Steel Bridge Service Life Prediction Using Bootstrap Method

Steel bridges play an important role in the transportation system of many countries. To ensure that bridges are structurally sound, engineers monitor their performance, which is known as structural health monitoring. Historical evidence indicates that bridge damage is pervasive and that the service life of bridges is decreasing. To manage safety and costs, engineers must be able to accurately predict the service life of bridges. A statistical method to predict service life for steel bridges is presented, which can assist bridge engineers, bridge owners, and state officials in the objective assessment of deteriorated bridges for retrofit or replacement. Timely repair and retrofit of bridges increase their safety levels and decrease costs. A nonparametric statistical approach based on the bootstrap method for stress analysis for fatigue life prediction of steel bridge components is proposed. The bootstrap provides a simple approach for the reproduction of the extremely complex probability distribution of measured strain data. It is completely automated numerical method which requires no theoretical calculations and it is not based on the asymptotic results. The service life index is introduced which quantifies the fatigue life of steel bridges under daily traffic loads. A regression model is developed for the prediction of remaining service life of steel bridges using a service life function. The predicted remaining service life derived from the function can contribute to effective management of steel bridges.

[1]  Bradley Efron,et al.  Censored Data and the Bootstrap , 1981 .

[2]  Moe M. S. Cheung,et al.  Service Life Prediction of RC Bridge Structures Exposed to Chloride Environments , 2009 .

[3]  David Gilmore,et al.  Implementation of Field Strain Measurements for Fatigue Lifetime Evaluation , 2002 .

[4]  Davor Grandić,et al.  Evaluation of Torsional Stiffness in Beam and Slab Bridge Decks Based on Load Testing , 2015 .

[5]  Brian Brenner,et al.  Statistical Bridge Signatures , 2014 .

[6]  Z. Y. Wang,et al.  Finite element based fatigue assessment of corrugated steel web beams in highway bridges , 2015 .

[7]  Byung Hwan Oh,et al.  Realistic Assessment for Safety and Service Life of Reinforced Concrete Decks in Girder Bridges , 2007 .

[8]  Filippo Domma,et al.  A new class of distribution functions for lifetime data , 2014, Reliab. Eng. Syst. Saf..

[9]  E. W. C. Wilkins,et al.  Cumulative damage in fatigue , 1956 .

[10]  Brian Brenner,et al.  Instrumentation, Nondestructive Testing, and Finite-Element Model Updating for Bridge Evaluation Using Strain Measurements , 2012 .

[11]  Yi-Qing Ni,et al.  Monitoring-Based Fatigue Reliability Assessment of Steel Bridges: Analytical Model and Application , 2010 .

[12]  J. Stedinger.  Frequency analysis of extreme events , 1993 .

[13]  Félix Belzunce,et al.  On sufficient conditions for mean residual life and related orders , 2013, Comput. Stat. Data Anal..

[14]  Raif Sakin,et al.  Statistical analysis of bending fatigue life data using Weibull distribution in glass-fiber reinforced polyester composites , 2008 .

[15]  F. C. Hadipriono,et al.  ANALYSIS OF RECENT BRIDGE FAILURES IN THE UNITED STATES , 2003 .

[16]  David Hinkley,et al.  Bootstrap Methods: Another Look at the Jackknife , 2008 .

[17]  Brandon Chavel,et al.  Framework for Improving Resilience of Bridge Design , 2011 .

[18]  Dan M. Frangopol,et al.  Bridge fatigue reliability assessment using probability density functions of equivalent stress range based on field monitoring data , 2010 .

[19]  Dennis R. Mertz,et al.  STEEL BRIDGE MEMBERS UNDER VARIABLE AMPLITUDE LONG LIFE FATIGUE LOADING , 1983 .

[20]  Jan Ming Ko,et al.  Fatigue analysis and life prediction of bridges with structural health monitoring data — Part II: application , 2001 .

[21]  Darrell F. Socie,et al.  Simple rainflow counting algorithms , 1982 .

[22]  Amir Tarighat,et al.  Model Based Damage Detection of Concrete Bridge Deck Using Adaptive Neuro-Fuzzy Inference System , 2013 .

[23]  Maxence Bigerelle,et al.  Application of Lambda Distributions and Bootstrap analysis to the prediction of fatigue lifetime and confidence intervals , 2006 .

[24]  Dan M. Frangopol,et al.  Fatigue Assessment and Service Life Prediction of Existing Steel Bridges by Integrating SHM into a Probabilistic Bilinear S-N Approach , 2013 .

[25]  Chun Sheng Wang,et al.  Fatigue Reliability Updating Evaluation of Existing Steel Bridges , 2012 .

[26]  Sreenivas Alampalli,et al.  Estimating Fatigue Life of Bridge Components Using Measured Strains , 2006 .

[27]  Maxence Bigerelle,et al.  Bootstrap analysis of FCGR, application to the Paris relationship and to lifetime prediction , 1999 .

[28]  Yi-Qing Ni,et al.  Statistical analysis of stress spectra for fatigue life assessment of steel bridges with structural health monitoring data , 2012 .

[29]  Y. Edward Zhou,et al.  Assessment of Bridge Remaining Fatigue Life through Field Strain Measurement , 2005 .

[30]  Abdollah Hosseini,et al.  Life-cycle prediction of steel bridges using reliability-based fatigue deterioration profile: Case study of neka bridge , 2013 .