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In this article, a review focusing on the frost resistance of recycled coarse aggregate concrete (RAC) and the bond performance of RAC with rebar under freeze–thaw environment is carried out. The results show that there are still some controversies about the advantages and disadvantages of the frost resistance of RAC, and many influencing factors have not been considered. The mass of the RAC pullout specimens shows a trend of first increasing and then decreasing after freeze‒thaw cycles. The failure modes of the RAC pullout specimens after freeze‒thaw cycles include pullout failure, pullout-splitting failure, splitting failure, and rebar yielding failure. The bond performance of RAC with rebar shows a degradation trend after freeze‒thaw cycles, mainly in the decrease in bond strength and the increase in bond slip. The bond performance of RAC specimens is inferior to that of natural aggregate concrete specimens after freeze‒thaw cycles. The bond strength prediction models and the bond–slip constitutive relation prediction models of RAC with rebar after freeze‒thaw cycles have been summarized to ensure that engineers can better understand their applicability. The bond stress distribution between the RAC and rebar in the anchoring area is not uniform and constantly changes with the number of freeze‒thaw cycles. The degradation mechanism of the bond performance of RAC with rebar after freeze‒thaw cycles is analyzed.

Wasted clay bricks as coarse aggregate of recycled concrete is an effective solution to save energy and reduce CO2 emissions in the construction industry. However, the mechanical properties and frost resistance of recycled brick aggregate (RBA) concrete are inferior to those of ordinary concrete, which limits its widespread application. In this research, the effects of RBA, polypropylene fiber (PPF) and nano-silica (NS) on the mechanical properties and frost resistance of concrete were investigated. The effect of RBA, PPF, and NS on the compressive strength was quantitatively analyzed, and microstructural analysis and fractal dimension calculation of the concrete were performed. The results show that the concrete compressive strength decreased with the increase in RBA replacement rate, and it was effectively improved by adding PPF and NS (PPF-NS). The compressive strength first increased and then decreased with the increase in PPF and NS. The improvement effect of 0.12% PPF and 2% NS on the compressive strength of 50% replacement rate of RBA concrete was most effective. The gray relational degrees between the compressive strength and RBA, PPF, and NS were 0.6578, 0.8297, and 0.5941, respectively. The frost resistance of PPF-NS modified concrete was better than that of ordinary concrete, mainly manifested in its superior apparent phenomena, mass loss, and strength loss. Compared with normal concrete, the microstructure was denser and the fractal dimension of the cross-section was higher for RBA concrete modified with PPF-NS before and after freeze–thaw cycles.

In the rockfall prevention and control project, the reinforced concrete (RC) slab and sand (gravel soil) soil cushion layer are commonly used to form the protection structure, thereby resisting the rockfall impact. Considering that the oversized deformation of the cushion layer under impact load using the finite element simulation cannot converge, this article establishes a numerical calculation model using smoothed particle hydrodynamics–finite-element method coupling (SPH–FEM). First, the standard Lagrange finite-element mesh is established for the whole model using ABAQUS, and then the finite-element mesh of the soil cushion layer is converted to SPH particle at the initial moment of the calculation, and finally the calculation results are solved and outputted. The results indicate that, compared with the results of the outdoor rockfall impact test, the relative errors of the rockfall impact force and the displacement of the RC slab are within 10%, which proves the rationality of the coupling algorithm; moreover, in terms of the numerical simulation, the SPH–FEM coupling algorithm is more practical than the finite element for reproducing the mobility of the rockfall impacting the sand and soil particles. In addition, at an impact speed of less than 12 m·s−1, the cushion layer is able to absorb more than 85% of the impact energy, which effectively ensures that the RC slab is in an elastic working state under small impact energy and does not undergo destructive damage under large impact energy; the peak impact force of the rockfall is approximately linear with the velocity, and the simulated value of the peak impact force is basically the same as that of the theoretical value of Hertz theory; the numerical simulation is good for reproducing the damage process of the RC slab in accordance with the actual situation. The SPH–FEM coupling algorithm is more justified than the FEM in simulating the large deformation problem, and it can provide a new calculation method for the design and calculation of the rockfall protection structure.

Due to recycled aggregate concrete technology, sustainable resource utilization can be achieved, but the weak frost resistance of this type of concrete affects its application in cold regions. Using waste polypropylene fibers as reinforcing materials can improve the mechanical properties and durability of concrete. This study explores the influence of waste polypropylene fiber on the frost resistance durability and microstructure of recycled brick aggregate (RA) concrete. The results show that with the increase in freeze–thaw cycles, the mass of the concrete first increases and then decreases, while its relative dynamic elastic modulus and compressive strength gradually decrease. After 60 freeze–thaw cycles, the maximum mass loss, maximum relative dynamic elastic modulus loss, and maximum compressive strength loss of the RA concrete are 1.73, 45.1, and 73.7%, respectively. Waste fiber (WF) can improve the frost resistance of concrete, as demonstrated by the obvious reduction in mass loss, relative dynamic elasticity modulus loss, and compressive strength loss, which are 0.11, 33.0, and 64.0%, respectively, after 60 freeze–thaw cycles. The action mechanism of WF on the frost resistance of RA concrete is revealed, and the life prediction model of RA concrete with WF under freeze–thaw conditions is established.

The tropospheric Zenith Wet Delay (ZWD) is one of the primary sources of error in Global Navigation Satellite Systems (GNSS). Precise ZWD modeling is essential for GNSS positioning and Precipitable Water Vapor (PWV) retrieval. However, the ZWD modeling is challenged due to the high spatiotemporal variability of water vapor, especially in low latitudes and specific climatic regions. Traditional ZWD models make it difficult to accurately fit the nonlinear variations in ZWD in these areas. A hybrid deep learning algorithm is developed for high-precision ZWD modeling, which considers the spatiotemporal characteristics and influencing factors of ZWD. The Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined in the proposed algorithm to make a novel architecture, namely, the hybrid CNN-LSTM (CL) algorithm, combining CNN for local spatial feature extracting and LSTM for complex sequence dependency training. Data from 46 radiosonde sites in South America spanning from 2015 to 2021 are used to develop models of ZWD under three strategies, i.e., model CL-A without surface parameters, model CL-B with surface temperature, and model CL-C introducing surface temperature and water vapor pressure. The modeling accuracy of the proposed models is validated using the data from 46 radiosonde sites in 2022. The results indicate that CL-A demonstrates slightly better accuracy compared to the Global Pressure and Temperature 3 (GPT3) model; CL-B shows a precision increase of 14% compared to the Saastamoinen model, and CL-C exhibits accuracy improvements of 30% and 12% compared to the Saastamoinen and Askne and Nordius (AN) model, respectively. Evaluating the models’ generalization capabilities at non-modeled sites in South America, data from six sites in 2022 were used. CL-A shows overall better performance compared to the GPT3 model; CL-B’s accuracy is 19% better than the Saastamoinen model, and CL-C’s accuracy is enhanced by 33% and 10% compared to the Saastamoinen and AN model, respectively. Additionally, the proposed hybrid algorithm demonstrates a certain degree of improvement in both modeling accuracy and generalization accuracy for the South American region compared to individual CNN and LSTM algorithm.

In this article, the bond performance of recycled coarse aggregate concrete with rebar after freeze–thaw cycles is analyzed by the ABAQUS finite element method. The result shows that the finite element simulation value of the ultimate bond strength of recycled aggregate concrete specimens is in good agreement with the experimental value, while the agreement between the finite element simulation value of the peak bond slip of recycled aggregate concrete specimens and the experimental value is low; the bond strength between rebar and recycled aggregate concrete increased with the increase of concrete strength and concrete cover depth; the calculation formula for the bond strength between recycled aggregate concrete specimens with different concrete strengths and different concrete cover depth after freeze and thaw cycles are obtained.

In this article, the Ansys Workbench was used to carry out the finite element analysis of 15 kW wind turbine blades with different damaged positions and different damaged degrees. The results show that the change rate of natural frequency, displacement modes, and strain modes of the blades increased with the increase in the damage degree; the change rate of the natural frequency and displacement modes of the blade decreased with the increase in the speed, while the change rate of the strain modes increased; the change allocation ratio of the displacement modes and strain modes after the damage was more obvious at the damage location than other positions, which can be used to locate the damage position of the blade; the change allocation ratio of strain modes is higher than the change allocation ratio of displacement modes when the damage degree is the same, which means that the recognition effect of the strain modes is more significant than that of the displacement modes.

To reduce the hazards of collapse accidents in the construction process and to ensure that the lives, health, and property of construction workers are protected, this study used the theory of safety system engineering to identify the hazards of collapse accidents, analyze the hazards, predict the consequences, evaluate the systemic risks, and evaluate the effects and improve them. At the same time, the risk factors of collapse were evaluated qualitatively and quantitatively by using the analysis methods of fault tree analysis (FTA) and the analytic hierarchy process (AHP). Finally, according to the evaluation results, the main factors causing collapse accidents were found; this provided a reliable and practical basis for the prevention of collapse accidents. Then, according to the decisive factors, corresponding measures were taken in advance to achieve the aim of preventing and controlling collapse accidents. The results show that equipment maintenance, material inspection, and construction site safety management play an important role in preventing collapse accidents.

Ginsenoside Compound K (GCK) is the main metabolite of natural protopanaxadiol ginsenosides with diverse pharmacological effects. Gut microbiota contributes to the biotransformation of GCK, while the effect of gut microbiota on the pharmacokinetics of GCK in vivo remains unclear. To illustrate the role of gut microbiota in GCK metabolism in vivo, a systematic investigation of the pharmacokinetics of GCK in specific pathogen free (SPF) and pseudo-germ-free (pseudo-GF) rats were conducted. Pseudo-GF rats were treated with non-absorbable antibiotics. Liquid chromatography tandem mass spectrometry (LC–MS/MS) was validated for the quantification of GCK in rat plasma. Compared with SPF rats, the plasma concentration of GCK significantly increased after the gut microbiota depleted. The results showed that GCK absorption slowed down, Tmax delayed by 3.5 h, AUC0-11 increased by 1.3 times, CLz/F decreased by 0.6 times in pseudo-GF rats, and Cmax was 1.6 times higher than that of normal rats. The data indicated that gut microbiota played an important role in the pharmacokinetics of GCK in vivo. [ABSTRACT FROM AUTHOR]

In the accommodation field, reasonable pricing is crucial for hosts to maximize their profits and is also an essential factor influencing tourists' tendency to choose. The link between price prediction and findings about the causal relationships between key indicators and prices is not well discussed in the literature. This research aims to identify comprehensive pricing determinants for sharing economy-based lodging services and utilize them for lodging price prediction. Utilizing data retrieved from InsideAirbnb, we recognized 50 variables classified into five categories: property functions, host attributes, reputation, location, and indispensable miscellaneous factors. Property descriptions and a featured image posted by hosts were also added as input to indicate price-influencing antecedents. We proposed a price prediction model by incorporating a fully connected neural network, the bidirectional encoder representations from transformers (BERT), and MobileNet with these data sources. The model was validated using 8380 Airbnb listings from Amsterdam, North Holland, Netherlands. Results reveal that our model outperforms other models with simple or fewer inputs, reaching a minimum MAPE (mean absolute percentage error) of 5.5682%. The novelty of this study is the application of multimodal input and multiple neural networks in forecasting sharing economy accommodation prices to boost predictive performance. The findings provide useful guidance on price setting for hosts in the sharing economy that is compliant with rental market regulations, which is particularly important for sustainable hospitality growth. [ABSTRACT FROM AUTHOR]

In this article, the ABAQUS finite element software is used to simulate the bond behavior of the steel bar in salt–frost-damaged recycled coarse aggregate concrete, and the influence of the steel bar diameter and the concrete cover thickness on the bond strength is investigated. The result shows that the calculated bond–slip curve is in good agreement with the experimental bond–slip curve; the mean value of the ratio of the calculation results of ultimate bond strength to the experiment results of ultimate bond strength is 1.035, the standard deviation is 0.0165, and the coefficient of variation is 0.0159, which proves that the calculation results of the ultimate bond strength are in good agreement with the experimental results; with the increase of steel bar diameter and the concrete cover thickness, the ultimate bonding strength of RAC and steel increases; the calculation formulas for the ultimate bond strength of specimens with different steel bar diameters (concrete cover thickness) after different salt–frost cycles are obtained.