Ultimate Load Prediction in Composite I-Beams Eric v. K. Hill and Edward C. Fatzinger APPROACH/TECHNICAL CHALLENGES Ten fiberglass/epoxy beams cantilever.

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Presentation transcript:

Ultimate Load Prediction in Composite I-Beams Eric v. K. Hill and Edward C. Fatzinger APPROACH/TECHNICAL CHALLENGES Ten fiberglass/epoxy beams cantilever loaded at 333 lb f /min using MTS hydraulic actuator Record AE data during loading to failure from web mounted transducer next to fixed support low proof load (≤25% of average ultimate load)Train BPNN on low proof load (≤25% of average ultimate load) AE amplitude histograms from 6 beams (1, 3, 4, 6, 7 and 8) as inputs; then test (predict) ultimate loads on remaining 4 beams (2, 9, 10 and 11) ACCOMPLISHMENTS/RESULTS Worst case prediction error: 12.9%Worst case prediction error: 12.9% OBJECTIVES Back-propagation neural network (BPNN) prediction of ultimate load in composite I-beams using low proof load acoustic emission (AE) amplitude distribution (histogram) data as input Goal:±15%Goal: Worst case prediction error within ±15% Composite I-Beam under Cantilever Load with AE Transducers Attached Piezoelectric Element Wear Plate Integral Preamplifier BNC Connector Physical Acoustics Corporation R-15I AE (150 kHz Resonant) Transducer with Integral Preamplifier

47” 14” 10” 2” 0.375” Radius 2.5” 1.4” 0.2” Fillet Radius 0.2” Composite I-Beam Construction Exploded View End View Side View

NeuralWorks Professional II/PLUS ® Software Predicted Ultimate Load 570 lb f Worst Case Error: 12.9% Actual Ultimate Load 505 lb f BPNN for Predicting Composite I-Beam Ultimate Load from Low Proof Load AE Amplitude Data Failed Composite I-Beam and AE Analyzer and AE Analyzer BPNN Training and Test Results AE Amplitude Parameter Amplitude Histogram (≤25% Average S u )