Zhongling Zong

This study investigates the time-dependent compressive behavior of seawater sea-sand concrete (SSC) and establishes intelligent prediction models for its evaluation. SSC samples with three strength grades (C30, C40, and C60) were fabricated and tested at curing ages of up to 720 d, accompanied by microstructural characterization. A database containing 344 compressive-strength records was compiled from the present experiments and published literature. Four models were developed and compared, including a back-propagation (BP) neural network, a genetic algorithm-optimized BP (GA-BP) network, a Bagging-BP ensemble model, and a gradient boosting regression tree (GBRT) model. Model interpretability was further examined using SHapley Additive exPlanations (SHAP). The results indicate that SSC exhibits rapid early-age strength gain, while moderate fluctuations may occur during middle and later curing periods. Strength–elastic modulus relationships were established for SSC and showed satisfactory fitting accuracy, with R² values exceeding 0.80. Among the investigated models, GA-BP demonstrated favorable predictive performance under the adopted data split, achieving the lowest root mean square error with R = 0.9413. SHAP interpretation highlighted curing duration and water-to-cement ratio as the dominant factors governing compressive strength. Overall, the proposed GA-BP model can serve as a useful reference for SSC strength prediction and mix-design optimization within the current dataset.

This study investigates the compressive performance of fiber-reinforced polymer (FRP) rebar-reinforced Seawater and Sea Sand Concrete (SSC) columns through an integrated approach combining finite element analysis, theoretical derivation, and machine learning. Finite element models were developed to quantify the influence of key parameters on the ultimate bearing capacity and lateral deflection. The results indicate that the compressive capacity decreases significantly with increasing eccentricity and slenderness ratio. Columns reinforced with steel rebars demonstrated superior load-bearing and anti-lateral displacement capabilities compared to their FRP-reinforced counterparts. A theoretical formula for predicting the compressive capacity was derived; however, it systematically overpredicted the experimental measurements by approximately 36%. To develop data-driven predictive models for the ultimate load capacity of FRP–SSC columns, four machine learning models, backpropagation neural network (BPNN), bootstrap aggregating BPNN (Bagging-BP), genetic algorithm-optimized BPNN (GA-BP), and gradient boosting regression trees (GBRT), were employed. Using sectional dimension, concrete strength, reinforcement parameters, eccentricity, and slenderness ratio as inputs, the validation sets of the models achieved R-values of 0.942, 0.918, 0.933, and 0.990, respectively. Feature importance analysis based on SHAP identified eccentricity as the most influential parameter. Results from this work can help to understand the behavior of FRP–SSC columns under compression.

This study aims to reveal the control mechanism of key geometric parameters (flange thickness and flange edge thickness) of H-shaped cross-section on the bending performance of UHPC piles. Through conducting bending tests, combined with digital image correlation (DIC) technology and finite element simulation, the mechanical behavior was studied, and based on the principal strain field obtained from DIC, a strain field concentration index was proposed. The results show that: as the load ratio increases, the strain field concentration and the peak value of the mid-span principal strain continuously increase, and the crack evolution changes from dispersed development to localized control; near the limit state, the strain field concentration can reach approximately 0.28, and the peak value of the principal strain increases in an increasing trend, approximately 20% or more. Under the specific conditions of this test, in terms of ductility and energy absorption, when the flange thickness is constant, increasing the flange thickness of the web increases the energy absorption of the component by approximately 6% to 10%, while the ductility coefficient decreases by approximately 9% to 15%; when the web thickness is constant, increasing the flange thickness reduces the ductility coefficient by approximately 21% to 25%, and the energy absorption decreases by approximately 27% to 29%. The strain field concentration can effectively reflect the evolution process of the localization of bending cracks in H-shaped UHPC piles and can be used for quantitative analysis of their ductility degradation and energy absorption characteristics. It should be clarified that this study does not claim to isolate the effect of a single parameter.

Adding fibers into cement to form fiber-reinforced soil cement material can effectively enhance its physical and mechanical properties. In order to investigate the effect of fiber type and dosage on the strength of fiber-reinforced soil cement, polypropylene fibers (PPFs), polyvinyl alcohol fibers (PVAFs), and glass fibers (GFs) were blended according to the mass fraction of the mixture of cement and dry soil (0.5%, 1%, 1.5%, and 2%). Unconfined compressive strength tests, split tensile strength tests, scanning electron microscopy (SEM) tests, and mercury intrusion porosimetry (MIP) pore structure analysis tests were conducted. The results indicated that the unconfined compressive strength of the three types of fiber-reinforced soil cement peaked at a fiber dosage of 0.5%, registering 26.72 MPa, 27.49 MPa, and 27.67 MPa, respectively. The split tensile strength of all three fiber-reinforced soil cement variants reached their maximum at a 1.5% fiber dosage, recording 2.29 MPa, 2.34 MPa, and 2.27 MPa, respectively. The predominant pore sizes in all three fiber-reinforced soil cement specimens ranged from 10 nm to 100 nm. Furthermore, analysis from the perspective of energy evolution revealed that a moderate fiber dosage can minimize energy loss. This paper demonstrates that the unconfined compressive strength test, split tensile strength test, scanning electron microscopy (SEM), and mercury intrusion porosimetry (MIP) pore structure analysis offer theoretical underpinnings for the utilization of fiber-reinforced soil cement in helical pile core stiffening and broader engineering applications.

Many engineering disasters are related to water infiltration in cracks. Understanding the water infiltration in multi-layered sandstone cracks is crucial for monitoring and preventing water-related disasters in coal mines. In this study, we utilized the electrical resistivity tomography (ERT) technique to conduct a water infiltration monitoring experiment on the 2d-strata-model with cracks. The electrical resistivity ratio profiles with respect to the background unveiled the existence of three cracks. Model photography demonstrates two cracks of those cracks. The infiltration cracks exhibit distinct shape of a stripe or island chain electrical change ratio anomaly in the resistivity ratio profiles. Electrical resistivity change ratio is associated with the infiltration within the cracks. As the infiltration progresses, the resistivity change ratios relative to the background gradually decreases. This is evident in the reduction in ratios in the original stripe or island chain areas in the electrical resistivity ratio profiles. The diminished range expands, manifesting as an increase in the area of the original stripe or island chain. The infiltration patterns of the cracks can be categorized into three types: a stripe pattern, island chain and stripe pattern, and island chain pattern. The preferential flow paths along the crack are related to both the infiltration time and the volume of infiltration. In the early stages, there are clear preferential flow paths along cracks. However, as infiltration time and volume increase, these preferential flow paths along the cracks become less pronounced and may even disappear. The findings prove that ERT is suitable for monitoring water infiltration along the cracks in multi-layered sandstone in the early infiltration stage. Experiment results monitoring water infiltration cracks on the 2D-model show that the ERT and 2D-model are suitable for studying water infiltration along the cracks. This research can provide valuable reference for preventing engineering disaster.

Water percolation usually occurs in soil, making it highly challenging to simultaneously monitor and verify the water percolation process in real-time. We propose employing electrical resistivity tomography and the photography technique, respectively, to visualize and validate water percolation. An experiment was conducted to monitor water percolation in sand within a transparent glass trough using electrical resistivity tomography (ERT) and photography techniques. The experimental results show that the relatively low-resistivity anomalies in the electrical resistivity profiles and correction electrical resistivity profiles, derived from the experimental data, have a half-ellipse shape, while the wetting areas captured in the photographs exhibit a bulb-shaped pattern. The relatively low-electrical-resistivity anomaly areas delineated by the 2000 Ω·m contour line in the electrical resistivity profiles and the 120 Ω·m contour line in the correction electrical resistivity profiles exhibit a remarkable correspondence with the wetting areas captured in the simultaneous photographs. Our findings show that the ERT and photography techniques are suitable for the real-time monitoring and simultaneous verification of water percolation in sand within a narrow glass trough.