By introducing the local density factor into the taper equation， five Larix olgensis plantations in Mengjiagang Forest Farm， Jiamusi City， Heilongjiang Province， were scanned by TLS. The tree height， diameter at breast height and diameter at different relative heights of Larix olgensis were extracted from the collected point cloud data. Based on Kozak variable exponential taper equation， a taper equation with local density factor was constructed， and the fitting result was R_{adj}^{\mathrm{2}}=0.935， RMSE=0.735 6， Bias=0.869 3. The research showed that： the taper equation model with local density factor had good fitting accuracy， Compared with the traditional Kozak taper equation model and the taper equation model with global density factor， the taper equation model with local density factor had better fitting effect. The calculation range of the local density factor of the Larix olgensis taper equation was positively correlated with tree height and diameter at breast height. The experimental results can provide a theoretical basis for scientific and reasonable timber production in Mengjiagang Forest Farm， and preliminarily reveal the influence of local density factor on the trunk shape of Larix olgensis.

To study the identification of burned areas and post fire vegetation restoration in the Daxing'an Mountains region， based on Landsat TM remote sensing images from 2006 to 2020， Google Earth Engine was used to write code. The research background was the 2006 forest fire in the Nayuan Forest Farm in the Songling District of the Daxing'an Mountains region. The differential normalized burned ratio （dNBR） data was used to identify the burned areas， and the severity was classified into mild， moderate， severe， and extremely severe levels. Based on the Enhanced Vegetation Index （EVI） values of burned areas， methods such as univariate linear regression analysis， Mann-Kendall mutation test for climate diagnosis and Theil-Sen media trend analysis for treud analysis were used to analyze the vegetation restoration characteristics of burned areas from 2006 to 2020， and to explore the process of vegetation restoration in the Daxing'an Mountains region. The results showed that， 1）Based on dNBR， the burned areas in the study area was 2 488.7 hm²， with 23.5%， 9.6%， 35.2%， and 31.7% of the areas affected by mild， moderate， severe， and extremely severe fires， respectively. Severe and extremely severe areas of excessive fire were distributed in the western and eastern parts of the burned area， and the severity of excessive fire gradually decreased from the central to the southern and northern parts. The EVI values decreased by about 30%， 40%， 58%， and 67% compared to before the fire， respectively. 2）The recovery rate of EVI in forest burned areas with different intensities showed extremely severe， severe， moderate， mild. During the vegetation restoration process， the EVI value of the burned areas gradually increased. Mild and moderate burned areas can recover 6-8 years after the fire， while the recovery of severely burned areas required 14 years. 3）During the restoration process of burned areas， there were fewer EVI mutation points in forested areas compared to grasslands， indicating stronger stability of forest ecosystems compared to irrigated grasslands. There were also certain differences in the mutation situation of forest burning sites with different intensities， and the mutation time point in the control area lagged behind the burning sites.

In order to explore the potential of L-band full-polarization SAR data to estimate forest aboveground biomass （AGB）， five polarimetric scattering ratio parameters （R₁， R₂， R₃， R₄， R₅） were constructed based on the canopy-ground scattering component of Unmmaned Aero Vehicle Synthetic Aperture Radar （UAVSAR） data of the AfricaSAR project. Calculating the Radar Vegetation Index （RVI）， and 21 polarimetric decomposition parameters were extracted by four model-based decompositions， including six-component and seven-component decomposition. Finally， all features were merged and the random forest feature importance was used to screen out the optimal feature combination， and random forest （RF）， support vector machine regression （SVR）， K-nearest neighbor regression （KNN） were used to estimate forest AGB of Lope， The Gaboneses Repbulic， Africa， with different feature combinations. The results showed that the polarimetric scattering ratio parameters， bulk scattering （Vol） and RVI had high sensitivity to forest AGB， and the correlation between R₂ and AGB was 0.823， and the optimal feature combination was Vol， polarimetric scattering ratio parameters and RVI. Machine learning models with different feature combinations had shown good performance， the coefficient of determination （R²） of the machine learning model based on the polarimetric decomposition parameters was bigger than 0.800， and the root mean square error （RMSE） was less than 88.000 Mg/hm²， and the best effect was the RF model based on the optimal feature combination， which increased R² by 0.144 and decreased RMSE by 30.327 Mg/hm² compared with the polarimetric decomposition parameters alone. The polarimetric scattering ratio parameter had certain potential in the estimation of forest AGB， the introduction of RVI improved the accuracy of the model， the model-based decomposition was suitable for forest AGB estimation， and the machine learning model based on feature screening can better invert forest AGB， and there was no obvious saturation point when the AGB reached 400 Mg/hm².

In order to explore the effect of chemical treatment on adventitious bud formation of P. koraiensis top pruned seedlings， 5-year-old P. koraiensis bare root seedlings were used as experimental materials. After top pruning at the beginning of the growing season， different chemicals were sprayed every 15 days （GA₃ （200， 500， 1 000 mg/L）， 6-BA （100， 300， 500 mg/L）， KMnO₄ （0.1%， 0.2%， 0.3%））. Using spray water treatment as a control， the formation of adventitious buds of P. koraiensis seedlings treated with different chemicals was investigated. The results showed that the chemical treatment （except 1 000 mg/L GA₃ and 0.1% KMnO₄） increased the incidence of adventitious buds of P. Koraiensis top pruned seedlings. Compared with the control， the sprouting rate increased by 5.72% to 48.58%， and the number of sprouting strip increased by 15.37% to 175.94%. The suitable concentration of GA₃ to promote P. koraiensis top pruned seedlings was 200 mg/L， which can increase the sprouting rate by 34.28%， and the number of sprouting strip by 33.63%.The suitable concentration of 6-BA to promote P. koraiensis truncated seedlings was 500 mg/L， which can increase the sprouting rate by 48.58%， and the number of sprouting strip by 175.94%. The suitable concentration of KMnO₄ to promote P. koraiensis top pruned seedlings was 0.2%， which can increase the sprouting rate by 42.85%， and the number of sprouting strip by 95.18%. The better effect of promoting adventitious bud formation can be obtained by using 500mg/L 6-BA to treat P. koraiensis top pruned seedlings.

By exploring the species diversity and soil physical and chemical properties of four typical forest types （natural broad-leaved mixed forest， artificial broad-leaved mixed forest， artificial broad-leaved pure forest， and artificial coniferous and broad-leaved mixed forest） in Zengcheng District， Guangzhou City， based on their origins and stand types， we analyzed their species diversity differences and the relationship between them and soil environmental factors， providing a theoretical basis for improving the quality of natural forests accurately. Using the typical quadrat survey method and statistical analysis methods， we calculated the Magalef richness index， Shannon-Wiener index， and Simpson dominance index to analyze the differences in species composition， dominance， and species diversity of the arbor and shrub layers in typical forest communities in Zengcheng District， and conducted correlation analysis with soil environmental factors. A total of 193 species belonging to 82 families and 153 genera were surveyed in the study area. The dominant species and their importance values of the four forest types were as follows： natural broad-leaved mixed forest （Machilus thunbergii， 6.36%）， artificial broad-leaved mixed forest （M. thunbergii， 5.52%）， artificial broad-leaved pure forest （Eucalyptus， 29.54%）， and artificial coniferous and broad-leaved mixed forest （Canarium， 4.73%）. The dominant species in natural and artificial broad-leaved mixed forests showed an inverted J-shaped distribution， while artificial coniferous and broad-leaved mixed forests and broad-leaved pure forests showed a normal distribution. The four forest types had significant differences in species diversity， soil nitrogen， and phosphorus content. Among them， the artificial broad-leaved pure forest had the lowest species diversity in the arbor layer and the highest species diversity in the shrub layer. The soil nutrient content of the four forest types varied significantly， and there was a significant correlation between species diversity and soil physical and chemical properties. Forests of different origins and stand types in the region require different management measures.

To reveal the evolution characteristics of vegetation cover in Nanchang City and identify the main climate drivers affecting its change trends， in order to provide guidance for its long-term stable and benign development and active response to the subsequent climate change. In this paper， NDVI monthly data and ten meteorological driving factors such as temperature， precipitation and pressure of Nanchang City from 2000 to 2020 were used to study the importance of different drivers by using single sample K-S test， Friedmann test， Kendall harmony coefficient test and random forest analysis. The results showed that， 1） the NDVI of Nanchang City showed a fluctuating downward trend from 2000 to 2020， the peak appeared in 2000， while the trough occurred in 2010. 2） The overall vegetation cover of the city showed a spatial distribution pattern of high perimeter and low center， with the NDVI values in the north and the west being relatively high and decreasing at a slow rate， and the NDVI values of the central areas of the city such as the East Lake District and the Castle Peak Lake District being relatively low and with an obvious downward trend. 3） The great average value of the vegetation cover appeared in August each year， which was most significantly affected by temperature and least affected by wind direction， with a certain time lag influenced by precipitation， so the study of vegetation cover change should be determined by combining the driving effect of multiple elements.

In order to understand the non-structural carbon （NSC） content， nitrogen （N） phosphorus （P） potassium （K） ecological stoichiometric characteristics and their responses to tending thinning in different age stages of Populus×xiaohei， and to provide a basis for scientific management of Populus×xiaohei， the young and middle Populus×xiaohei plantation in Hongqi Forest Farm of Daqing City， Heilongjiang Provinc was studied， the effects of tending thinning on NSC content and NPK ecological stoichiometry in each organ of the plantation were investigated and analyzed by setting up thinning plots with thinning intensity of 25% （based on number of trees）. The results showed that there were different NSC content and NPK ecological stoichiometric distribution characteristics of two kinds of Populus×xiaohei plantations. The contents of NSC in the leaf， branch and root of middle-aged forest were decreased by 31.76%， 21.19% and 38.98% compared with those of young forest， respectively （P < 0.05）. The N content of leaf in the middle-aged forest was increased by 11.85% compared with that in the young forest （P < 0.05）. Compared with young forest， the P content of branch， stem and root of middle-aged forest decreased by 17.57%， 35.85% and 60.11%， respectively （P < 0.05）， and the K content of leaf， branch， stem and root decreased by 21.05%， 34.67%， 22.99% and 46.22%， respectively （P < 0.05）. Tending thinning significantly affected the NSC content in the stem and root of young forest， and the NSC content in the branch and roots of middle-aged forest （P < 0.05）. NSC content in stem of young forest increased by 29.35%， while NSC content in root decreased by 40.37%. The contents of NSC in branch and root of middle-aged forest increased by 12.81% and 33.51%， respectively. Tending thinning significantly affected N content in organs， K content in branch， P content in stem and K content in leaf and root of young forest （P < 0.05）. After tending， N content in leaf， branch， stem and root of young forest increased by 5.77%， 15.68%， 53.71% and 10.85%， respectively， K content in branch increased by 36.90%， and P content in stem decreased by 19.63%. The K content in the leaf and root of the middle-aged forest increased by 20.86% and 31.69%， respectively. The NSC content in leaf was negatively correlated with N content （P < 0.01）， and positively correlated with K content （P < 0.05）； NSC content in branch was negatively correlated with N content， positively correlated with P content （P < 0.05）， and significantly positively correlated with K content （P < 0.01）； NSC content in stem was significantly positively correlated with N content （P < 0.01）； NSC content in root was significantly negatively correlated with N content， and significantly positively correlated with P content （P < 0.01）. The above results showed that the distribution pattern of NSC and N， P and K elements in the organs of young and middle-aged Populus×xiaohei were significantly different when the stands were not tended. The carbon resources of the young forest were more allocated to the root system to promote its growth and extension， and the middle-aged forest was more invested in the growth and competition of the aboveground part of the tree. The distribution pattern of NSC， N， P and K in each organ was changed by tending thinning. The proportion of carbon resources in the aboveground and underground parts of the two kinds of standing Populus×xiaohei changed after tending. Tending thinning of middle age Populus×xiaohei forest increased the carbon sink and improved the P limit.

This study focused on the natural birch forest （Betula platyphylla） in the Daxing'an Mountains of Yakeshi Forestry Administration in Inner Mongolia. Using 198 sample plots， the relationship between carbon density in the natural birch forest stand and various stand variables was analyzed. A basic model for predicting carbon density was established， incorporating site conditions （grass birch forest， rhododendron-vaccinium birch forest， hazelnut birch forest） as dummy variables to predict stand carbon density across different forest types. This paper offered insights and methodologies for constructing carbon density models and advancing forest carbon sequestration in forestry research. The results showed that the determination coefficient （R²） for the basic model of carbon density in the natural birch forest was 0.703， root mean square error （R_MSE） was 8.615 t/hm^-2， Akaike information criterion （AIC） was 841.206， and Bayesian Information Criterion （BIC） was 851.071. After site conditions were introduced as dummy variables， R² increased to a maximum of 0.818， and RMSE were all less than or equal to 8.241 t/hm^-2， indicating that the model had good stability and the predicted parameters were more accurate. AIC for the dummy variable model was less than or equal to 541.431， and BIC was less than or equal to 550.320. The dummy variable model can reflect the change of carbon density under different site conditions， and both the fitting and testing of the model show that it is suitable for the prediction of forest carbon density in the study area， which provides a reference for the estimation of natural birch forest carbon density.

Pine wilt disease， as a world quarantine disease， seriously endangers pine plants and threatens forest ecological security. However， there are currently no effective measures to control and eliminate it. To suit people's needs for supplying diverse ecological services to the forest， high-intensity （60% logging intensity） and low-intensity （15% logging intensity） thinning measures were used in Pinus massoniana forests to harvest wood infected with pine wilt disease. The alterations in bacterial and fungal composition in the soil of P. massoniana forests impacted by the disease were analyzed using high-throughput sequencing. The objective was to investigate the alterations in the organization of the soil microbial community in P. massoniana forests impacted by thinning-induced pine wilt disease and assess the effects on the soil microenvironment. The soil bacterial and fungal populations of P. massoniana woods were investigated using the high-throughput sequencing technique. The findings demonstrated that the bacterial and fungal populations in the soil varied significantly among the three stands. The high thinning measure forest group （H-CK） had the greatest Shannon diversity index and Chao1 index， and the diversity and richness of the soil microbial community in the H-CK group were considerably higher than those in the low thinning measure forest group （L-CK） and control group （CK）. Actinobacteriota was the marker bacterial community and Leotiomycetes was the marker fungus community in the L-CK group. In the H-CK group， Chloroflexi was the marker bacterial community and Dothideomycotes was the marker fungal community. The high-intensity thinning H-CK group's soil organic matter content rose by 13% as compared to the control group， according to an analysis of soil physical and chemical properties. The H-CK group's total nitrogen content increased to 1.12 g/kg and its alkaline nitrogen content was 64.15 mg/kg as soil organic matter buildup increased. The forest's soil fertility was enhanced as a result， with a significant increase in both the total and accessible nitrogen content of the soil （P<0.05）. Steadily impoving forest quality， strengthening the disease resistance and boosting the ecological function of the current， pure Pinus massoniana forest that has been harmed by pine wilt disease by progressively transforming it into a multilayer， unevenly aged needle broad-leaved mixed forest.

In order to preliminarily evaluate the early growth of Pinus koraiensis and select excellent families， taking 117 full-sib family seedlings obtained by controlled pollination in the Affinity Seed Garden of Wangqing Forestry Branch of Changbaishan Forest Industry Group as materials， we observed their growth for three consecutive years and conducted a comprehensive evaluation after establishing a progeny measurement forest in the Affinity Seed Garden in the 91st forest class， class 4. The differences in seedling height and ground diameter of the offspring of P. koraiensis full-sib family lines from 6 to 8 years old were highly significant among family lines， and the interaction between family lines and district groups was highly significant （P<0.01）； the average seedling height at 6 years old was 39.79 cm， the average annual growth rate at 8 years old was 25%， the average ground diameter at 6 years old was 11.11 mm， the average annual growth rate at 8 years old was 34%， and the phenotypic coefficient of variation of each trait ranged from 15.84% to 25.43%. The heritability of each trait was high， which was conducive to the further selection and evaluation of the family line. Using the Breggin multiple trait analysis method to select superior family lines， 24 superior family lines were from 117 familes with a benchmark of 20%. The excellent family lines selected by growth traits can provide the basis and materials for renewing the seed orchard generation and promoting the superiority and expansion of P. koraiensis.

When the tractor is undertaking the forestry carrying operation， due to the complex road conditions in the forest， it mostly runs at a low speed， has a high steering demand， and often has a load unit， so it pays more attention to its driving stability and steering sensitivity. In this paper， CarSim software is used to build the physical model and load unit model of the tractor， the control strategy of the tractor is designed based on Simulink software combined with the two-degree-of-freedom dynamic model， and the two-degree-of-freedom model of tractor is established. The braking torque feedback control is carried out by combining the yaw rate and the side deflection angle of centroid with the fuzzy control theory. In addition， the proportional control strategy of front and rear wheel angle is introduced according to the four-wheel steering mode of the tractor. Under the CarSim-Simlulink co-simulation platform， combined with the complex road conditions of forestry operations， the low-speed driving and steering experiments of tractor under different control strategies are carried out. The results show that the control strategy adopted in this paper can effectively reduce the steering radius of the tractor under complex road conditions， increase the steering speed， and limit the side deflection angle of centroid to a small range. The tractor has high stability， the steady state value of yaw rate is closer to the ideal state， and the steering is more sensitive.

In order to investigate the self-heating thermal runaway problem of ternary lithium-ion battery used in a certain forest plant protection machine， the structural composition of the battery cell， the heat generation mechanism of each side reaction and the three heat transfer modes of heat radiation， heat conduction and convective heat transfer between the battery cell， end plate， heat insulation pad， box body and the environment were first explored. Then based on the thermal runaway modeling method of the equivalent circuit model， the thermoelectric characteristic model of the 280 Ah large-capacity battery module was established in the Amesim one-dimensional simulation software by the physical parameters of the battery cell， the side reaction mechanism， and the exothermic heat transfer model.The heat transfer characteristics of the battery during self-heating thermal runaway and the path of thermal runaway propagation in the module were analyzed and simulated.Finally， according to the national standard GB 38031—2020， the self-heating experiment of the battery module was carried out to obtain the change of the cell temperature with time， and the specific position of the thermal runaway of the trigger cell was confirmed by CT scanning. The simulation and experimental results showed that the established model had high accuracy and reliable performance.The maximum error of the thermal runaway temperature of the cell was 11.3%， and the maximum error of the thermal runaway trigger time was 4.2%.It can provide technical reference for the design and development of battery thermal management， such as the prediction of thermal runaway temperature and the safety of preventing thermal diffusion of battery pack.

For the problems of complex forest canopy images and poor segmentation accuracy， a novel canopy image segmentation method based on improved northern goshawk algorithm （INGO） by using multi-strategy fusion was proposed. Firstly， a random reverse learning strategy was introduced for the initialization of northern goshawk to increase population diversity and improve search efficiency. Adaptive weight factors were added in the exploration phase of the northern goshawk to improve the algorithm's search ability and accelerates its convergence speed. Non-linear convergence factors were introduced in the development stage of northern goshawk to balance global search and local development capabilities. Secondly， multiple-threshold Kapur entropy was employed as the fitness function， the improved algorithm was tested using eight benchmark functions， and the results demonstrated that it effectively enhanced both accuracy and search speed. Finally， the improved algorithm was applied to threshold segmentation research on forest canopy images， and comparative analysis was conducted on fitness value， peak signal-to-noise ratio of forest image segmentation （PSNR）， structural similarity （SSIM）， and feature similarity （FSIM）. Experimental results indicated that the improved algorithm can obtain more accurate forest canopy segmentation thresholds and higher segmentation accuracy.

In order to predict the sealing quality of the differential assembly of forestry timber tranpsort vehicle trucks beforehand and improve the quality of its products and the assembly qualification rate， a prediction model based on grey correlation analysis algorithm combined with particle swarm （PSO） optimized BP neural network is proposed. The key assembly process parameters affecting the sealing quality of differential assembly screened out by the grey correlation analysis algorithm are taken as input variables， and the leakage value of differential assembly is taken as output variable to create a prediction model based on particle swarm algorithm optimized BP neural network， and the results show that the PSO-BP prediction method simplified by the grey correlation analysis obtains the smallest average relative error of 1.18%. On this basis， PyQt5 GUI library is applied to develop a differential assembly leakage value prediction system. The results of the study can provide a theoretical basis for the prediction of differential assembly sealing quality.

Aboveground biomass reflects the growth of vegetation and the magnitude of carbon storage， and the accuracy of this parameter is crucial for carbon cycle research and climate change mitigation. In this study， a new idea of using deep learning to realize banana canopy detection segmentation and aboveground biomass estimation was proposed. Firstly， the deep learning algorithm YOLOv8s-seg was used as the basic framework improvement， and UAV remote sensing images were applied to realize banana canopy detection segmentation. Then， the canopy coverage area of banana trees was extracted， combined with the measured aboveground biomass data for fitting， and the aboveground biomass estimation model of banana was established by linear regression， K-Nearest Neighbor （KNN）， support vector machine， random forest and XGBoost （eXtreme Gradient Boosting）. Finally， the model estimation results were compared and analyzed to determine the optimal model. The results showed that the improved YOLOv8s-seg model can quickly and effectively detect and segment banana canopies. Through verification， it was found that the fitting effect and prediction error of the aboveground biomass estimation model based on XGBoost were better than those of other models， with R² of 0.881 4， root mean square error （RMSE） of 231.37 kg， and mean absolute error （MAE） of 140.47 kg， which could predict the aboveground biomass more accurately and was more suitable for the inversion of the aboveground biomass of bananas， which further verified the feasibility of using UAV and deep learning methods to extract canopy information to estimate the aboveground biomass.

The corrosion resistance of modification rubberwood with micro sized copper zinc compounds（MCZ） can reach the national level I corrosion resistance standard， but in order to adapt to complex environmental applications， the leaching resistance and dimensional stability need to be further improved. Propylene glycol was used to modify MCZ， and then combined with tannin-boric acid for synergistic treatment of rubberwood. The results showed that when the rubberwood was impregnated with PG-MCZ and modified with tannin-boric acid with a mass concentration of 1%， the leaching resistance of the treated wood was the best. At this point， the modified rubberwood had the best anti moisture expansion and anti drying shrinkage effects， and the best dimensional stability. Although the addition of propylene glycol slightly weakened the corrosion resistance of rubberwood， it can still meet the national level I corrosion resistance standard and improve flame retardancy and smoke suppression of rubberwood. This study has a positive effect on promoting the development and utilization of environmentally friendly wood preservatives.

This study employed a microwave-assisted ionic liquid extraction method to extract three alkaloids （berberine， palmatine， and berbamine） from the bark of Phellodendron amurense Rupr.. Initially， the impact of the type of ionic liquid and liquid-to-solid ratio on extraction efficiency was investigated through single-factor experiments. Subsequently， the effects of ionic liquid concentration， extraction time， and temperature on extraction efficiency were further explored. The Box-Behnken response surface methodology was then applied to optimize and obtained the optimal extraction process， which was subsequently compared with traditional extraction methods. The research findings indicated that ［Bmim］［Br］ solution exhibited the highest extraction efficiency for the alkaloids. The optimal extraction conditions included an ionic liquid concentration of 0.31 mol/L， a temperature of 94°C， and an extraction time of 15 minutes. Under these conditions， the yields of berberine， palmatine， and berbamine were 0.047%±0.003%， 0.421%± 0.011%， and 1.150%±0.028%， respectively. Compared to the traditional water heating reflux method， the extraction efficiencies were 9.3%， 12.8%， and 23.6% higher， respectively. This study demonstrates that the use of microwave-assisted ionic liquid extraction technology can efficiently extract alkaloids from the bark of Phellodendron amurense Rupr.， providing a theoretical basis and empirical support for the extraction of active components from Phellodendron amurense Rupr..

Based on the orthogonal test design method， the expansive mudstone in the cutting of Jingxin expressway in Zhangjiakou city was tested， and the relationship between the expansion rate of mudstone samples under different overlying loads and the soaking time， initial water content and compaction degree was studied， and the sensitivity of various factors was analyzed by the range analysis method. The results showed that the curve of expansion rate with the soaking time presented an S-shaped trend， which can be roughly divided into initial expansion stage， accelerated expansion stage and slow expansion stage. Under the same initial water content， the expansion rate increased with the increase of the compaction degree. Under the same compaction degree， the expansion rate decreased with the increase of initial water content. The range analysis method showed that the soaking time of the sample was the main factor affecting the expansion rate. Water content and compaction degree were the secondary influencing factors， and the influence of water content on the sample expansion rate was obvious under small load， and the load continued to increase， the compaction degree exceeded the influence of water content on the sample expansion rate.

In order to further explore the influence of freezing and thawing on the deformation and strength of loess under the actual working conditions， this paper carries out the compression test of loess after experiencing 0， 1， and 4 pressurized freeze-thaw cycles， respectively. The results show that： after experiencing pressurized freeze-thaw， the frozen loess mostly shows brittle damage mode， and the stress-strain curve is strain-hardening curve. The strength of loess decreases by about 35% after one pressurized freeze-thaw cycle， and the strength decreases by 51% after four pressurized freeze-thaw cycles， which is a nonlinear negative correlation. In the case of the same number of freeze-thaw cycles， with the increase of the water content， the peak stress will be reduced gradually and the strength in the range of water content decreases by nearly 77%. The uniaxial compressive strength of frozen loess shows a significant increase with the increase of initial dry density and the decrease of test temperature， when the temperature decreases from -10℃ to -15℃， the strength increases 1.5 times， and the strength increases 2 times when the temperature decreases from -15℃ to -20℃. Finally， based on the Gaussian function， a strength model of frozen loess is constructed considering the influence of pressurized freeze-thaw cycles， which can better reflect the strain hardening situation of frozen loess.

Direct-injection SBS modified asphalt has advantages such as easy quality control， low cost， and minimal pollution. It can address the issue of unstable quality in conventional wet-process SBS modified asphalt and has been gradually widely applied in recent years. This study investigated the aging resistance performance of direct-injection SBS modified asphalt at different dosages through short-term aging. By conducting temperature scanning tests， mid-temperature fatigue performance tests， low-temperature rheological property tests， and atomic force microscope tests on the aged direct-injection modified asphalt， the aging resistance performance of the SBS modified asphalt was studied from macroscopic to microscopic scales. The macroscopic experimental results indicated that the direct-injection SBS modified asphalt exhibited an increase in elastic components and fatigue life after short-term aging. During the aging process， the Guolu direct-injection SBS modified asphalt showed a secondary development phenomenon. The microscopic test results revealed that， after short-term aging， both conventional wet-process SBS modified asphalt and Liaohe direct-injection SBS modified asphalt exhibited reduced roughness due to changes in the content of the four components and the degradation of SBS. In contrast， the Guolu direct-injection SBS modified asphalt exhibited increased surface roughness due to further swelling development， consistent with the conclusions drawn from the macroscopic tests.

In order to solve the problems in predicting the asphalt aging behavior， such as the large performance deviation between simulated aging asphalt and actual aging asphalt， the lack of scientific evaluation index easy to promote in the practical engineering， properties of natural aging SBS （Styrene Butadiene Styrene Triblock Copolymer） modified asphalt from an in-service expressway were tested by Asphalt Binder Quality Test System （ABQT）. Then， some indexes， including maximum creep deformation， elastic recovery rate， loading deformation rate coefficient and rebound deformation rate coefficient， were proposed to evaluate the changes of asphalt’s properties； meanwhile， the accuracy of evaluating asphalt aging behavior with the above indicators were also analyzed by analyzing the differences between the above indicators and conventional performance indicators in evaluating the asphalt aging properties. On this basis， the nonlinear prediction model for asphalt aging was established based on the above indicators， and the aging rate values of asphalt were obtained to analyze the degradation laws of asphalt properties under natural aging conditions， and ultimately a reasonable preventive maintenance time was proposed. Results showed that， the maximum creep deformation， loading deformation rate coefficient and rebound deformation rate coefficient can effectively characterize the decay process of asphalt aging properties under natural aging conditions. Meanwhile， the nonlinear prediction model for asphalt aging established based on the above indicators had strong applicability， which can achieve bidirectional calculation for asphalt performance and aging time. Furthermore， as the aging time prolonged， the aging rate values corresponding to the maximum creep deformation， loading deformation rate coefficient and rebound deformation rate coefficient all decayed in the form of a cubic function model； and the turning points of aging rate were 6.67 a， 5.56 a， and 6.67 a， respectively. Therefore， considering the safety of asphalt mixture performance， it was suggested that 5.56 a as the threshold for intervention in preventive maintenance measures to restore asphalt performance.

In order to solve the earthquake caused the foundation saturated sand and silt liquefaction， resulting in building settlement， tilt and even damage， this paper focuses on the severe liquefaction of sandy foundation soil in the construction project in Jiang dong New District， Haikou City， Hainan Province. The technique of equal energy and equal deformation compaction piles is used to densify the soil around the piles for secondary consolidation. The liquefaction index （I_{\mathrm{L}\mathrm{E}}） is calculated by conducting standard penetration tests to measure the difference before and after reinforcement and carrying out single pile vertical static compression tests to determine the bearing capacity. The field tests show that the overall foundation soil within the treatment depth has improved from severe liquefaction to slight liquefaction approaching non-liquefaction. The fitted equation for the average difference of standard penetration tests with depth is Y=0.307 857X+10.361 7， with an R² value of 0.847 92 and a Pearson coefficient of 0.931 09. The average number of hammer blows per layer increases by 12-15 within the range of -5 m to -13 m， indicating uniform reinforcement in each soil layer. The designed characteristic value of bearing capacity for single piles is 2 250 kN after reinforcement. The above research findings can serve as a reference for similar engineering projects.