Probabilistic neural networks that predict compressive strength of high strength concrete in mass placements using thermal history. (15th January 2022)

• Record Type: Journal Article
• Authors: Roberson, Madeleine M.; Inman, Kathleen M.; Carey, Ashley S.; Howard, Isaac L.; Shannon, Jay
• Is Part Of: Computers & structures. Volume 259(2022)
• Publication Date: 2022-01-15
• Subjects: Ultra-high performance concrete -- Neural network -- Mechanical properties -- Bayesian variational inference -- Thermal history -- Monte Carlo dropout; Structural engineering -- Data processing -- Periodicals; Electronic data processing -- Structures, Theory of -- Periodicals; 624.171
• DOI: 10.1016/j.compstruc.2021.106707
• Languages: English
• ISSNs: 0045-7949

Abstract

Highlights: Thermal inputs had more impact on model predictions than mix components. Monte-Carlo dropout was effective for quantifying uncertainty due to dataset noise. Bayesian inference did not help the model extrapolate beyond the training data. 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.