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Acoustic Emission Fatigue Life Prediction in Bridge Steel

Acoustic Emission Fatigue Life Prediction in Bridge Steel Eric v. K. Hill, Andrej Korcak, Jamil Suleman and Fady F. Barsoum. OBJECTIVES Monitor acoustic emission (AE) activity during fatigue testing of notched tensile and I-beam specimens

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Acoustic Emission Fatigue Life Prediction in Bridge Steel

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  1. Acoustic Emission Fatigue Life Prediction in Bridge Steel Eric v. K. Hill, Andrej Korcak, Jamil Suleman and Fady F. Barsoum • OBJECTIVES • Monitor acoustic emission (AE) activity during fatigue testing of notched tensile and I-beam specimens • Use Kohonen self-organizing map (SOM) neural network to classify AE data from plastic deformation (ahead of crack tip), plane strain and plane stress fracture in notched specimens • Employ back propagation neural network (BPNN) to predict fatigue lives from first (0-25%) and third (50-75%) quarter AE amplitude histogram data • Goal: Predict fatigue or cyclic life to failure with worst case error within ±15% I-35W Mississippi River Bridge in Minneapolis Collapsed due to Fatigue of Gussett Plate • APPROACH/TECHNICAL CHALLENGES • Monitor AE from cyclic fatigue crack growth in ten 12x1x0.25 inch tensile specimens and ten S4x7.7 I-beams, all made from notched A572-G50 steel • Train BPNN on 0-25% and 50-75% AE data for 7 tensile specimens and test (predict fatigue life) on remaining 3; train on 6 I-beams and test on 4 ACCOMPLISHMENTS/RESULTS • SOM successfully classified AE fatigue cracking data into plastic deformation, plane strain and plane stress fracture mechanisms • BPNN worst case errors for 0-25% and 50-75% AE data, respectively: tensile specimens, -19.4% and 11.8%;I-beams, -12.4% and 4.5% Cause of Failure: Fatigue of Undersized Gussett Plate

  2. Fatigue Testing of Notched A572-G50 Bridge Steel Tensile Specimens in MTS Machine Fatigue Specimen with AE Transducers Plane Stress vs. Plane Strain Fracture Failure Mechanism Occurrences as a Function of Time Pocket AE Analyzer AE Waveform Parameters SOM Failure Mechanism Classifier

  3. I-Beam Fatigue Testing Using MTS Hydraulic Actuator • 10 Transversely loaded I-Beams (S4x7.7) • 3 Point bending • 1.0 Hz, 0-3,800 lbf @ center of 112 inch span • A572-G50 bridge steel • 0.10 inch deep V-notch on bottom flange V-Notch on Bottom Flange along with Extensometer and AE Transducer Fatigue Crack Failure through Bottom Flange then through Web Section I-Beam in 3-Point Bending with MTS Hydraulic Actuator Applying 1 Hz Cyclic Loading (0-3,800 lbf)

  4. Back Propagation Neural Network (BPNN) 50-75% • NeuralWorks Professional/II Plus software • 71 input neurons for AE amplitude histogram data and 1 for actual fatigue life • Variable number of hidden layer processing elements or neurons • Output neuron for predicted fatigue life or cycles to failure • 10 notched I-beams used for high cycle fatigue (HCF ≥ 10,000 cycles) life prediction • 0-25% and 50-75% AE amplitude histograms used as BPNN input • Train on AE data from 6 beams; test (predict fatigue life) on AE data from remaining 4 beams 0-25% 50-75% Data Worst Case Error

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