In this study, 50% iron ore tailings (IOTs) were used to prepare the cemented mortar at low economic costs and with great environmental benefits. Basalt fiber (BF) and polyacrylonitrile fiber (PANF) were added to the tailing mortar to improve the comprehensive performance of tailing mortars, including BF (0~0.5%), PANF (0~0.05%) and the combination of them. The results show that the addition of BF and PANF can significantly improve the ultrasonic velocity, uniaxial compressive strength (UCS), split-tensile strength (STS), flexural strength (FS) and toughness of the tailing mortar. A novel finding is that the enhancement of hybrid fibers is much better than single fiber, and the best hybrid fiber combination is B0.25P0.05 (0.25 wt% BF and 0.05 wt% PANF), because this combination not only causes the most considerable increase in strength but also possesses great cost-effectiveness. Compared to the B0P0 group without fibers, the maximum increments of B0.25P0.05 in UCS, STS and FS are 45.74%, 52.33% and 15.65%, respectively. It is evidenced that the improvement in STS is the largest because the fibers have good cracking resistance and bridging effect in the tailing mortar. The scanning electron microscope (SEM) further confirms that too many hybrid fibers will agglomerate and produce more voids, which is harmful to the development of the internal structure. Beyond B0.25P0.05, the hydration products are also reduced due to the decrease in nucleation sites, observed by combining X-ray diffraction (XRD) tests. Therefore, it is suggested that the hybrid fibers containing 0.25% BF and 0.05% PANF should be used in this tailing mortar.

The choice of prediction model plays a decisive role in prediction performance, and hybrid models contribute to enhancing both model performance and prediction accuracy. This study aims to improve the predictive performance of models in time series data processing and further explore urban river water quality prediction models. The hybrid ARIMA-LSTM model was proposed the to simulate and predict water quality indicators in urban rivers of Zhongshan City. The hybrid model firstly used the linear prediction capability of the ARIMA model to extract linear components from water quality data, thereby generating residuals as inputs for subsequent steps. Then the nonlinear prediction of the LSTM model used the results from the previous step as the input to obtain nonlinear data. Finally, the prediction results of the ARIMA model and the LSTM network were integrated to obtain fitting evaluation results, thereby improving the accuracy of prediction outcomes. Furthermore, three traditional individual prediction methods (CNN, LSTM, ARIMA) along with hybrid ARIMA-LSTM model were conducted and compared to evaluate the performance of the hybrid model. The results showed that the hybrid prediction model was superior to individual prediction methods in terms of accuracy. By integrating LSTM's ability to capture complex nonlinear temporal features with ARIMA's proficiency in modeling linear trends and seasonal components, the hybrid model exhibited superior comprehensive performance in characterizing dynamic water quality variation patterns. The study proved that using the hybrid model could effectively improve the accuracy of short-term urban water quality prediction, and that the prediction accuracy of different pollutants varied under the same prediction method.

Rainfall-induced failures in red clay slopes are common, yet the coupled influence of soil structure degradation and rainfall temporal patterns on slope hydromechanical behavior remains poorly understood. This study advances the understanding by investigating a cut slope failure in Yunnan through integrated field monitoring, laboratory testing, and numerical modeling. Key advancements include: (1) elucidating the coupled effect of structure degradation on both shear strength reduction and hydraulic conductivity alteration; (2) systematically quantifying the impact of rainfall temporal patterns beyond total rainfall; and (3) providing a mechanistic explanation for the critical role of early-peak rainfall. Mechanical and hydrological parameters were obtained from intact and remolded samples, with soil-water retention estimated via pedotransfer functions. A hydro-mechanical finite element model of the slope was constructed and calibrated using recorded rainfall, displacement data and failure surface. Six simulation scenarios were designed by combining three strength conditions (intact at natural water content, intact at saturation, remolded at natural water content) with two hydraulic conductivity values (intact vs. remolded). Additionally, four synthetic rainfall patterns, including uniform, peak-increasing, peak-decaying and bell-shaped rainfall, were simulated to evaluate their influence on pore water pressure development and slope stability. Results show remolding reduced hydraulic conductivity 4.7-fold, slowing wetting front advance and increasing shallow pore water pressure. Intact soil facilitated deeper drainage, elevating pressure near the soil-rock interface. Strength reduction induced by structure degradation (water saturating and remolding) enlarged the slope deformation zone by 1.5 times under same hydraulic conductivity. Simulations using saturated intact strength best matched field observations. The results from this specific slope indicate that strength parameters primarily control stability, while permeability affects deformation depth. Simulations considering different rainfall patterns indicate that slope stability depends more critically on the temporal distribution of rainfall intensity than on the total amount. Overall, peak-decaying rainfall led to the most rapid rise in pore water pressure, earliest instability and lowest failure rainfall threshold, whereas peak-increasing rainfall showed the opposite trends. Our findings outline a practical framework for assessing red clay slope stability during rainfall. This framework recommends using saturated intact strength parameters in stability analysis. It highlights the important influence of rainfall temporal patterns, especially those with an early peak, on failure timing and rainfall threshold.