• Structural Engineering and Mechanics
• /
• v.42 no.2
• /
• pp.153-174
• /
• 2012

Micromechanical facture models can be used to predict ductile fracture in steel structures. In order to calibrate the parameters in the micromechanical models for the largely used Q345 steel in China, uniaxial tensile tests, smooth notched tensile tests, cyclic notched bar tests, scanning electron microscope tests and finite element analyses were conducted in this paper. The test specimens were made from base metal, deposit metal and heat affected zone of Q345 steel to investigate crack initiation in welded steel connections. The calibrated parameters for the three different locations of Q345 steel were compared with that of the other seven varieties of structural steels. It indicates that the toughness index parameters in the stress modified critical strain (SMCS) model and the void growth model (VGM) are connected with ductility of the material but have no correlation with the yield strength, ultimate strength or the ratio of ultimate strength to yield strength. While the damage degraded parameters in the degraded significant plastic strain (DSPS) model and the cyclic void growth model (CVGM) and the characteristic length parameter are irrelevant with any properties of the material. The results of this paper can be applied to predict ductile fracture in welded steel connections.

• International Journal of Advanced Culture Technology
• /
• v.3 no.1
• /
• pp.161-168
• /
• 2015

This article aims to apply a friction stir spot welding for producing a lap joint of AA1100 aluminum alloy and SGACD zinc coated steel. The experiment was designed by MINITAB and then investigated the relation among the friction spot joint parameters. The experimental results are as follows. The friction spot joining could successively produce the lap joint of AA1100 aluminum alloy and SGACD zinc coated steel. Interaction between the rotate speed, the hold time and the tool insert speed affected to vary the tensile shear strength of the lap joint. The prediction of the optimized welding parameters that indicated the tensile shear strength of 1966 N was the rotated speed of 4000 rpm, the pin hold time of 6 sec, the pin insert rate of 6 mm/min with the S/N ratio of 66.56 that was higher than that of the total mean S/N ratio. The practical experiment of the predicted welding parameters indicated the tensile shear strength of 2165 N and had the S/N ratio of 66.70 that was higher than the predicted tensile shear strength.

• Computers and Concrete
• /
• v.22 no.2
• /
• pp.197-207
• /
• 2018

In the present study, group method of data handling networks (GMDH) are adopted and evaluated for shear strength prediction of both FRP-reinforced concrete members with and without stirrups. Input parameters considered for the GMDH are altogether 12 influential geometrical and mechanical parameters. Two available and very recently collected comprehensive datasets containing 112 and 175 data samples are used to develop new models for two cases with and without shear reinforcement, respectively. The proposed GMDH models are compared with several codes of practice. An artificial neural network (ANN) model and an ANFIS based model are also developed using the same databases to further assessment of GMDH. The accuracy of the developed models is evaluated by statistical error parameters. The results show that the GMDH outperforms other models and successfully can be used as a practical and effective tool for shear strength prediction of members without stirrups ($R^2=0.94$) and with stirrups ($R^2=0.95$). Furthermore, the relative importance and influence of input parameters in the prediction of shear capacity of reinforced concrete members are evaluated through parametric and sensitivity analyses.

• Geomechanics and Engineering
• /
• v.36 no.6
• /
• pp.631-640
• /
• 2024

In the traditional slope stability analysis, ignoring the spatial variability of slope soil will lead to inaccurate analysis. In this paper, the K-L series expansion method is adopted to simulate random field of soil strength parameters. Based on Random Limit Equilibrium Method (RLEM), the influence of variation coefficient and fluctuation range on reliability of soil slope supported by micro-pile is investigated. The results show that the fluctuation ranges and the variation coefficients significantly influence the failure probability of soil slope supported by micro-pile. With the increase of fluctuation range of soil strength parameters, the mean safety factor of the slope increases slightly. The failure probability of the soil slope increases with the increase of fluctuation range when the mean safety factor of the slope is greater than 1. The failure probability of the slope increases by nearly 8.5% when the fluctuation range is increased from δ_v=2 m to δ_v =8 m. With the increase of the variation coefficient of soil strength parameters, the mean safety factor of the slope decreases slightly, and the probability of failure of soil slope increases accordingly. The failure probability of the slope increases by nearly 31% when the variation coefficient increases from COV_c=0.2, COV_φ=0.05 to COV_c=0.5, COV_φ=0.2.

• Computers and Concrete
• /
• v.11 no.2
• /
• pp.105-121
• /
• 2013

This paper investigates the parameters that control the design of Fiber Reinforced Polymer (FRP) reinforced concrete flexural members proportioned following the ACI 440.1R-06. It investigates the critical parameters that control the flexural design, such as the deflection limits, crack limits, flexural capacity, concrete compressive strength, beam span and cross section, and bar diameter, at various Mean-Ambient Temperatures (MAT). The results of this research suggest that the deflection and cracking requirements are the two most controlling limits for FRP reinforced concrete flexural members.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.10 no.1
• /
• pp.82-90
• /
• 2007

In order to analyse the strength problems for an unsaturated soil, it is required to examine closely the characteristics of the parameters of shear strength which was changed with the metric suction and void ratio. To this ends, a triaxial compression test was conducted on the three samples-granular soil, cohesive soil and silty soil. The specimen was made by pressing the static pressure on the mold filled soil and was controled the void ratio with the different compaction ratio. And the test was performed by using the modified apparatus of the triaxial compression tester. The range of matric suction was 0-90 kPa.The measured results for the deviator stress and parameters of shear strength were analysed with the void ratio and the compaction ratio, and they were examined closely the characteristics of the strength for an unsaturated soil.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.5 no.4
• /
• pp.48-69
• /
• 1997

The use of advanced composite materials in many engineering structures has steadily increased during the last decade. Advanced composite materials allow the design engineer to tailor the directional stiffness and the strength of materials as required for the structures. Design variables to the design engineer include multiple material systems. ply orientation, ply thickness, stacking sequence and boundary conditions, in addition to overall structural design parameters. Since the vibration and impact strength of composite cylindrical shell is an important consideration for composite structures design, the reliable prediction method and design methodology should be required. In this study, the optimum design of composite cylindrical shell for maximum natural frequency, buckling strength and impact strength are developed by analytic and numerical method. The effect of parameters such as the various composite material orthotropic properties (CFRP, GFRP, KFRP, Al-CFRP hybrid), the stacking sequences, the shell thickness, and the boundary conditions on structural characteristics are studied extensively.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2005.05a
• /
• pp.79-82
• /
• 2005

This study reports on the structural characteristics of slab-column connections using an ultra-high-strength-fiber-reinforced concrete from new and retrospective data. The parameters investigated were the ' puddling ' of ultra-high-strength-fiber-reinforced concrete and the use of high-strength concrete in the slab. The effects of these parameters on the punching shear capacity, negative moment cracking, and stiffness of the two-way slab specimens are investigated. Furthermore, the ACI Code (2002), the CSA Standard (1994), the BS Standard (1985) and the CEB-FIP Code (1990) predictions are compared to the experimental results obtained from some slab-column connections tested in this experiment and those tested by other investigators. The beneficial effects of the ultra-high-strength-fiber-reinforced concrete puddling and of the use of high-strength concrete are demonstrated. It is also concluded that the punching shear strength of slab-column connections is a function of the flexural reinforcement ratio.

• Computers and Concrete
• /
• v.22 no.2
• /
• pp.249-259
• /
• 2018

This paper presents Artificial Neural Network (ANN) models for evaluating bond strength of deformed, plain and cold formed bars in low strength concrete. The ANN models were implemented using the experimental database developed by conducting experiments in three different universities on total of 138 pullout and 108 splitting specimens under monotonic loading. The key parameters examined in the experiments are low strength concrete, bar development length, concrete cover, rebar type (deformed, cold-formed, plain) and diameter. These deficient parameters are typically found in non-engineered reinforced concrete structures of developing countries. To develop ANN bond model for each bar type, four inputs (the low strength concrete, development length, concrete cover and bar diameter) are used for training the neurons in the network. Multi-Layer-Perceptron was trained according to a back-propagation algorithm. The ANN bond model for deformed bar consists of a single hidden layer and the 9 neurons. For Tor bar and plain bars the ANN models consist of 5 and 6 neurons and a single hidden layer, respectively. The developed ANN models are capable of predicting bond strength for both pull and splitting bond failure modes. The developed ANN models have higher coefficient of determination in training, validation and testing with good prediction and generalization capacity. The comparison of experimental bond strength values with the outcomes of ANN models showed good agreement. Moreover, the ANN model predictions by varying different parameters are also presented for all bar types.

• Geomechanics and Engineering
• /
• v.20 no.3
• /
• pp.191-205
• /
• 2020

Soil shear strength parameters play a remarkable role in designing geotechnical structures such as retaining wall and dam. This study puts an effort to propose two accurate and practical predictive models of soil shear strength parameters via hybrid artificial neural network (ANN)-based models namely genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. To reach the aim of this study, a series of consolidated undrained Triaxial tests were conducted to survey inherent strength increase due to addition of polypropylene fibers to sandy soil. Fiber material with different lengths and percentages were considered to be mixed with sandy soil to evaluate cohesion (as one of shear strength parameter) values. The obtained results from laboratory tests showed that fiber percentage, fiber length, deviator stress and pore water pressure have a significant impact on cohesion values and due to that, these parameters were selected as model inputs. Many GA-ANN and PSO-ANN models were constructed based on the most effective parameters of these models. Based on the simulation results and the computed indices' values, it is observed that the developed GA-ANN model with training and testing coefficient of determination values of 0.957 and 0.950, respectively, performs better than the proposed PSO-ANN model giving coefficient of determination values of 0.938 and 0.943 for training and testing sets, respectively. Therefore, GA-ANN can provide a new applicable model to effectively predict cohesion of fiber-reinforced sandy soil.