Resistance factors for steel highway bridges

1984 ◽  
Vol 11 (2) ◽  
pp. 324-334 ◽  
Author(s):  
D. J. Laurie Kennedy ◽  
Karen A. Baker
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Highway Bridges ◽  
Limit States ◽  
Welded Steel ◽  
Mean Values ◽  
Resistance Factors ◽  
Performance Factors ◽  
Simulation Performance ◽  
Girder Bridges

Resistance (performance) factors for bridge members composed of rolled and welded steel sections are developed consistent with the live and dead load factors given in the Ontario Highway Bridge Design Code (OHBDC).Ratios of the load components of seven different spans of plate and box girder bridges are used with the statistical data for loads used in the development of the OHBDC. Monte Carlo simulation techniques are used to compose distribution curves for the various resistance functions from distribution curves describing the appropriate geometric properties, material properties, and test/predicted ratios. Using the 0.001 and 0.05 fractiles of the distribution curves so obtained for the resistances, mean values and coefficients of variation are obtained for equivalent lognormal distribution curves.Resistance factors are then developed for the fully plastic moment resistance, the yield moment, the inelastic buckling moment resistance, the elastic buckling moment resistance, the moment resistance of composite sections, and column resistances for slenderness parameter values of 0.8, 1.0, and 1.2. A general resistance factor of 0.93 is recommended for all the resistances and bridges studied. Resistance factors of 0.95 and 0.98 are considered appropriate for the flexural resistance of bridges composed of welded and rolled sections, respectively. Key words: bridges, limit states, Monte Carlo simulation, performance factors, resistance factors, steel, rolled sections, welded sections.

Safety factors and limit states analysis in geotechnical engineering

1984 ◽  
Vol 21 (1) ◽  
pp. 1-7 ◽  
Author(s):  
G. G. Meyerhof
Keyword(s):  
Geotechnical Engineering ◽  
Stability Conditions ◽  
Safety Factors ◽  
Limit States ◽  
Resistance Factors ◽  
Performance Factors ◽  
Load Factors ◽  
Partial Safety Factors ◽  
And Performance ◽  
Earth Retaining Structures

This paper outlines the ultimate and serviceability limit states in geotechnical engineering analyses. The magnitude of customary total and suggested partial safety factors in earthworks, earth retaining structures, excavations, and foundations is discussed. On the basis of comparisons between these safety factors and using recommended load factors on various types of loading, including water pressures, common resistance factors on cohesion and friction of soils and performance factors can be established together with some additional modification factors for particular stability conditions. The serviceability limit states of foundations and structures are briefly discussed.

scholarly journals Reliability Estimation for Highway Bridges

2020 ◽  
Author(s):  
Nafiseh Kiani
Keyword(s):  
Reliability Index ◽  
Structural Reliability ◽  
Limit State ◽  
Highway Bridges ◽  
Reliability Estimation ◽  
State Function ◽  
Limit States ◽  
Resistance Factors ◽  
Load And Resistance Factors ◽  
Reliability Methods

Structural reliability analysis is necessary to predict the uncertainties which may endanger the safety of structures during their lifetime. Structural uncertainties are associated with design, construction and operation stages. In design of structures, different limit states or failure functions are suggested to be considered by design specifications. Load and resistance factors are two essential parameters which have significant impact on evaluating the uncertainties. These load and resistance factors are commonly determined using structural reliability methods. The purpose of this study is to determine the reliability index for a typical highway bridge by considering the maximum moment generated by vehicle live loads on the bridge as a random variable. The limit state function was formulated and reliability index was determined using the First Order Reliability Methods (FORM) method.

Monte Carlo simulation of the effect of counting losses on measured X-ray intensities

2007 ◽  
Vol 40 (5) ◽  
pp. 964-965
Author(s):  
T. Ida
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Dead Time ◽  
Statistical Properties ◽  
Time Model ◽  
Mean Values ◽  
Counting Loss ◽  
X Ray ◽  
The Mean

The statistical properties of intensities affected by counting loss based on conventional non-extended and extended dead-time models are examined by a Monte Carlo method. It has been confirmed that the variance of the counted pulses for the non-extended dead-time model with the rate of generated pulsesr and the dead-time τ is given by \sigma_{\rm non}^2 = \mu_{\rm non}/(1+r \tau)^2, while that for the extended dead-time model is given by \sigma_{\rm ext}^2 = \mu_{\rm ext} [1 - 2r\tau \exp(-r \tau)], as proposed by Laundy & Collins [(2003).J. Synchrotron Rad.10, 214–218], for the mean values of counted pulses μnonand μext, respectively. Practical formulae to estimate the statistical errors of the corrected intensities are also presented.

scholarly journals Corrigendum to ``Descriptive Measures of Poisson-Lomax Distribution''

2020 ◽  
Vol 43 (2) ◽  
pp. 345-353
Author(s):  
Khushnoor Khan
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probability Distribution ◽  
Programming Language ◽  
Numerical Results ◽  
Due Diligence ◽  
Mean Values ◽  
Lomax Distribution ◽  
Mathematical Properties ◽  
The Mean

This corrigendum focuses on the correction of numerical results derived from Poisson-Lomax Distribution (PLD) originally proposed by Al-Zahrani & Sagor (2014). Though the mathematical properties and derivations by Al-Zahrani & Sagor (2014) were immaculate but during the execution ofthe R codes using Monte Carlo simulation some anomalies occurred in the calculation of the mean values. The same  anomalies are addressed in thepresent corrigendum. The outcome of the corrigendum will provide basic guidelines for the academia and reviewers of various journals to match thenumerical results with the shape of the probability distribution under study. The results will also emphasize the fact that code writing is a cumbersome process and due diligence be exercised in executing the codes using any programming language. Relevant R codes are appended in Appendix 'A'.

Load and Resistance Factor Design of Driven Piles

1996 ◽  
Vol 1546 (1) ◽  
pp. 88-93 ◽  
Author(s):  
George G. Goble
Keyword(s):  
Quality Control ◽  
Highway Bridges ◽  
The United States ◽  
Resistance Factor ◽  
Limit States ◽  
Resistance Factors ◽  
Nominal Strength ◽  
Factor Design ◽  
Load And Resistance Factor ◽  
Driven Pile

A load and resistance factor design (LRFD) bridge specification has been accepted by the AASHTO Bridge Committee. This design approach is now being implemented for highway bridges in the United States, including the design of driven pile foundations. To test the new specification's practicality and usefulness, an example problem has been solved using it. In the example, a pipe pile was designed to be driven into a granular soil to support a bridge column subjected to a factored axial compression load of 10 MN. The nominal strength selected for the pile was 1.58 MN with an estimated length of 25 m. Since the resistance factors are defined by the specified quality control procedures, the number of piles required in the foundation also depends on the quality control. In this example, the number of piles required varied from 15 to 8 with improved quality control, for a savings of almost half of the piles. This example indicated that the new AASHTO LRFD specification for driven pile design can be used effectively to produce a more rationally designed foundation. Some modifications should be made to include additional serviceability limit states, and additional research may indicate that changes should be made in some of the resistance factors.

ANN based investigations of reliabilities of the models for concrete under triaxial compression

2016 ◽  
Vol 33 (7) ◽  
pp. 2019-2044 ◽  
Author(s):  
Ertekin Öztekin
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Simulation Method ◽  
Monte Carlo Simulation Method ◽  
Limit States ◽  
Content Type ◽  
Artificial Neural

Purpose A lot of triaxial compressive models for different concrete types and different concrete strength classes were proposed to be used in structural analyses. The existence of so many models creates conflicts and confusions during the selection of the models. In this study, reliability analyses were carried out to prevent such conflicts and confusions and to determine the most reliable model for normal- and high-strength concrete (NSC and HSC) under combined triaxial compressions. The paper aims to discuss these issues. Design/methodology/approach An analytical model was proposed to estimate the strength of NSC and HSC under different triaxial loadings. After verifying the validity of the model by making comparisons with the models in the literature, reliabilities of all models were investigated. The Monte Carlo simulation method was used in the reliability studies. Artificial experimental data required for the Monte Carlo simulation method were generated by using artificial neural networks. Findings The validity of the proposed model was verified. Reliability indexes of triaxial compressive models were obtained for the limit states, different concrete strengths and different lateral compressions. Finally, the reliability indexes were tabulated to be able to choose the best model for NSC and HSC under different triaxial compressions. Research limitations/implications Concrete compressive strength and lateral compression were taken as variables in the model. Practical implications The reliability indexes were tabulated to be able to choose the best model for NSC and HSC under different triaxial compressions. Originality/value A new analytical model was proposed to estimate the strength of NSC and HSC under different triaxial loadings. Reliability indexes of triaxial compressive models were obtained for the limit states, different concrete strengths and different lateral compressions. Artificial experimental data were obtained by using artificial neural networks. Four different artificial neural networks were developed to generate artificial experimental data. They can also be used in the estimations of the strength of NSC and HSC under different triaxial loadings.

scholarly journals Stochastic Responses of Multi-Degree-of-Freedom Uncertain Hysteretic Systems

2011 ◽  
Vol 18 (1-2) ◽  
pp. 387-396
Author(s):  
Yimin Zhang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Moment Method ◽  
Degree Of Freedom ◽  
Random Response ◽  
Hysteretic Model ◽  
Mean Values ◽  
Hysteretic Systems ◽  
The Mean ◽  
Second Moment Method

On the basis of the Bouc-Wen hysteretic model, the effective numerical method for the response of nonlinear multi-degree-of-freedom (MDOF) stochastic hysteretic systems is presented using second moment method. Using this method, the mean values, variances and covariances are computed. The Monte Carlo simulation is applied to validate the method. The results obtained by the two methods are contrasted, and the solutions of the method in this paper agreed very well with the Monte Carlo simulation. It has solved the random response of nonlinear stochastic vibration systems which is caused by the stochastic hysteretic loop itself.

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