Owing to the corrosion of the steel tubes and the creep of the core concrete, the long-term deformation of concrete-filled steel tubular (CFST) columns accelerates over time, which further affects both the static and dynamic performance of the specimens In this study, a total of 18 stub CFST columns and 2 reference plain concrete columns were tested under sustained load and accelerated corrosion caused by spraying. Experimentally, the corrosion of the steel tubes exacerbated CFST creep. Subsequently, interesting observations were made through finite element (FE) simulations and parametric analysis. Simulations results showed that under the same volume loss rate, local surface corrosion significantly affected CFST creep. The final creep coefficient of CFST was proportional to the volume loss rate, concrete elastic modulus, sustained load ratio and inversely proportional to the loading age. To prevent the creep failure of CFST compression columns, it is recommended to limit the volume loss rate to 16.90 % and the sustained load ratio to 0.45. A empirical equation for the final creep coefficient in the long-term performance design of CFST columns is proposed. Therefore, it is essential to consider the coupling effect of corrosion and creep.

Recycled aggregate concrete (RAC) offers a sustainable solution for construction waste utilization; however, the long-term deflection behavior of reinforced RAC (RRAC) beams under combined chloride attack, wetting-drying cycles, and sustained loading remains unclear, and current design codes lack reliable prediction methods. This paper reports selected findings from a comprehensive experimental investigation into the long-term deflection response of 24 RRAC beam specimens subjected to varying recycled coarse aggregate (RCA) replacement ratios (50% and 100%), sustained mechanical load levels (0.3, 0.5, and 0.7), and chloride-induced corrosion rates. Corrosion was accelerated through cyclic wetting-drying exposure in a saline environment maintained at a constant elevated temperature of , while sustained loads were maintained throughout the 360-day test period. A simplified method for predicting the long-term deflection of RRAC beams is developed based on a time-dependent constitutive model following the Chinese code JTG 3362-2018. The proposed method accounts for ambient temperature and humidity, chloride-induced corrosion, cracking, tension stiffening, nonlinear creep, and shrinkage, and is validated against experimental data. The results indicate that an increase in any of the three factors—RCA replacement ratio, load level, or wetting–drying cycle duration—significantly accelerates both corrosion development and deflection growth. Under identical conditions, beams with 100% RCA replacement exhibit larger deflections than those with 50% RCA, and this discrepancy widens over time. Validation yields a mean measured-to-predicted deflection ratio of 0.87 (COV = 11.63%) at a load level of 0.3, and approximately 1.00 (COV < 4.50%) for load levels exceeding 0.3. Shrinkage contributes about 13% to the total deflection. The proposed method is simple, reliable, and applicable to structural design and service-life assessment.

Abstract Harsh environmental conditions (de-icing salts, CO 2 emissions, freeze–thaw, etc.) in northern China threaten the durability of reinforced concrete (RC) highway bridges, with carbonation and chloride ingress acting synergistically. This paper presents an integrated COMSOL framework that couples nonlinear PDEs for heat, moisture, chloride, and CO 2 transport, where carbonation’s effect on chloride diffusion is quantified by the reduction in bound chlorides. The framework is validated against eight-year field data from an in-service prestressed concrete box-girder bridge, showing good agreement (max error ~ 16%). Latin Hypercube Sampling is used for probabilistic reliability-based durability life prediction. The critical vulnerable zone is located at the web–bottom slab intersection of the mid-span section, where the chloride concentration and carbonation depth are at least 1.5 times the corresponding values at the center of the bottom slab. Including the synergistic effect reduces the predicted service life to 81.1 years, compared to carbonation‑ignored scenarios. This framework provides a robust tool for assessing durability under realistic multi‑field coupling.

Concrete-filled steel tube (CFST) columns exhibit complex interactions between the steel tube and concrete during the loading process, making the accurate prediction of their axial compressive capacity particularly challenging. To improve the accuracy and applicability of predicting the axial compressive strength for circular CFST columns, this study proposes a novel interpretable optimized ensemble learning model. Initially, a comprehensive database is constructed using experimental test data of CFST columns, including feature parameters such as geometric parameters and material properties, followed by Pearson correlation analysis to quantify the relationships among the feature variables. Subsequently, an optimized ensemble learning model integrating random forest (RF) and Transformer architectures is developed for predicting the axial compressive strength of circular CFST columns, in which the Tianji's horse racing optimization algorithm is employed to automatically tune the model hyperparameters. Finally, the SHAP method is utilized to offer interpretations for both global and local perspectives of the proposed model. Various evaluation indicators are employed to compare the predictive performance of the proposed ensemble learning model with that of empirical methods and other machine learning models. The results demonstrate that the proposed ensemble learning model outperforms both empirical formulas and competing machine learning models. In addition, the findings reveal that ensemble strategies do not necessarily guarantee better performance than individual models, and model performance is highly sensitive to hyperparameter configurations. Notably, the column diameter, steel tube thickness, and concrete compressive strength exert significant impacts on the axial compressive strength of circular CFST columns.

There are many factors that affect the shear capacity of FRP (fiber-reinforced polymer)-strengthened reinforced concrete (RC) beams, and traditional capacity models based on empirical or semi-empirical formulas often suffer from insufficient accuracy. To enhance the predictive accuracy and generalization ability of the shear capacity of FRP-strengthened RC beams, this study proposes an interpretable machine learning model based on the Jaya-CNN-LSTM model. A comprehensive database consisting of 315 test data on shear capacity of FRP-strengthened RC beams, encompassing various FRP reinforcement modes, has been established. Key feature parameters for predicting the shear capacity of FRP-strengthened RC beams are selected through Pearson correlation coefficient analysis. Based on the Jaya algorithm, the hyperparameters of the ensemble CNN-LSTM prediction model are adaptively optimized. A comparative analysis is conducted between the proposed method, other machine learning models, and existing empirical formulas to evaluate the proposed model’s efficacy. The results demonstrate that the proposed model outperforms other machine learning models and empirical formulas in terms of prediction accuracy and stability. Furthermore, the machine learning-based predictions align more closely with experimental values than those derived from empirical formulas. Additionally, the SHAP method is utilized to quantify the critical parameters’ impact on predicting the shear capacity of FRP-strengthened RC beams. The results reveal that there is an explicit mapping relationship between key features such as shear-span ratio, concrete strength, and yield strength of stirrups and the shear capacity of FRP-strengthened RC beams, providing technical support for practical applications.

The fatigue of heavy-haul railway bridges is considered a key concern due to high stress levels and cyclic loading. The evaluation of fatigue reliability is required to include factor correlations. A major challenge is presented by the construction of the cumulative distribution function (CDF) and the description of correlations between random variables. In this study, the copula function is used to analyze the fatigue failure probability of the Shuohuang heavy-haul railway bridge. A C-vine copula (CVC)-based joint probability density function (JPDF) is derived with eight correlated parameters. To enhance efficiency in small failure probability calculations, the subset simulation and most probable point (MPP) Monte Carlo importance sampling are introduced based on the Rosenblatt transform and C-vine model. Comparisons with traditional Monte Carlo methods confirm that high accuracy and efficiency are achieved. The results show that when parameter correlations are ignored, failure probability is underestimated, increasing safety risks in bridge assessments.

The coarse aggregate formed by crushing waste ceramics is used to produce recycled ceramic coarse aggregate concrete (RCCAC), which is then poured into steel tubes, resulting in a recycled ceramic aggregate concrete-filled steel tube (RCCACFST). The restraining effect of the steel tube can compensate for deficiencies in both the material properties and the interface performance. Concrete creep is a well-known factor that affects the mechanical properties of CFST structures. Therefore, this paper presents a creep test, scanning electron microscopy (SEM) and calculation analysis for RCCACFST, and some valuable data and innovative results were obtained. Firstly, the creep tests of six square RCCACFST short columns were carried out under different stress levels and recycled ceramic coarse aggregate (RCCA) replacement rate. The test results demonstrated that the incorporation of RCCA reduced the creep coefficient of the composite columns by 7.4 %-18.8 %. Nonlinear creep development was evident when the stress level in the core concrete exceeded 0.49. Furthermore, SEM analysis showed that the constraint performance of the RCCA on the cement matrix was weaker than that of the natural aggregate. The experimental data were then compared with predictions made using the age-adjusted effective modulus method (AAEM). The ACI209-W and Ruiz-ACI209-W models were recommended for predicting the long-term deformation of RCCACFST under low and high loading levels, respectively. Finally, the influence of 5 key parameters on RCCACFST creep was studied. The results can be used as reference for the full-life cycle design of RCCACFST.

Chloride ingress in reinforced concrete (RC) structures deteriorates the structural service performance, seriously threating to the structural safety. In real-world, uncertainty and stochasticity are inevitably involved in testing and modeling of the diffusion profile of chloride ingress concrete (CIC). In this paper, a hierarchical Bayesian estimation framework is developed to predict the chloride concentration profile in concrete. Firstly, a CIC stochastic model based on the Fick’s second law of diffusion is constructed to characterize chloride concentration profile. Error terms are included in the stochastic model to capture measurement errors associated with inspections. Secondly, by an instruction to a universal parameter vector and an equivalence of lower order moments, a hierarchical Bayesian estimation method based on trans-distributional reversible jump Markov chain Monte Carlo algorithm (RJ-MCMC) is developed to select a single best distribution-form of the stochastic model parameters from among their multiple distribution-forms or structures. Thirdly, given that distribution-form was specified, the hierarchical Bayesian estimation method and Hybrid Markov chain Monte Carlo (H-MCMC) algorithm are incorporated to update the parameters in the CIC model based on measurement data from inspections. An example involving an in-service RC bridge was employed to validate the developed CIC model and demonstrate the proposed Bayesian estimation framework for the analysis of chloride concentration profile. Results of the analysis indicate both the uncertainty and stochasticity in the parameters of the CIC model as well as the uncertainty in distribution-form must be accounted for in the prediction of chloride concentration profile. The proposed framework will facilitate better decision-making for maintenance and repair activities.

In recent years, there have been several instances of building collapse incidents resulting from shear failure. The complicated stress state and extremely nonlinear behavior seen in RC structures under shear loading are responsible for this phenomenon. Scholars and engineers worldwide have yet to propose a suitable model to address the issue correctly. Most computational approaches currently in use rely on semi-empirical and semi-theoretical formulations. The primary modes of shear transmission in cracked reinforced concrete include shear stress inside the uncracked concrete region, aggregate interlock, dowel action, stirrup action, and tensile residual stress. The shear force calculation is contingent upon the choice of the shear transfer model. This study comprehensively overviews the current shear calculation techniques and their respective applications. The pros and cons of these techniques were thoroughly examined. The present study investigated the suitability of these shear calculation formulas for RC structures subjected to intricate natural conditions, mechanical stress, and high-strength and high-performance concrete structures.

[Objective] Due to the influence of geological conditions, tunnel burial depth, tunnel diameter, and properties of the overlying soil layer, the Peck formula exhibits significant deviations when applied to the shield tunneling construction in water-bearing sandy soil layers in Nanchang area. Therefore, a modification of the formula is necessary. [Method] Based on the field-measured data from a shield tunneling construction in a interval on Nanchang Metro Line 3, along with other measured data, the tunnel burial depth, tunnel radius, and internal friction angle of the overlying soil 3 primary factors that affect land subsidence are comprehensively considered. A calculation formula for land subsidence trough width applicable to Nanchang Metro shield tunneling in water-bearing sandy soils is derived. The range of formation loss rate is determined through inverse calculation using Peck formula. A correction coefficient for maximum subsidence is introduced to perform linear regression modification of Peck formula. [Result & Conclusion] The land subsidence curves predicted by the modified Peck formula closely match the measured subsidence curves, indicating that the formula can effectively predict land subsidence caused by metro shield tunneling construction.

Corrosion leads to the performance degradation of metal and its composite structures, such as steel-concrete structures. The manual detection method is susceptible to subjective judgment, and the associated costs are substantial. In order to assess the corrosion evolution during routine maintenance, it is necessary to identify the corrosion zone. In this paper, we propose a framework for identification of a real-world local corrosion zone under HAU-Net, aiming to address the practical challenges associated with accurately measuring the shape and size of such corrosion zones in actual applications. Firstly, to improve the model's attention to the local corrosion zone, a new segmentation model called HAU-Net Model is developed by adding a self-designed hybrid attention convolution module (HACM) to the U-Net network. Secondly, the image of a real-world local corrosion zone is fused with the LiDAR data of the corrosion zone by using the registration method, obtaining a new image of 1mm pixel scale. Thirdly, the HAU-Net Model is used to test the new image to obtain a binary image. Finally, the shape identification and size calculation of the real-world local corrosion zone are performed based on the binary image. The experimental results show that, the proposed HAU-Net model can fully make use of the channel context information and spatial context information, and obtain a better segmentation performance on the corrosion dataset constructed from available corrosion images; it outperforms the original U-Net model in terms of the accuracy, precision, and MIoU metrics, which is improved by 2.3%, 4.06%, and 3.95%, respectively. Meanwhile, the errors between the predictions and actual measurements of the real-world local corrosion zone in the test are less than 5%, demonstrating the applicability and efficacy of the proposed method.

Accurate time-dependent reliability assessment is crucial for the safety evaluation and service life prediction of existing bridge structures. This study proposes a time-dependent failure probability model incorporating auto-correlated stochastic processes using a multivariate integral framework based on Gaussian copula functions. The model considers the joint effects of non-stationary resistance degradation, vehicle load effects, and the time intervals of load occurrences, all characterized by stochastic processes with inherent correlations. Specifically, the resistance degradation is modeled using a Gamma process, vehicle load occurrences follow a Poisson process, load effect magnitudes are described by a Type I extreme value distribution, and load occurrence intervals are represented by an exponential distribution with a first-order autoregressive structure. A case study on an existing reinforced concrete bridge is conducted to validate the proposed model. The bridge's service life is estimated by comparing the time-dependent failure probability with a target failure threshold. Results reveal that neglecting correlations among stochastic processes leads to underestimated structural reliability, and the underestimation becomes more significant with stronger correlations. The findings highlight the necessity of incorporating stochastic dependencies in time-dependent reliability analysis for more accurate and realistic evaluation of bridge performance over time.

In this paper, 17 types of circular, hollow steel tube columns were designed for the axial compression test. A defect was corroded with an acid rain spray method. The effects of the geometric spatial location of local corrosion zones, three-dimensional size, shape, and number of local corrosion zones on the axial compression load-bearing capacity of the circular hollow steel columns were investigated. Through model verification and parameter analysis in the finite element software ABAQUS, a finite element model of 136 local corrosion, hollow steel tube columns under axial compression was established. In conjunction with experimental and numerical analysis, the primary factor influencing the load-bearing capacity of the steel tube columns was the decrease in effective cross-sectional zones at the corroded zones. Single or multiple local corrosion zones of the same size distributed along the length of the column can reduce the load-bearing capacity of steel tube columns. However, the number, location, and distribution of corrosion zones with the same size have similar degrees of influence on the load-bearing capacity of the steel tube column, with no significant differences. In the case of the same corrosion ratio eta, the load-bearing capacity of steel tube column exhibits a linear relationship with the increase in both the radial corrosion thickness and the circumferential corrosion width within the locally corroded zone. The axial corrosion length in the corroded region has little effect on the load-bearing capacity of the steel tube columns. Ranking the effect of corrosion parameters on the axial compression bearing capacity under the same corrosion ratio eta, the largest one is the radial corrosion thickness; the next are the circumferential corrosion width and the axial corrosion length. A practical formula was developed to calculate the load-bearing capacity of locally corroded steel tube columns, using the rate of section loss in the corroded region as the dependent variable. The formula accurately calculates the axial compressive load-bearing capacity of locally corroded steel tube columns and provides valuable reference for evaluating and maintaining steel tube structures.

[Objective] During the process of EPB SC (earth pressure balanced shield construction), inevitable soil disturbances occur, causing ground deformation and subsequently impacting the surrounding built environment negatively. Therefore, conducting research on the ground deformation caused by EPB SC holds significant importance. [Method] By integrating various relevant literatures, the research is centered around the connections between each stage of the construction. The working mechanism of EPC SC is clarified, the causes of ground deformation are studied from three aspects: tunnel geometry factors, soil geological conditions, and shield tunneling parameters. The prediction methods for ground deformation caused by EPC SC are discussed, the applicability and pros and cons of each method are analyzed. Based on the causes of deformation and deformation prediction, control measures for ground deformation caused by EPB SC are listed. The shortcomings in the research of EPB SC are pointed out, and the future development direction of EPB SC is discussed. [Result & Conclusion] The causes of ground deformation induced by EPB SC are tunnel geometry factors, soil geological conditions, and shield tunneling parameters. The prediction methods for ground deformation caused by EPC SC include empirical formula method, theoretical analysis method, numerical simulation method of finite elements and finite difference, model test method, and artificial intelligence method. Based on the monitoring of ground deformation, continuously adjusting the shield parameters during construction is an effective method to control ground deformation by EPB shield.

This study aims to examine the effects of local corrosion on the axial compression performance of concrete-filled steel tubular (CFST) members. Nineteen CFST short columns with local corrosion were designed and fabricated to undergo axial compression mechanical property tests, with the radial corrosion depth of the local corrosion area as the key test parameter. The failure mechanism and mechanical property change laws of CFST axial compression short columns with circumferential full corrosion at the ends and middle were studied. Combined with finite element modeling, the influence laws of the three-dimensional geometrical characteristics of the local corrosion zone, i.e., the axial length, the annular width and the radial depth, on the structural bearing performance were thoroughly explored and discussed. The results revealed that the main reason for the reduction in load-carrying capacity of circular CFST axial columns due to local corrosion is attributed to the reduction of the effective cross-sectional area of the steel tube in the corrosion area. When local corrosion occurs at different axial positions, the variation range of the bearing capacity of CFST columns is within 10%. Regarding the impact of the three dimensions of local corrosion on the axial load-carrying capacity of CFST, the radial corrosion depth was identified as the most influential factor, followed by the annular corrosion width, and finally by the axial corrosion length. When the axial corrosion length exceeds 20% of the specimen length, its further influence on the load-carrying capacity is considered limited. Finally, a practical calculation formula for the bearing capacity of locally corroded CFST columns is proposed. The predicted results of this formula fit well with the test results and can quickly estimate the remaining bearing capacity of the structure by measuring the geometric parameters of the local corrosion area, providing a reference for the assessment and maintenance of CFST structures.

In this paper, a total of six different ribbed and two nonribbed L-shaped concrete-filled steel tubular (CFST) short columns are designed; finite element modeling and calculations are carried out and verified with measured results. The optimal ribbing method among the ribbing methods applied is proposed. The mechanisms of the longitudinal stress of the concrete, longitudinal stress of the outer steel tube, and lateral compressive stress of the concrete for the L-shaped CFST short column without ribs and with the optimal ribbing method are compared and analyzed. The results show that the optimal ribbing method involves setting half-width longitudinal stiffening ribs in the middle of the two wide sides of the L-shaped steel tube. The stiffening ribs can delay the occurrence of local buckling failure of the steel tube, thus strengthening the restraining effect of the steel tube on the concrete and improving the uniformity of the longitudinal stress of the core concrete. In addition, based on an analysis of a large number of parameters, four methods for calculating the bearing capacity of nonribbed L-shaped CFST short columns are compared. A design formula for the bearing capacity of ribbed L-shaped CFST columns is proposed by considering the improvement in the concrete performance. The calculation results agree well with the finite element and experimental results, and the error is <5%.

For low-carbon sustainability, recycled rubber particles (RPs) and recycled aggregate (RA) could be used to make rubber-modified recycled aggregate concrete (RRAC). The characteristics (compressive strength and peak strain) of RRAC with various amounts of RA and RPs after heating at various temperatures were studied in this work. The results show that high temperatures significantly decreased the uniaxial compressive strength (UCS), whereas the addition of RA (e.g., 50%) and RPs (e.g., 5%) can mitigate the negative effect caused by high temperatures. The peak strain can also be improved by increasing the replacement ratios of RA and RP. Support vector regression (SVR) models were trained using a total of 120 groups of UCS and peak strain experimental datasets, and an SVR-based multi-objective optimization model was proposed. The excellent correlation coefficients (0.9772 for UCS and 0.9412 for peak strain) found to illustrate the remarkable accuracy of the SVR models. The Pareto fronts of a tri-objective mixture optimization design (UCS, strain, and cost) were successfully generated as the decision reference at varying temperature conditions. A sensitivity analysis was performed to rank the importance of the input variables where temperature was found as the most important one. In addition, the replacement ratio of RA is more important compared with that of the RP for both the UCS and strain datasets. Among the mechanical properties of concrete, compressive strength and peak strain are two key properties. This study provides guidance for the study of RRAC constitutive models under high temperatures.

In order to explore the rail surface analytical representation model of deformation accumulation and stiffness mutation of key components in ballastless track-bridge system induced by earthworks deformation of high-speed railway, according to the structural characteristics of double block, unit slab and longitudinally connected slab ballastless track on high-speed railway bridge, the analytical representation model of rail surface deformation and earthworks deformation of three ballastless track structures considering the stiffness mutation of key components and the influence of the participation of limit components was derived, respectively, based on Castigliano’s second theorem and linear superposition principle.Furthermore, the test, simulation and measured results verified the representation model, and the influences of bridge deformation, stiffness mutation of key components and participation of limit components on the interlayer deformation transfer and connection contact of ballastless track-bridge system were analyzed based on the model.The results show that the results from the proposed model are in good agreement with the test results, simulation results and measured data of rail inspection vehicle, verifying the correctness of the proposed model.Three kinds of ballastless track have ‘following’ deformation with the bridge under their self-weights, the unit slab has strong adaptability to bridge deformation, and the longitudinally connected slab has strong ability to resist bridge deformation, but it is easy to produce a large range of slab bottom detachment, which brings safety hazards to operational safety of train.Under the pier settlement, the stiffness mutation of key components only affects the rail surface deformation in the mutation area, the rail surface deformation of the longitudinally connected slab shall be in the decreasing sequence caused by the breaking of fastener, by the unit slab, and by the double block, the rail surface deformation of the longitudinally connected slab is gentler than that of the unit slab in the ‘subsidence’ area caused by mortar debonding, and the ‘subsidence’ amplitude of the unit slab is less than that of the longitudinally connected slab caused by mortar deattachment.The participations of lateral block and shear reinforcement have little contribution to the rail surface deformation, while the participation of shear tooth groove has great influence on the rail surface deformation.

Ultra high performance concrete（UHPC） is faced with the problems of high cost and high self-shrinkage of cement matrix due to its extremely low water-binder ratio and high cement content in its wide application. One of the effective solutions is to replace part of cement with industrial by-products or wastes. As the waste ceramic tile has become a large amount of industrial waste, the application of ceramic tile powder in UHPC can effectively solve the problems of high consumption of cement and accumulation of waste ceramic tile. Therefore, ceramic tile powder was used to replace 10wt%, 15wt%, 20wt% and 25wt% by mass of cement to prepare a new type of green lowcarbon UHPC. The effect law of ceramic tile powder on the compressive strength of UHPC was studied, and the modified Andreasen accumulation model, XRD analyses, TG/DTG, SEM observation were used to investigate the modification mechanisms, and the environmental footprint and the cost of ceramic tile powder on UHPC were also analyzed. The results show that the effect of the addition of ceramic tile powder on the compressive strength of UHPC is within ±10% at all age. Interestingly, ceramic tile powder has a significant influence on the development of compressive strength at 7-28 days and 28-60 days, and the increase rates of compressive strength of UHPC with25wt% ceramic tile powder can reach 104.6% and 51.8%, respectively. This is mainly because that the addition of ceramic tile powder improves the packing compactness of UHPC, produces secondary hydration reaction and calcium silicate hydrate gel with low calcium-silicon ratio, improves the hydration degree of cement, and reduces the width of interface transition zone. According to environmental impact and cost calculation, ceramic tile powder can effectively reduce energy consumption, CO2 emission and cost of UHPC.

基于钢筋混凝土异形柱的设计思路,提出了一种组合式十字形钢管混凝土柱.利用方钢管和槽钢焊接形成有多腔室的钢管混凝土柱.以试件长度和截面形式为主要参数,共设计制作5个试件.通过对其进行轴心受压试验研究,考察了试件的破坏形态和荷载-应变关系曲线,并分析了各参数对此新型截面形式钢管混凝土柱轴压承载力和延性的影响.组合式十字形钢管混凝土试件最终破坏形态为沿试件长度方向分布多个较小鼓曲.试验结果表明,钢管对混凝土约束良好,该组合形式有效提高了十字形异形柱的承载力并改善了其延性;相同截面形式的组合式十字形钢管混凝土极限承载力随着柱高的增加,略有下降;相同柱高、不同截面形式的钢管混凝土柱,随着柱肢长度的增加,其极限承载力显著增加;在现行规范的基础上,提出了组合式十字形钢管混凝土柱轴压承载力计算公式,根据该公式计算所得理论值与试验值吻合较好.