood. This study employs a transparent fracture model with visualization to systematically investigate the effects of fracture aperture, flow conditions, and surface roughness on EICP-mediated sealing. Results indicate that fractures with narrower apertures promote tortuous finger-like flow paths, while wider-aperture fractures show more uniform deposition, with fewer but wider preferential flow paths. An appropriate injection rate around 1 mL/min ensures uniform precipitation and effective clogging, avoiding inlet clogging at lower rates (0.1 mL/min) and flushing effect reducing deposition at higher rates (10 mL/min). Additionally, rough fractures exhibit higher precipitation efficiency and greater permeability reduction, driven by their irregular surface geometry, which creates more deposition sites and complex flow compared to smooth fractures. Image processing reveals that precipitation patterns in rough fractures match closely with aperture distribution, compared to more concentrated deposition in smooth fractures. These findings provide insights for optimizing EICP-mediated fracture sealing, with implications for groundwater protection and geotechnical practices.

To realize the soil reinforced through the carbonation of ternary binder under ambient pressure and mild conditions, the present study introduces triethanolamine (TEA), which serves as an effective carbonation accelerator. Through the unconfined compressive strength (UCS) test, the soft soil solidified with ternary eco-binder consisting of ground granulated blast-furnace slag (GGBS), metakaolin (MK), and calcium carbide residue (CCR), subjected to carbonation, is investigated. The effect of TEA on the carbonation of soil is evaluated by the UCS and the CO₂ mineralization. This study clarifies the influence factors, including the initial water content, TEA dosage, binder constituent ratio, and content. The optimal binder constituent ratio for the strength growth and carbonation efficiency of carbonated soil is approximately 4:4:2 for GGBS, CCR, and MK, respectively. The incorporation of TEA at a low dosage (<0.15 %) enhances the strength of carbonated soil, whereas the high dosages impair the strength. The synergistic effect of TEA and carbonation further improves the strength and compressibility of soil. The soil with 1.5 % TEA carbonated for 7d exhibits a 44.8 % increase in strength compared to that without TEA, which is attributed to a 2.2-fold increase in carbonation efficiency. The addition of TEA accelerates the ion dissolution and CO₂ dispersion, promoting the carbonation reaction in soft soil. Calcite and aragonite precipitate during carbonation, contributing to the strength development of soil. The carbonates phase difference and the pore structure density with different TEA dosages are also demonstrated to be the strength influence factors.

In hard rock tunnel excavation, controlling the blasting profile to prevent overbreak and underbreak is critical for safety and cost-effectiveness. Discontinuities such as joints and faults significantly affect the mechanical properties of the rock mass, and their distribution critically influences the blasting outcomes. This study explores the impact of joint distribution on the tunnel blasting profile through field measurements and numerical simulations. Real-time monitoring of the tunnel face was conducted using the digital twin method, capturing both rock discontinuities and blasting profiles. Field results revealed that overbreak tends to occur at joints outside contour boreholes, where the joints lead the blasting profile diverging from the borehole connection line. To quantify this effect, dynamic finite element simulations were conducted to assess the influence of borehole-joint distances (d = 25 cm, 50 cm, and 100 cm) and intersecting joint angles (α = 60°, 90°, and 120°) on blasting stress wave propagation and rock fracture development. The results demonstrated that joints within the hard rock mass guide and restrain the propagation of blasting stress wave, leading to the formation of a fracture zone induced by the reflected stress wave (the RSW fracture zone). The morphology of the RSW fracture zone closely matched the field blasting profile, validating the numerical simulation results. Furthermore, the borehole-joint distance and the intersecting joint angle were found to govern the extent and geometry of the RSW fracture zone. These findings provide valuable insights for optimizing blasting designs in jointed hard rock masses to control tunnel excavation profiles better.

Accurate extraction of rock mass discontinuity parameters is crucial for stability assessment and engineering safety. High-resolution remote sensing facilitates automated extraction, but its effectiveness relies heavily on precise normal estimation to ensure geometric reliability. Conventional methods struggle to preserve sharp features such as edges and corners, thereby reducing accuracy. To address this, we propose a normal estimation method based on local geometric adjustment that enhances feature extraction while maintaining sharp geometries. The approach consists of four steps: (1) classifying points, (2) applying normal and axial projections, (3) fitting segmentation lines via least squares, and (4) refining normals by optimizing local neighborhoods. The proposed method was evaluated on computer-aided design (CAD) models, real objects, and rock mass point clouds, and benchmarked against eight representative algorithms, including principal component analysis (PCA), 2-Jet PCA, Voronoi-based PCA, PCPNet, neural gradient function (NeuralGF), low rank representation (LRR), normal estimation via shifted neighborhood (NSN) and pair consistency voting (PCV). Experimental results demonstrate that our method achieves superior accuracy and efficiency, significantly improving structural plane extraction and ensuring better preservation of sharp geometric features.

Landfill facilities around the world are designed to protect the environment and public health by using impermeable liner systems that isolate the waste and leachate produced from the waste. However, the functionality of liners has been reported to be significantly compromised by environmental loading due to the seasonal climatic and physico-chemical changes that alter their volume deformation and hydraulic characteristics. Bentonite admixed natural soils are employed as liner materials if they meet the hydraulic conductivity requirement in their as-compacted state. However, limited studies addressed the effects of wet-dry cycles combined with chemical contamination on the volumetric and hydraulic behaviour of bentonite admixed natural soils. In this study, Indian red soil was ameliorated with 10%, 20%, and 30% bentonite by weight, and the mixtures were subjected to alternate wetting and drying cycles using distilled water, 0.4 M NaCl, and 0.4 M CaCl₂ solutions. All red soil-bentonite specimens met the hydraulic conductivity design criterion of 1 × 10^-7 cm/s in their as-compacted states. However, significant variation in hydraulic behaviour was observed at the end of the wet-dry cycles, particularly with chemical contamination. The microstructural examination through scanning electron microscopy (SEM) and mercury intrusion porosimetry (MIP) revealed an increase in macropores volume with wet-dry cycles and increase in the induced osmotic suction, which was found to be a key factor influencing the hydraulic conductivity.