Abstract Crumb rubber concrete (CRC) is increasingly recognized as a sustainable construction material due to its environmental benefits and potential mechanical performance. However, achieving consistent CRC strength remains challenging, partly due to limited attention to crumb rubber aggregate (CRA) gradation, particle characteristics, and replacement techniques. This study introduces a novel Total Gradation Deviation (TGD) metric to optimize the aggregate packing and enhance mix-design consistency—an aspect that is largely overlooked in most existing CRC research. Additionally, by using 10%–30% finely graded CRA (0.6–4.75 mm), this study offers a more integrated analysis than typically reported in the literature. The findings reveal that compressive strength ( f c ′ ) and density reductions are influenced by not just CRA size but also the effective rubber dosage, specific gravity variation, and gradation alignment. These factors, which are often neglected, partially explain the inconsistent f c ′ results reported in the literature across different studies. As a result, conventional replacement methods become problematic, frequently leading to misleading reductions in strength. Whereas the finer CRA particles ( < 1.2 mm ) enhanced the f c ′ by 5–6 MPa (i.e., 16%–30% increment) due to improved packing and reduced porosity, the broader CRA size distributions (0–5 mm) yielded moderate but structurally acceptable strength ( > 20 MPa ) that aligns with international standards. Optimized mix-designs with a sand-to-aggregate ratio (SAR) of 0.48–0.51, CRA-to-cement ratio < 0.12 , and TGD less than 14% achieved 30–35 MPa, yielding up to 36% f c ′ gain compared to untreated CRA mixes reported in the literature. Overall, these findings offer an invaluable practical, low-cost alternative to other chemical pretreatment methods and support a shift toward more standardized, gradation-based CRC mix-designs that lays a foundation for future work on unified performance indices, environmental modeling, and microscale validation.
Complex rotating machinery is essential to safety-critical industrial systems, where undetected faults may trigger cascading failures and pose serious safety risks. Inherent dynamic couplings, multi-source interferences, and time-varying feedback loops lead to complex fault propagation, presenting major challenges for reliable and interpretable diagnostics. Traditional data-driven methods, primarily relying on correlation-based models, often fail to generalize under non-stationary conditions and provide limited understanding of underlying failure mechanisms. To overcome these limitations, this study proposes the Dynamic Causal Mechanism Learning Diagnostics Framework (DCMLDF), designed to enhance system reliability and mitigate fault risks. The framework incorporates a multi-scale temporal encoder, semantics-aware causal graph inference, a causality-guided predictive decoder, and a unified optimization strategy to enable end-to-end modeling of dynamic fault evolution from high-dimensional sensor data. Evaluated on four benchmark datasets, DCMLDF consistently outperforms five representative baselines in both accuracy and stability. More importantly, it reveals system-level causal dependencies that characterize evolving failure pathways, thereby enabling early warning, root-cause identification, and risk-informed decision-making. These findings highlight the framework’s effectiveness in improving predictive reliability and operational safety, offering a scalable solution for prognostics and health management in complex mechanical systems.
The failure of rock material is an instability process caused by progressive accumulation of internal damage. The damage state and evolution pattern of rocks are crucial for early warning of its failure time. Red sandstone was selected as the study object, this study quantitatively revealed its damage acceleration behavior and the key transitional points of the evolution stages under uniaxial compression. By integrating acoustic emission (AE) signals to construct damage variables, a method for predicting and early warning of rocks failure time was established. The results demonstrated that during the critical instability stage, red sandstone exhibited a clear damage acceleration phenomenon, which accompanied by the rapid initiation and propagation of tensile cracks. The key threshold points of the damage response could be determined based on the stress state and damage variables of the specimens. At the first key damage point B, the damage variable defined by AE ringing counts was approximately 0.28 for specimen a, and 0.31 for specimen b. The damage threshold at this first critical point reached about 30 α ranged from 2 to 4. The coefficient of variation for the stress level corresponding to the acceleration point was less than 8
As significant carriers of China’s historical and cultural heritage, grotto temples and rock carvings (GTRC) warrant systematic investigation into their spatio-temporal distribution and spatially associated factors to support effective conservation and management. This study employs GIS spatial analysis, the optimal parameters-based geographical detector (OPGD), and the Multiscale Geographically Weighted Regression (MGWR) model to analyze the spatial distribution, resource richness, and multidimensional spatial associations with national-level GTRC from natural, economic, and cultural perspectives. The results show that: (1) Spatially, national-level GTRC exhibit an uneven pattern characterized by “dense in the central region, more in the east and less in the west”, with high-density areas concentrated along the Henan–Shanxi border. Resource richness shows significant clustering, forming high-value areas in the Central Plains and Sichuan-Chongqing regions. (2) Temporally, the Sui and Tang dynasties were the peak construction period, and the trajectory of the center of gravity shows a shift in distribution direction from “northwest-southeast” to “southwest-northeast”. (3) The OPGD results indicate that economic development exhibits the strongest explanatory power, followed by population density and intensity of religious belief, with economic-religious interaction showing the strongest explanatory power. (4) The MGWR analysis reveals marked regional heterogeneity: in Henan, distribution is more strongly associated with economic, demographic, and religious factors, whereas in the Sichuan-Chongqing region it is more closely related to religious and cultural forces. These findings clarify the multidimensional factors associated with national-level GTRC patterns and provide theoretical and methodological support for their conservation and sustainable utilization.
To address the challenge of inaccurate fatigue life prediction for welds in existing orthotropic steel bridge decks (OSBDs), which stems from incomplete information and insufficient model generalizability, this paper proposes a precise prediction framework that integrates multi-source authentic data and machine learning. Firstly, a U-Net model is employed to achieve automated identification and measurement of the OSBDs’ geometric parameters, resolving the issue of distorted resistance information. Secondly, based on wavelet analysis, strain data from bridge health monitoring is denoised and reconstructed, establishing an authentic stress time-history database. To tackle the scarcity of fatigue test data, a Gaussian Variational Bayesian Network- Attention (GVBN-Attention) prediction model is developed. This model enhances the identification of key features through a self-attention mechanism and quantifies parameter uncertainty using variational Bayesian inference. Experimental results on 192 sample sets demonstrate that the proposed GVBN-Attention model significantly outperforms comparative models, including Gaussian Process Regression (GPR) and Bayesian Neural Networks (BNNs), on key metrics such as the coefficient of determination and root mean square error, exhibiting superior predictive accuracy and generalization capability. Furthermore, SHAP (SHapley Additive exPlanations)-based interpretability analysis reveals the influence mechanisms of input features like stress amplitude and deck plate thickness on fatigue life, validating the rationality of the model's decision-making. This study provides a new method for fatigue life assessment of steel deck welds under data-scarce conditions, characterized by high accuracy, interpretability, and uncertainty quantification.
