Prediction Of Bearing Capacity Research Articles

Development of Z number-based fuzzy inference system to predict bearing capacity of circular foundations

Precise bearing capacity prediction of circular foundations is essential in civil engineering design and construction. The bearing capacity is affected by factors such as depth, density of soil, internal angle of friction, cohesion of soil, and foundation radius. In this paper, an innovative perspective on a fuzzy inference system (FIS) was proposed to predict bearing capacity. The uncertainty of fuzzy rules is eliminated by using Z-number theory. The effective parameters, i.e., depth, density of soil, internal angle of friction, cohesion of soil, and foundation radius were considered as inputs to the proposed model. To compare regression and FIS model with Z-based FIS, statistical indices such as the coefficient of determination (R2), root mean square error (RMSE), and variance account for (VAF) were employed. For training and testing Z-FIS, the R2 was (0.977 and 0.971), the RMSE was (1.645 and 1.745), and the VAF was (98.549% and 98.138), whereas for the FIS method, the values were (0.912 and 0.904), (5.962 and 6.76), and (90.12% and 88.49%). It should be mentioned that Z theory decreased the computational time by 89.28% (174.04 s to 18.65 s). The comparison of the statistical indicators of the presented models revealed the superiority of the Z-FIS model over the FIS. Notably, sensitivity analysis revealed that the most effective parameters on bearing capacity are internal angle of friction, depth, and soil density.

Experimental study on the punching failure model of soft red rock subgrade by BPT-BVM methods and it’s assessment of the load bearing capacity

The recommended bearing capacity of medium weathering mudstone foundation is less than the capacity of the rock structure to withstand loads in Southwest China. A comprehensive failure characterization of medium weathering mudstone in Chengdu has been performed including bearing plate test (BPT), binocular vision measurement (BVM) test, uniaxial compressive strength test, trial trench test of shallow rock surface and 3D imaging in this paper. Failure behavior of rock has been modeled with 3D imaging algorithm that utilizes Zhang’s calibration method in BVM system combination with trial trench test of shallow rock surface. The bearing capacity of medium weathering mudstone foundation were extracted from uniaxial experiments and BPT-BVM test by fitting relevant material properties to the data. The results revealed that: Bearing capacity of medium weathering mudstone of layered isotropic in Chengdu is undervalued. Specifically, the characteristic load carrying value is in the range 1500–2500 kP, that is 50% higher than in the local standard system. Failure process is different from Hoek–Brown Failure Criterion, presenting a wave peak transfer phenomenon of the increment displacement into the distance. Thus, it can be reduced to that of punching failures for thin bedded structures of Moudstone foundations. Compressive strength of soft rock proves to be main factor limiting the bearing capacity, a clear correlation between the uniaxial compressive strength reduction coefficient and the bearing capacity has been used to establish, leading to the proposal of a load bearing capacity prediction model.

Research on the anti-collapse performance of steel frame structures with infilled walls in the form of reduced beam sections connections

Steel has more deformation capacity and resistance to compression buckling than concrete materials, which providing steel frames a distinct edge in preventing collapse. This study examines the anti-collapse performance and mechanism of steel frame structures with infilled walls in the form of reduced beam section connections. It also examines the effects of wall thickness, frame aspect ratio, and connection control size on performance and addresses the applicability of current code formulas in the early deformation of the structure. The study discovered that steel frame structures with infilled walls in the form of reduced beam section connections can, during the failure stage, enhance the infilled wall's contribution to the collapse bearing capacity, thereby stimulating the structure's potential performance, in contrast to those with infilled walls in the form of non-reduced beam section connections. Due to the supporting effect of the double strut that forms inside the infilled wall during the failure stage, steel frame structures with infilled walls that have non-reduced beam section connections have higher deformation capacity than corresponding pure frames; however, this effect also decreases the deformation capacity of the structure with reduced beam section. Furthermore, the infilled walls cause the structure to collapse in a same way across a range of aspect ratios, yet their contribution to the structural bearing capacity differs across these same aspect ratios. It is noteworthy that altering the local connection control size can greatly enhance the structure anti-collapse performance, even in unfavorable aspect ratio scenarios. The study also discovered that the bearing capacity of the structure studied in this paper was overestimated by the FEMA356-2000 code and the MSJC code, which were used to predict the initial bearing capacity of infilled frames. The former's overestimation reaches a maximum of 39 %, while the latter's prediction is only accurate within a limited range of aspect ratios. Furthermore, the formula by Qian and Li, which has a maximum error of 9 % in bearing capacity prediction within the standard range of aspect ratios, is better appropriate for the failure mode of the structure described in this study.

Bearing capacity of shallow foundations: a focus on the depth factors in combination with the respective N-factors

The ongoing refinement of bearing capacity equations remains pivotal in soil mechanics and foundation engineering, reflecting its critical role in ensuring design efficacy and construction safety. This study conducts a thorough evaluation of classical bearing capacity methods—Terzaghi, Meyerhof, Vesic, and Hansen—and methods included in various design standards, such as EN1997:2004, prEN1997:2023, GEO, AASHTO, FHWA, and API. It explores the performance and applicability of these approaches, identifying areas for potential improvement. In response to identified challenges, the paper proposes the integration of a unified depth factor. This new factor is designed to be applicable across all N-terms, providing a more versatile and accurate tool for bearing capacity predictions. Unlike the original depth factors unique to each method, which may not fully address complex soil and footing conditions, the unified depth factor is developed to enhance prediction accuracy for a wide range of conditions, including both flexible and rigid footings under varying flow rules (ψ = 0 and ψ = φ). This depth factor corrects for modeling errors, emphasizing the importance of pairing the correct set of N-factors with their corresponding depth factor. By offering a singular depth factor that aligns with the outcomes of finite element analysis, this paper not only simplifies the computational process but also enhances the accuracy of bearing capacity predictions across a diverse range of soil conditions and footing types. The comparative analysis, based on finite element analysis, validates the proposed method’s effectiveness, showcasing its potential to significantly refine foundation design practices by comparing it with both traditional and newly developed depth factors.

Research on shear characteristics of pile–soil interface under cyclic loading

Offshore wind turbine constructions are developing rapidly in China. Generally, large-diameter rigid steel piles are used in offshore wind turbine foundations. However, existing design methods for pile foundations with bearing capacity are often based on small-diameter flexible piles and are inapplicable to real situations. Simultaneously, thick layers of clay cover the fields. Therefore, the shear characteristics of pile–clay interface require examination to optimize design methods. A large-scale interface shear test model corresponding to laboratory tests is established using PFC software, and the micro-parameter values are validated. The simulation results well follow the laboratory test results, and the model accurately reflects pile–clay interactions during shearing. Based on the simulation model, the shear characteristics of the steel–clay interface under different normal loads, cyclic load frequencies, and cyclic load amplitudes are analyzed. With an increase in normal load, the shear strength of the steel-clay interface increases gradually, whereas it slightly decreases with an increase in the cyclic load frequency and amplitude; particularly, the effect of amplitude is significant. The mechanical response of clay at a mesoscopic scale is analyzed. Results show that the greater the normal load, the greater the horizontal displacement of particles. However, the influence of cyclic load frequency and amplitude is the opposite: normal load does not change the contact force distribution of soil particles, and clay volume change is determined by the normal load. Thus, this study provides support for bearing capacity prediction of offshore wind farm pile foundations.

Study on design optimization of GFRP tubular column composite structure based on machine learning method.

Circular reinforced concrete wound glass fiber reinforced polymer (GFRP) columns and reinforced concrete filled GFRP columns are extensively utilized in civil engineering practice. Various factors influence the performance of these two types of GFRP columns, thereby impacting the whole project. Therefore, it is highly significant to establish the prediction models for ultimate displacement and ultimate bearing capacity to optimize the design of the two types of GFRP columns. In this study, based on the experiments conducted under different conditions on the two kinds of GFRP columns, automatic machine learning along with four other commonly used machine learning methods were employed for modeling to analyze how the column parameters (cross section shape, concrete strength, height of GFRP column, wound GFRP wall thickness, inner diameter of wound GFRP column) affect their performance. The differences in performance among these five machine learning methods were analyzed after modeling. Subsequently, we obtained the variation patterns in ultimate displacement and ultimate bearing capacity of the columns influenced by each parameter by testing the data using the optimal model. Based on these findings, the optimal design schemes for the two types of GFRP columns are proposed. The contribution of this paper is three-fold. First, AutoML sheds light on the automatic prediction of ultimate displacement and ultimate bearing capacity of GFRP column. Second, in this paper, two optimal design schemes of GFRP columns are proposed. Third, for AEC industrial practitioners, the whole process is automatic, accurate and less reliant on data expertise and the optimization design scheme proposed in the article is relatively scientific.

Predicting the ultimate bearing capacity of spudcan in stiff-over-soft clay based on a failure model varying with soil parameter

A jack-up platform is prone to a ‘punch-through’ accident when working on the stiff-over-soft clay foundation. To assess the risk of punch-through, it is necessary to plot the bearing capacity-penetration curve for evaluating the penetration depth of the pile leg or to calculate the ultimate bearing capacity of the spudcan and compare it with the preload. However, accurate prediction of spudcan bearing capacity is not easy. In this paper, the failure mode about the stiff-over-soft clay under different geological conditions when punch-through occurs is discussed using coupled Eulerian lagrangian (CEL) method, varying the strength ratio between soft and stiff soil layers, the thickness of stiff soil relative to the spudcan diameter, and the non-homogeneity factor of soil strength. Based on the soil failure model with varying soil parameters, a new calculation method for ultimate bearing capacity of spudcan is proposed and then verified against existing centrifuge test results. The results show that the shape of soil shear plane in stiff clay is significantly affected by the strength ratio of soil layer and the non-homogeneity factor of soil strength. And the presented new method whose prediction errors compared with centrifuge test results are less than 5%, has better calculation accuracy than the ISO (International Organization for Standardization) method, which is valuable in guiding the calculation of the ultimate bearing capacity of the spudcan and ensuring the safe operation of the jack-up drilling units on stiff-over-soft clay.

Appraisal of numerous machine learning techniques for the prediction of bearing capacity of strip footings subjected to inclined loading

Appraisal of numerous machine learning techniques for the prediction of bearing capacity of strip footings subjected to inclined loading

Optimizing pile bearing capacity prediction: Insights from dynamic testing and smart algorithms in geotechnical engineering

In contemporary mining and geotechnical projects, various approaches are employed to predict the bearing capacity of piles (Qu). However, accurately modeling pile behavior using numerical, experimental, analytical, and regression methods proves challenging and, at times, infeasible due to the intricate nature of geotechnical materials, uncertainties, and the interaction between soil and piles. Consequently, formulations are generally presented, incorporating assumptions and simplifications that deviate from the actual complexity of the problem. Moreover, despite the high accuracy of pile loading tests as a reliable method in various design stages, their high costs and time requirements deter designers from conducting field tests. To address these challenges, this study performed 50 dynamic tests (HSDT) on precast concrete piles in Indonesia, Pekanbaru, to generate the necessary datasets. To mitigate experimental costs, two optimization algorithms fruit fly optimization (FFO) and invasive weed optimization (IWO) were employed. These models incorporated pile set (S), drop height (H), hammer weight (W), cross-sectional area (A), and length (L) as input parameters. Finally, the models' accuracy was evaluated using squared correlation coefficient (R2), variance accounted for (VAF), mean square error (MSE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results revealed that the FFO algorithm achieved an accuracy range of 0.971–0.978. Similarly, the IWO algorithm exhibited an accuracy range of 0.984–0.988. Additionally, sensitivity analysis indicated that, among the input parameters, W had the most significant impact on the Qu in both algorithms.

Ultimate bearing capacity of geosynthetic reinforced soil abutment centrifuge model tests

Five centrifuge models of geosynthetic reinforced soil (GRS) abutments with segmental block facing were loaded in the acceleration field under plane strain condition. Influencing factors including reinforcement tensile strength, setback and bearing area width were considered and analysed. Results show that the abutment with stronger reinforcement remained stable under the maximum loading capacity. However, abutments with weaker reinforcement showed excessive vertical strain, local deformation or even collapse. The ultimate bearing capacity increased with the lengthening of setback but decreased due to a larger bearing area. The rupture of reinforcements was observed and considered as the cause of the failures. Compared with the measured ultimate bearing capacity, the values calculated by the semi-empirical formula of design guidelines were significantly conservative. The failure surface of failed abutment developed from the rear edge of the bearing area to the middle height of the abutment at an angle of nearly 45° + φ/2. The setback and the bearing area width affected the form and position of the failure surface. The difference between the potential failure surface predicted by available methods and the measured failure surfaces has been discussed, and suggestions for the design and ultimate bearing capacity prediction of GRS abutments with segmental block facing are provided.

Experimental and numerical research on the uniaxial behavior of the stiffened circular concrete-filled steel tube stub columns

In large-span bridges or high-rise buildings, CFST columns with large width-to-thickness ratio are often adopted, which require proper stiffening measures to improve the mechanical behaviors. This paper presents a comparative study of circular CFST stub columns with different stiffening methods, including longitudinal stiffeners (cross-shaped, T-shaped and ordinary type), additional reinforcement and headed studs. Axial compression tests have been conducted to investigate the effects of different stiffening methods on the failure mode, ultimate strength and ductility of the circular CFST stub columns. Finite element models are established to simulate the columns’ behaviors and conduct parametric studies. Based on test and simulation results, an axial bearing capacity prediction model for circular CFST stub columns stiffened by longitudinal stiffeners is proposed. It is demonstrated that the failure modes of the steel tube and core concrete are changed by the longitudinal stiffeners. The longitudinal steel stiffeners as well as reinforcing cage can effectively improve the axial bearing capacity and ductility of the columns. The latter is more efficient in promoting the bearing capacity while the former is more efficient in improving the ductility and residual strength. A combination of the additional stirrups and the ordinary stiffeners will be a more economical choice. In addition, the proposed formula gives better prediction accuracy compared to the equations in various design codes.

Experimental, finite element and analytical characterization of hysteretic response of ductile connections with nailed angle brackets for mass timber structures

Engineered wood products along with advanced processing and fabrication are pushing the limits of modern wood construction. Innovative concepts and applications facilitate the development of a new generation of structural systems. However, capacities of such systems are often governed by capabilities of the connections. This paper presents the experimental results with numerical and analytical models of angle brackets for prediction of load bearing capacity, stiffness, and ductility. Three types of metal brackets in beam-column connections have been investigated and their performance has been studied in various loading arrangements. Detailed finite-element models of each connection have been developed to gain insights into their behavior. An analytical approach is also adopted to represent the connections. Comparison with test data suggests that the models can reproduce results with good accuracy. The findings confirm feasibility of implementing ductile connections in practical mass timber structures.

Shear bearing capacity prediction of STRC shear walls using data‐augmented fusion model

AbstractMachine learning (ML) accurately predicts the shear bearing capacity of steel tube‐reinforced concrete (STRC) shear walls, aiding optimization design. However, creating a database for STRC shear walls experimentally is time‐consuming and costly. The aim of this study is to propose a method for high‐accuracy prediction of STRC shear wall shear bearing capacity using a small‐sample dataset. This study employs generative adversarial network (GAN) data augmentation techniques to address the issues of insufficient ML model training and low prediction accuracy in small‐sample databases. Based on the stacking framework, a fusion model (Stacking‐XRL) combining extreme gradient boosting (XGBoost), random forest (RF), and least absolute shrinkage and selection operator (LASSO) is established to predict the shear bearing capacity of STRC shear walls. Results show that after augmenting the training set with GAN, the prediction performance of K‐nearest neighbors (KNN), backpropagation neural network (BPNN), RF, light gradient boosting machine (LightGBM), XGBoost, and stacking‐XRL models significantly improve, with average increases of 10% in R2 and average decreases of 30% and 25% in MAE and RMSE, respectively. The proposed stacking‐XRL fusion model outperforms tested models, existing formulas, and Abaqus numerical simulations for the shear bearing capacity of STRC shear walls. Model interpretation reveals that the shear span ratios as the most important factors in predicting shear bearing capacity, followed by axial force ratio and whole section configuration steel tubular index.

Prediction of ultimate bearing capacity of rock-socketed piles based on GWO-SVR algorithm

The application of rock-socketed pile in engineering is becoming ever more common. Finding the ultimate bearing capacity of rock-socketed piles with a high-performance ratio and precision is an important area of research. The rock-socketed piles underwent a field load test, and ABAQUS software was utilized to create a model of the interaction between the pile and the soil. For the geometric and physical parameters of the pile, soil, and bedrock, a total of eighteen pile-soil parameters were chosen. Reasonably constructed orthogonal and control variable tests are utilized, and a sensitivity analysis of the relevant factors is performed. Ten elements that significantly affect the ultimate bearing capacity of rock-socketed piles (with a probability less than 0.05) are selected as the input for the bearing capacity prediction of rock-socketed piles based on the test's consistent findings. The ultimate bearing capacity of rock-socketed piles is predicted using the support vector regression (SVR) model, and the absolute coefficient, mean absolute error (MAE), mean square error (MSE) and other indicators are chosen as the evaluation indices of the model prediction effect. The SVR model predicts an absolute coefficient of 0.7, which is an unsatisfactory prediction outcome. Consequently, the model is optimized via the nested grey wolf (GWO) algorithm. With a value of 0.995, the GWO-SVR model is more akin to 1 than the SVR model. There are 0.14 and 0.034 for the MAE and MSE, respectively. The models of (Artificial bee colony) ABC-SVR, (Cuckoo search) CS-SVR, (Sparrow search algorithm)SSA-SVR, and (Particle swarm optimization) PSO-SVR are built. When comparing different assessment indicators, the GWO-SVR model is smaller than the MAE and MSE. When the prediction model is used on a real project, the engineering requirements can be satisfied by the prediction effect.

Bearing capacity prediction of the concrete pile using tunned ANFIS system

The design process for pile foundations necessitates meticulous deliberation of the calculation pertaining to the bearing capacity of the piles. The primary objective of this work was to investigate the potential use of Coot bird optimization (CBO\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{CBO}}$$\\end{document}) techniques in predicting the load-bearing capacity of concrete-driven piles. Despite the availability of several suggested models, the investigation of Coot bird optimization (CBO\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{CBO}}$$\\end{document}) for estimating the pile-carrying capacity has been somewhat neglected in this research. This work presents and validates a unique approach that combines the Coot bird optimization (CBO\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{CBO}}$$\\end{document}) model with the Multi-layered perceptron (MLP\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{MLP}}$$\\end{document}) neural network and adaptive neuro-fuzzy inference system (ANFIS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{ANFIS}}$$\\end{document}). The findings of 472 different driven pile static load tests were put in a database. The proposed framework's building, validation, and testing stages were each accomplished utilizing the training set (70%), validation set (15%), and testing set (15%) of the dataset, respectively. According to the findings, MLPCBO\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ ext{MLP}}}_{{\ ext{CBO}}}$$\\end{document} and ANFISCBO\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ ext{ANFIS}}}_{{\ ext{CBO}}}$$\\end{document} both offer remarkable possibilities for accurately predicting the pile-bearing capacity of a given structure. The R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}^{2}$$\\end{document} values for ANFISCBO\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ ext{ANFIS}}}_{{\ ext{CBO}}}$$\\end{document} during the training stage were 0.9874, while during the validating stage, they were 0.9785, and during the testing stage they were 0.987. After considering various kinds of performance studies and contrasting them with existing literature, it has been concluded that the ANFISCBO\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ ext{ANFIS}}}_{{\ ext{CBO}}}$$\\end{document} model provides a more appropriate calculation of the bearing capacity of concrete-driven piles.

Application of Regression Techniques for Bearing Capacity Prediction in Aizawl

Application of Regression Techniques for Bearing Capacity Prediction in Aizawl

Shear Bearing Capacity Prediction of Steel-Fiber-Reinforced High-Strength Concrete Corbels on Modified Compression Field Theory

In order to analyze the shear mechanism of the steel-fiber high-strength concrete corbels, a calculation model for the shear bearing capacity of steel-fiber-reinforced high-strength concrete corbels was proposed based on the modified compression field theory. Considering the existence of residual tensile stress in steel-fiber-reinforced concrete at crack locations, the cracked steel-fiber-reinforced concrete was treated as a continuous material. The constitutive relation of cracked steel-fiber-reinforced concrete and the local stress equilibrium equation were modified. It was compared with the results of 34 steel-fiber high-strength concrete corbels, including those in this paper. The predicted results were compared with the experimental values and the predictions of the Fattuhi model, Campione model, and Russo model to validate the rationality of the proposed model. The results revealed that the mean value between the experimental values and the predicted results of the proposed model is 1.104, with a variance of 0.003, showing good agreement. The proposed model can accurately predict the shear bearing capacity of steel-fiber-reinforced high-strength concrete corbels.

Tooth surface analysis method based on thermo-mechanical coupling and its application in prediction of gear dry-running bearing capacity

In gear transmission, the continuous meshing of the teeth will lead to an increase in their temperature, which will have a certain impact on the working performance of the tooth surface. In order to explore the failure-bearing capacity of gears under dry-running conditions, temperature effects were considered in existing methods for determining tooth surface failure, making it more suitable for high-temperature working conditions caused by a lack of lubrication. The failure situation of gears within 35 min after losing lubrication was analyzed. The results indicate that when the gear loses lubrication, the tooth surface temperature of the gear pair has exceeded the material melting point after working at an input power of 2000 kW for 12 min. But when the power drops to 1000 kW, the instantaneous temperatures of the driving and driven gear tooth surfaces at the end of 35 min reach 1090 and 995°C, respectively, which are lower than the scuffing temperature of the tooth surface. The cumulative wear depths of the driving and driven gear tooth surfaces at the end of 35 min were 239 and 86.6 um, respectively, which were lower than the failure wear depth of the tooth surface; The allowable contact stress of the gear pair within 35 min is greater than its contact stress, meeting the judgment condition of no pitting failure. This indicates that reducing power can appropriately extend the failure time of gear teeth, thereby improving the ultimate working capacity of gears under dry-running conditions. In addition, a gear dry-running experiment was carried out. By comparing the theoretical calculation results with the measured gear failure degree, it shows that the improved method for determining gear surface failure is effective.

Using shear modulus to predict the bearing capacity of strip foundations on sand

This paper reports results of a study to predict load-settlement behaviour of strip foundations on a dry uncemented sand using only soil stiffness data, under both working loads and extreme loads. This involved predicting the strength of a foundation system without using any strength parameters. A non-linear stepwise load-settlement prediction algorithm was utilised, which used the small-strain shear modulus of layers below the footing, a suitable stiffness modulus reduction curve and elasticity theory. The algorithm was adapted to predict the ultimate bearing capacity, as well as to construct astress-settlement curve and determine the bearing pressure experienced beneath a foundation at any particular settlement. The method was evaluated using two different sets of modulus reduction curves, for small-scale model foundations on loose, medium dense and dense sand. Model tests were conducted at 1g. Particle Image Velocimetry (PIV) was used to observe strains within the soil during loading. These strains were used to calculate the shear modulus of discrete layers throughout loading and back-estimate the layer small-strain shear moduli. Additionally, the mode of shear failure for each test at ultimate bearing capacity was observed using PIV. Modulus reduction curves that initially degrade shear modulus rapidly beyond the elastic threshold strain (0.001%) gave superior predictions under the given conditions. Using such curves, ultimate bearing capacity predictions produced errors of 0.4% to 10.8%. Using the same input parameters, stresssettlement predictions were of satisfactory accuracy over the entire settlement range.

ИССЛЕДОВАНИЕ НЕСУЩЕЙ СПОСОБНОСТИ СВАЙ НА ОСНОВЕ ЭМПИРИЧЕСКИХ ДАННЫХ, ДИНАМИЧЕСКИХ ИСПЫТАНИЙ И ЧИСЛЕННОГО МОДЕЛИРОВАНИЯ

Axial bearing capacity is an important factor in designing of foundation. It must be able to carry the load of upper structure without any significant deformation due to important role in pile design. A part from empirical and numerical methods, axial pile bearing capacity can be predicted by pile driving analyzer test that use wave equation analysis during the test. Prediction of the axial bearing capacity and its distribution can be achieved by combining wave equation analysis with dynamic pile monitoring. This paper compares the axial capacity of pile obtained from PDA records with predicted axial capacities obtained using empirical, numerical, and interpretation of static load test method. The empirical method used NSPT data from bore testing that conduct in site, finite element method that uses in this study is Plaxis 8.6. Both of them was compared with PDA test result of axial bearing capacity. There is a difference value of the calculation results of axial pile bearing capacity and foundation settlement. Comparing with PDA, from empirical method has smallest deviation for axial bearing capacity which equal to -268.45 kN with percentage -6.05%, smallest settlement equal to 0,0044 m. Plaxis result shows that distinction between Qult result equal to -11.00 kN with percentage -0.24% and settlement of foundation equal to 0,005 m. From interpretation of static loading test method smallest deviation for Qult which equal to -40,00 kN, with percentage -0,90%, and smallest settlement equal to 0,0055 m. So, from all method the smallest deviation for Qult and PDA result is used finite element method. This is due to many data parameters has been inputted and analyzed in the Plaxis program, so the results of calculation analysis are more detailed and closer to the situation in the research site. Differences in axial bearing capacity can be caused by the test was carried out which depends on the accuracy of operator and parameters that used in the calculations.