The lack of river sand is becoming increasingly serious. In this study, we consider how to use sea sand to prepare innovative construction and building materials with excellent mechanical and durability properties. Sulphate corrosion causes expansion, cracking and spalling of concrete, resulting in the reduction or even loss of concrete strength and cementation force. In this paper, artificial seawater, sea sand, industrial waste, steel fiber and polycarboxylate superplasticizer were used to prepare ultra-high-performance polymer cement mortar (SSUHPC), and the sulphate corrosion mechanism was investigated. The strength and cementation force of mortar on the SSUHPC surface decreased and flaked off with the development of sulphate erosion, and the steel fiber rusted and fell off. A 3D model was established based on X-ray computed tomography (X-CT), and the results showed that SSUHPC maintained excellent internal structural characteristics despite severe sulphate erosion on the surface. Mercury intrusion porosimetry (MIP), scanning electron microscopy (SEM) and X-ray diffraction (XRD) techniques were adopted to investigate the sulphate corrosion mechanism of SSUHPC. We found a transition zone within 1–5 mm of the surface of SSUHPC. The Vickers hardness of mortar in this area was increased by 5~15%, and the porosity was reduced to 3.8489%. Obvious structural damage did not occur in this area, but a high content of gypsum appeared. UHPC prepared with seawater sea sand was found to have better sulphate resistance than that prepared with freshwater river sand, which supports the development and utilization of sea sand in concrete.

There are abundant sea-sand resources on the earth. Traditional sea-sand concrete faced various problems relating to insufficient anticorrosion ability. In this paper, artificial seawater, sea sand, industrial waste, steel fiber, and polycarboxylate superplasticizer were used to prepare ultra-high-performance polymer cement mortar (SSUHPC). At the same time, freshwater river-sand ultra-high-performance polymer cement mortar (FRUHPC) with the same mixing ratio was prepared for comparative study. The compressive strength of SSUHPC reached 162.1 MPa, while the that of FRUHPC reached 173.3 MPa, which was slightly higher. Meanwhile, SSUHPC showed excellent anticorrosion characteristics in terms of carbonization, frost resistance and chloride resistance, and especially for sulfate resistance. The composition of SSUHPC was separated into three parts: mortar, pore and steel fiber, and the performance difference mechanisms of SSUHPC and FRUHPC were investigated by X-ray computed tomography (X-CT), mercury intrusion porosimetry (MIP), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The hydration degree of mortar in SSUHPC was higher, with higher content of CSH and CH, and its better optimized gel pore characteristics gave SSUHPC better corrosion resistance. The mechanical properties of SSUHPC were slightly poor due to the uneven dispersion of steel fibers and air pores, with an- air pore porosity of 1.52% (above 200 μm) that was twice that of FRUHPC (0.6%). In this paper, the mechanics and anticorrosion performance of ultra-high-performance polymer cement mortar prepared with seawater sea sand were comprehensively evaluated, and the mechanism of performance difference between SSUHPC and FRUHPC was revealed, conducive to the targeted improvement of sea sand concrete.

Concrete is a multi-phase, porous system. The pore structure has an important influence on the properties of the concrete. In this paper, a kind of fiber reinforced mortar was prepared with desert sand and its pore structure was studied. The MIP technique was used to investigate the pore structure characteristics between 1 nm and 500 μm (in diameter). Meanwhile, the μX-CT technique was used to study the pore structure characteristics above 200 μm. It was found that the total porosity tends to decrease first and then increase as the dosage of desert sand increased. The porosity decreased gradually from the upper to bottom area inside the sample, and the diameter of the air voids near the upper area became larger. After curing for 28 days, the compressive strength of fiber reinforced mortar reached the maximum when the content of desert sand was 50%. In conclusion, the appropriate amount of desert sand can reduce the porosity of the fiber reinforced mortar to some extent and the number of large size air voids can be significantly reduced, which improves the pore structure and the mechanical properties of the fiber reinforced mortar.

We present a straightforward emulsion-induced interfacial anisotropic assembly method for in- situ synthesis of bowl-shaped, self-encapsulated mesoporous polydopamine (BMPDA) nanocontainers (M-M@P) loaded with 2-mercaptobenzimidazole (2-MBI). Results showed that the loading capacity of the bowl-shaped mesoporous polydopamine reaches 24 wt.%. The M-M@P exhibits a cumulative MBI release of 91.61% after immersion in a 3.5 wt.% NaCl solution at pH = 2 for 24 h, accompanied by a corrosion inhibition efficiency of 95.54%. Additionally, the acid-responsive M-M@P not only enables controlled release of MBI but also synergistically promotes the formation of a protective film on the carbon steel substrate via the chelation of PDA-Fe³⁺, thereby enhancing the self-healing performance of waterborne epoxy coatings.

High toxicity is the main reason for the limited application of traditional corrosion inhibitors. Herein, it is critical to find a green, efficient, and long-term stable alternative substitute for the hazardous and conventional corrosion inhibitor. Ambrosia trifida L is widely distributed in fields and riverside wetlands as an invasive plant in China. According to the concept of turning waste into treasure, the extract of Ambrosia trifida L leaves (ATL) has the potential to address this issue due to its natural origin and abundant presence of heterocyclic organics. Therefore, ATL, as a green corrosion inhibitor, is prepared for the first time via a simple water-based extraction method. FT-IR (Fourier transform infrared spectroscopy) and UV-Vis (UV-visible) indicate that ATL extract contains abundant heterocyclic organics with conjugated structures, which exhibit the potential to become a high-efficiency inhibitor. Notably, the active sites of ATL molecules and their interaction with Q235 steel at the molecular/atomic level are revealed via theoretical calculations. The highest E_binding value observed for the major components in the ATL extract is 259.66 kcal/mol, implying a significant adsorption capacity. The electrochemical results verify that microdose ATL extract can prominently inhibit steel corrosion, and the highest inhibition efficiency (η) is 97.5% (1000 mg/L). Following immersion for 24 h, the η value is enhanced to 99.0%, indicating a reliable and long-term ATL extract protection film is formed on the steel surface in harsh acidic solutions. The results of the weight loss, SEM (scanning electron microscope), and LSCM (laser scanning confocal microscopy) are consistent with the above conclusions. Finally, this study anticipates providing theoretical support for developing novel green plant extract inhibitors and aiding in their application in industrial pickling environments.

Concrete is the material of choice for constructing hydraulic structures in water-related buildings, and its mechanical properties are crucial for evaluating the structural damage state. Machine learning models have proven effective in predicting these properties. However, a single machine learning model often suffers from overfitting and low prediction accuracy. To address this issue, this study introduces a novel hybrid method for predicting concrete compressive strength by integrating multiple soft computing algorithms and the stacking ensemble learning strategy. In the initial stage, several classic machine learning models are selected as base models, and the optimal parameters of these models are obtained using the improved metaheuristic-based gray wolf algorithm. In the subsequent stage, the lightweight gradient boosting tree (LightGBM) model and the metaheuristic-based optimization algorithm are combined to integrate information from base models. This process identifies the primary factors affecting concrete compressive strength. The experimental results demonstrate that the hybrid ensemble learning and heuristic optimization algorithm achieve a regression coefficient of 0.9329, a mean absolute error (MAE) of 2.7695, and a mean square error (MSE) of 4.0891. These results indicate superior predictive performance compared to other advanced methods. The proposed method shows potential for application in predicting the service life and assessing the structural damage status of hydraulic concrete structures, suggesting broad prospects.