Please use this identifier to cite or link to this item: https://hdl.handle.net/11681/44483
Title: Probabilistic neural networks that predict compressive strength of high strength concrete in mass placements using thermal history
Authors: Roberson, Madeleine M.
Inman, Kathleen M.
Carey, Ashley Suzanne, 1995-
Howard, Isaac L. (Isaac Lem), 1979-
Shannon, Jameson D.
Keywords: Ultra-high performance concrete (UHPC)
Neural network
Mechanical properties
Bayesian variational inference
Thermal history
Monte Carlo dropout
Publisher: Engineer Research and Development Center (U.S.)
Series/Report no.: Miscellaneous Paper (Engineer Research and Development Center (U.S.)) ; no. ERDC/GSL MP-22-1
Is Version Of: Roberson, Madeleine M., Kathleen M. Inman, Ashley S. Carey, Isaac L. Howard, and Jay Shannon. "Probabilistic neural networks that predict compressive strength of high strength concrete in mass placements using thermal history." Computers & Structures 259 (2022): 106707. https://doi.org/10.1016/j.compstruc.2021.106707
Abstract: This study explored the use of artificial neural networks to predict UHPC compressive strengths given thermal history and key mix components. The model developed herein employs Bayesian variational inference using Monte Carlo dropout to convey prediction uncertainty using 735 datapoints on seven UHPC mixtures collected using a variety of techniques. Datapoints contained a measured compressive strength along with three curing inputs (specimen maturity, maximum temperature experienced during curing, time of maximum temperature) and five mixture inputs to distinguish each UHPC mixture (cement type, silicon dioxide content, mix type, water to cementitious material ratio, and admixture dosage rate). Input analysis concluded that predictions were more sensitive to curing inputs than mixture inputs. On average, 8.2% of experimental results in the final model fell outside of the predicted range with 67.9%of these cases conservatively underpredicting. The results support that this model methodology is able to make sufficient probabilistic predictions within the scope of the provided dataset but is not for extrapolating beyond the training data. In addition, the model was vetted using various datasets obtained from literature to assess its versatility. Overall this model is a promising advancement towards predicting mechanical properties of high strength concrete with known uncertainties.
Description: Miscellaneous Paper
Gov't Doc #: ERDC/GSL MP-22-1
Rights: Approved for Public Release; Distribution is Unlimited
URI: https://hdl.handle.net/11681/44483
http://dx.doi.org/10.21079/11681/44483
Appears in Collections:Miscellaneous Paper

Files in This Item:
File Description SizeFormat 
ERDC-GSL MP-22-1.pdf1.67 MBAdobe PDFThumbnail
View/Open